3.1. Overall structure

The system diagram of overall structure in Fig. 1 is first designed and includes outer and inner loops. The outer loop of the system is to generate the virtual desired trajectory $\boldsymbol{{x}}_{\boldsymbol{{r}}}$ according to the interaction force $\boldsymbol{{f}}$ and impedance model ${\boldsymbol{{Z}}}({\cdot})$ . The inner loop with an ABLF controller is designed to track the virtual desired trajectory precisely. The main purpose of the controller is to make the robot comply with the human’s objective, while guaranteeing safety and tracking accuracy.

Figure 1.

System structure of double-loop control.

In general, the desired impedance model in the Cartesian space is written as

(9) \begin{equation} {\boldsymbol{{f}}}={\boldsymbol{{Z}}}({\boldsymbol{{x}}_{\boldsymbol{{d}}}},{\boldsymbol{{x}}_{\boldsymbol{{r}}}}), \end{equation}

where $\boldsymbol{{x}}_{\boldsymbol{{d}}}$ is the desired trajectory and $\boldsymbol{{x}}_{\boldsymbol{{r}}}$ is the reference trajectory in Cartesian space. Then, the virtual desired reference trajectory $\boldsymbol{{q}}_{\boldsymbol{{r}}}$ can be obtained by using inverse kinematics. The outer loop of the system is to generate the virtual desired trajectory according to the interaction force $\boldsymbol{{f}}$ and the impedance model ${\boldsymbol{{Z}}}({\cdot})$ . The OPFB ADP method is proposed in the outer loop to determine the optimized impedance parameters of the unknown robot–environment dynamics model.

The main purpose for the inner loop is to guarantee the tracking performance of trajectory $\boldsymbol{{q}}_{\boldsymbol{{r}}}$ . For the security of robot arm, ABLF-based controller is designed to constrain the state and speed of robot. Then, the RBFNN is employed to fit the unknown robot dynamics in the constrained controller.

3.2. OPFB ADP method in outer loop

To optimize the robot–environment interaction, an impedance adaptation method considering the environment dynamics is developed. In this paper, the robot and environmental system are taken into consideration to achieve the desired interaction performance by minimizing the following cost function:

(10) \begin{equation} {\boldsymbol{\Gamma }} =\int _{t}^{\infty }{\left [{{{\dot{{\boldsymbol{{x}}}}}}^{T}}{{{\boldsymbol{{Q}}_{\textbf{1}}}}}\dot{{\boldsymbol{{x}}}}+{{({\boldsymbol{{x}}}-{{\boldsymbol{{x}}_{\boldsymbol{{d}}}}})}^{T}}{\boldsymbol{{Q}}_{\textbf{2}}}({\boldsymbol{{x}}}-{{\boldsymbol{{x}}_{\boldsymbol{{d}}}}})+{{{\boldsymbol{{f}}}}^{T}}{\boldsymbol{{Rf}}} \right ]}d{\boldsymbol{\tau }}, \end{equation}

where $\boldsymbol{{Q}}_{\textbf{1}}$ , $\boldsymbol{{Q}}_{\textbf{2}}$ are positive definite, describing the weight of velocity and tracking errors, respectively. $\boldsymbol{{R}}$ is the weight of the interaction force torque. The state variable is defined as

(11) \begin{equation} {\boldsymbol{\xi }} ={{\left [{{{\dot{{\boldsymbol{{x}}}}}}^{T}},{{{\boldsymbol{{x}}}}^{T}},{\boldsymbol{{s}}_{\textbf{1}}}^{T},{\boldsymbol{{s}}_{\textbf{2}}}^{T} \right ]}^{T}}, \end{equation}

where $\boldsymbol{{s}}_{\textbf{1}}$ and $\boldsymbol{{s}}_{\textbf{2}}$ are two states constructed in the model, which can be written as

(12) \begin{equation} \begin{split} \left \{ \begin{array}{lr} \dot{{\boldsymbol{{s}}_1}}={\boldsymbol{{F}}_{\textbf{1}}\boldsymbol{{s}}_{\textbf{1}}}\\ \\[-8pt]{\boldsymbol{{x}}_{\boldsymbol{{d}}}}={\boldsymbol{{G}}_{\textbf{1}}\boldsymbol{{s}}_{\textbf{1}}} \end{array} \right .,\text{ } \left \{ \begin{array}{lr} \dot{{\boldsymbol{{s}}_2}}={\boldsymbol{{F}}_{\textbf{2}}\boldsymbol{{s}}_{\textbf{2}}}\\ \\[-8pt]{\boldsymbol{{x}}_{\boldsymbol{{e}}}}={\boldsymbol{{G}}_{\textbf{2}}\boldsymbol{{s}}_{\textbf{2}}} \end{array} \right. \end{split}, \end{equation}

where $\boldsymbol{{F}}_{\textbf{1}}$ and $\boldsymbol{{G}}_{\textbf{1}}$ are two known matrices, $\boldsymbol{{F}}_{\textbf{2}}$ and $\boldsymbol{{G}}_{\textbf{2}}$ are unknown matrices. Two linear systems in (12) are utilized to determine the desired trajectory $\boldsymbol{{x}}_{\boldsymbol{{d}}}$ and the varying position of the contact trajectory $\boldsymbol{{x}}_{\boldsymbol{{e}}}$ .

According to (12), the cost function (10) can be rewriten as

(13) \begin{equation} \begin{aligned}{\boldsymbol{\Gamma }} &=\int _{t}^{\infty }{\left [{{{\dot{{\boldsymbol{{x}}}}}}^{T}}{\boldsymbol{{Q}}_{\textbf{1}}}\dot{{\boldsymbol{{x}}}}+\left [ \begin{matrix}{{{\boldsymbol{{x}}}}^{T}} &\quad {\boldsymbol{{x}}_{\boldsymbol{{d}}}}^{T} \\ \end{matrix} \right ]\left [ \begin{matrix}{\boldsymbol{{Q}}_{\textbf{2}}} & -{\boldsymbol{{Q}}_{\textbf{2}}} \\ \\[-8pt] -{\boldsymbol{{Q}}_{\textbf{2}}} &{\boldsymbol{{Q}}_{\textbf{2}}} \\ \end{matrix} \right ]\left [ \begin{matrix}{{{\boldsymbol{{x}}}}^{T}} \\ \\[-8pt] {\boldsymbol{{x}}_{\boldsymbol{{d}}}}^{T} \\ \end{matrix} \right ]+{{{\boldsymbol{{f}}}}^{T}}{\boldsymbol{{Rf}}} \right ]}d{\boldsymbol{\tau }} \\ &=\int _{t}^{\infty }{\left [{{{\boldsymbol{{y}}}}^{T}}{{{\boldsymbol{{Qy}}}}}+{{{\boldsymbol{{f}}}}^{T}}{\boldsymbol{{Rf}}} \right ]}d{\boldsymbol{\tau }}, \end{aligned} \end{equation}

where ${{{\boldsymbol{{Q}}}}}=\left [ \begin{array}{c@{\quad}c}{\boldsymbol{{Q}}_{\textbf{1}}} &{\textbf{0}} \\{\textbf{0}} &{{\boldsymbol{{Q}}}_{\boldsymbol{{2}}}^{\prime}} \end{array} \right ]$ with ${{\boldsymbol{{Q}}}_{\boldsymbol{{2}}}^{\prime}}=\left [ \begin{array}{c@{\quad}c}{\boldsymbol{{Q}}_{\textbf{2}}} & -{\boldsymbol{{Q}}_{\textbf{2}}}{{\boldsymbol{{G}}_{\textbf{1}}}} \\ -{\boldsymbol{{G}}_{\textbf{1}}}^{T} &{\boldsymbol{{G}}_{\textbf{1}}}^{T}{\boldsymbol{{Q}}_{\textbf{2}}}{\boldsymbol{{G}}_{\textbf{1}}} \end{array}\right ]$ .

For the convenience of calculation, a discrete output feedback method is introduced to solve adaptive dynamic programing problems. First, the continuous-time state-space function can be written as

(14) \begin{equation} \dot{{\boldsymbol{\xi }}}={{{\boldsymbol{{A}}}}\boldsymbol{\xi }}+{{\boldsymbol{{Bu}}}}, \qquad{{\boldsymbol{{y}}}}={{\boldsymbol{{C}}}}{\boldsymbol{\xi }} \end{equation}

where ${\boldsymbol{\xi }}\in \mathbb{R}^{n\times n}$ is the state, and ${\boldsymbol{{y}}}\in \mathbb{R}^p$ is the measured output, and ${\boldsymbol{{u}}}\in \mathbb{R}^m$ is the control input. In real application, $\boldsymbol{{u}}$ represents the interaction force $\boldsymbol{{f}}$ . Assume that $({{{\boldsymbol{{A}}}}},{{{\boldsymbol{{B}}}}})$ is controllable and $({{{\boldsymbol{{A}}}}},{\boldsymbol{{C}}})$ is observable. The matrices ${{{\boldsymbol{{A}}}}}\in \mathbb{R}^{n\times n}$ and ${{{\boldsymbol{{B}}}}}\in \mathbb{R}^{n\times m}$ include the unknown environment dynamics due to the unmeasurable state $\boldsymbol{{s}}_{\textbf{2}}$ , where

\begin{align*} & {{{\boldsymbol{{A}}}}}=\left [ \begin{array}{c@{\quad}c@{\quad}c@{\quad}c} -{{{{\boldsymbol{{M}}}}}^{-1}}({{\boldsymbol{{C}}_{\boldsymbol{{d}}}}}+{{\boldsymbol{{C}}_{\boldsymbol{{e}}}}}) & -{{{{{\boldsymbol{{M}}}}}}^{-1}}{{\boldsymbol{{G}}_{\boldsymbol{{e}}}}} & 0 & {{{{{\boldsymbol{{M}}}}}}^{-1}}{{\boldsymbol{{G}}_{\boldsymbol{{e}}}}}{{{\boldsymbol{{G}}_{\textbf{2}}}}} \\ \\[-8pt]1 & 0 & 0 & 0 \\ \\[-8pt]0 & 0 & {{\boldsymbol{{F}}_{\textbf{1}}}} & 0 \\ \\[-8pt]0 & 0 & 0 & {{\boldsymbol{{F}}_{\textbf{2}}}} \end{array} \right], \\[3pt] & \boldsymbol{{B}}={{\left [ \begin{array}{c@{\quad}c@{\quad}c@{\quad}c} -{{{{{\boldsymbol{{M}}}}}}^{-1}} & 0 & 0 & 0 \end{array} \right ]}^{T}},\text { }{{{\boldsymbol{{M}}}}}\text {=}{{\boldsymbol{{M}}_{\boldsymbol{{d}}}}}+{{\boldsymbol{{M}}_{\boldsymbol{{e}}}}}. \end{align*}

The output matrix $\boldsymbol{{C}}$ is defined as

\begin{equation*} {{{\boldsymbol{{C}}}}}=\left [ \begin {array}{c@{\quad}c@{\quad}c@{\quad}c} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 0 \\ \end {array} \right ]. \end{equation*}

Then, a discrete model of (14) using the zero-order hold method is obtained by taking periodic sampling

(15) \begin{equation} {\boldsymbol{\xi }}_{k+1}={\boldsymbol{{A}}_{\boldsymbol{{d}}}}{\boldsymbol{\xi }}_k+{\boldsymbol{{B}}_{\boldsymbol{{d}}}}{\boldsymbol{{u}}}_k, \qquad{\boldsymbol{{y}}}_k={\boldsymbol{{C}}}{\boldsymbol{\xi }}_k, \end{equation}

where ${\boldsymbol{{A}}_{\boldsymbol{{d}}}}=e^{\,{{{\boldsymbol{{A}}}}h}}$ , ${\boldsymbol{{B}}_{\boldsymbol{{d}}}}=(\!\int _{0}^{h}e^{\,{{{\boldsymbol{{A}}}}h}}\mathrm{d}{\boldsymbol{\tau }}){{{\boldsymbol{{B}}}}}$ , and $h\gt 0$ is a small sampling period. The stabilizing control policy is given as ${\boldsymbol{{u}}}_k={\boldsymbol{\mu }}({\boldsymbol{\xi }}_k)$ , which can minimize the performance index function:

(16) \begin{equation} {{{\boldsymbol{{V}}}}}^{{\boldsymbol{\mu }}}({\boldsymbol{\xi }}_k)=\sum _{i=k}^{\infty }({\boldsymbol{{y}}}_i^{\mathrm{T}}{\boldsymbol{{Q}}_{\boldsymbol{{d}}}}\boldsymbol{{y}}_i+{\boldsymbol{{u}}}_i^{\mathrm{T}}{\boldsymbol{{R}}_{\boldsymbol{{d}}}}{\boldsymbol{{u}}}_i) \end{equation}

with weighting matrices ${\boldsymbol{{Q}}_{\boldsymbol{{d}}}}\geq 0$ , ${\boldsymbol{{R}}_{\boldsymbol{{d}}}}\gt 0$ , and $({\boldsymbol{{A}}_{\boldsymbol{{d}}}},\sqrt{{\boldsymbol{{Q}}_{\boldsymbol{{d}}}}}{\boldsymbol{{C}}})$ is observable and $({\boldsymbol{{A}}_{\boldsymbol{{d}}}},{\boldsymbol{{B}}_{\boldsymbol{{d}}}})$ is controllable.

Since the solution of (16) is similar to the LQR problem, we can find a positive definite matrix ${\boldsymbol{{P}}_{\boldsymbol{{d}}}}\in \mathbb{R}^{n\times n}$ to rewrite (16) into the following form.

(17) \begin{equation} {{{\boldsymbol{{V}}}}}^{\mu }({\boldsymbol{\xi }}_k)={\boldsymbol{\xi }}_k^{\mathrm{T}}{\boldsymbol{{P}}_{\boldsymbol{{d}}}}{\boldsymbol{\xi }}_k. \end{equation}

Then, the LQR Bellman equation is obtained as follows:

(18) \begin{equation} {\boldsymbol{\xi }}_k^{\mathrm{T}}{\boldsymbol{{P}}_{\boldsymbol{{d}}}}{\boldsymbol{\xi }}_k={\boldsymbol{{y}}}_k^{\mathrm{T}}{\boldsymbol{{Q}}_{\boldsymbol{{d}}}}{\boldsymbol{{y}}}_k+{\boldsymbol{{u}}}_k^{\mathrm{T}}{\boldsymbol{{R}}_{\boldsymbol{{d}}}}{\boldsymbol{{u}}}_k+{\boldsymbol{\xi }}_{k+1}^{\mathrm{T}}{\boldsymbol{{P}}_{\boldsymbol{{d}}}}{\boldsymbol{\xi }}_{k+1}. \end{equation}

To find the optimal control, combining (15) and (18) yields

(19) \begin{equation} {\boldsymbol{\xi }}_k^{\mathrm{T}}{\boldsymbol{{P}}_{\boldsymbol{{d}}}}{\boldsymbol{\xi }}_k={\boldsymbol{{y}}}_k^{\mathrm{T}}{\boldsymbol{{Q}}_{\boldsymbol{{d}}}}{\boldsymbol{{y}}}_k+{\boldsymbol{{u}}}_k^{\mathrm{T}}{\boldsymbol{{R}}_{\boldsymbol{{d}}}}{\boldsymbol{{u}}}_k+({\boldsymbol{{A}}_{\boldsymbol{{d}}}}{\boldsymbol{\xi }}_k+{\boldsymbol{{B}}_{\boldsymbol{{d}}}}{\boldsymbol{{u}}}_k)^{\mathrm{T}}{\boldsymbol{{P}}_{\boldsymbol{{d}}}}({\boldsymbol{{A}}_{\boldsymbol{{d}}}}{\boldsymbol{\xi }}_k+{\boldsymbol{{B}}_{\boldsymbol{{d}}}}{\boldsymbol{{u}}}_k). \end{equation}

To determine the minimizing control, setting the derivative with respect to ${\boldsymbol{{u}}}_k$ to zero can obtain

(20) \begin{equation} {\boldsymbol{{u}}}_k=-({\boldsymbol{{R}}_{\boldsymbol{{d}}}}+{\boldsymbol{{B}}_{\boldsymbol{{d}}}}^{\mathrm{T}}{\boldsymbol{{P}}_{\boldsymbol{{d}}}}{\boldsymbol{{B}}_{\boldsymbol{{d}}}})^{-1}{\boldsymbol{{B}}_{\boldsymbol{{d}}}}^{\mathrm{T}}{\boldsymbol{{P}}_{\boldsymbol{{d}}}}{\boldsymbol{{A}}_{\boldsymbol{{d}}}}{\boldsymbol{\xi }}_k=-{\boldsymbol{{K}}_{\boldsymbol{{d}}}}^*{\boldsymbol{\xi }}_k. \end{equation}

According to ref. [Reference Lewis and Vamvoudakis31], the Riccati equation can be derived as

(21) \begin{equation} {\boldsymbol{{A}}_{\boldsymbol{{d}}}}^{\mathrm{T}}{\boldsymbol{{P}}_{\boldsymbol{{d}}}}{\boldsymbol{{A}}_{\boldsymbol{{d}}}}-{\boldsymbol{{P}}_{\boldsymbol{{d}}}}+{\boldsymbol{{C}}}^{\mathrm{T}}{\boldsymbol{{Q}}_{\boldsymbol{{d}}}}{\boldsymbol{{C}}}-{\boldsymbol{{A}}_{\boldsymbol{{d}}}}^{\mathrm{T}}{\boldsymbol{{P}}_{\boldsymbol{{d}}}}{\boldsymbol{{B}}_{\boldsymbol{{d}}}}({\boldsymbol{{R}}_{\boldsymbol{{d}}}}+{\boldsymbol{{B}}_{\boldsymbol{{d}}}}^{\mathrm{T}}{\boldsymbol{{P}}_{\boldsymbol{{d}}}}{\boldsymbol{{B}}_{\boldsymbol{{d}}}})^{-1}{\boldsymbol{{B}}_{\boldsymbol{{d}}}}^{\mathrm{T}}{\boldsymbol{{P}}_{\boldsymbol{{d}}}}{\boldsymbol{{A}}_{\boldsymbol{{d}}}}={\textbf{0}}. \end{equation}

For the unmeasurable states, an output feedback method is introduced to parameterize the internal state in terms of the filtered inputs and outputs of the system.

Based on the discretized system model (15), the state ${\boldsymbol{\xi }}_k$ is reconstructed with the previous $N$ inputs and outputs.

(22) \begin{equation} {\boldsymbol{\xi }}_k={\boldsymbol{{E}}_{\boldsymbol{{u}}}}\overline{{\boldsymbol{{u}}}}_{k-1,k-N}+{\boldsymbol{{E}}_{\boldsymbol{{y}}}}\overline{{\boldsymbol{{y}}}}_{k-1,k-N}= \left[\begin{array}{@{}c@{\quad}c@{}}{\boldsymbol{{E}}_{\boldsymbol{{u}}}} &{\boldsymbol{{E}}_{\boldsymbol{{y}}}} \end{array}\right] \left[\begin{array}{c} \overline{{\boldsymbol{{u}}}}_{k-1,k-N}\\ \\[-8pt] \overline{{\boldsymbol{{y}}}}_{k-1,k-N} \end{array}\right] ={\boldsymbol{\Theta }}\overline{{\boldsymbol{{z}}}}_{k-1,k-N}, \end{equation}

(23) \begin{equation} \overline{{\boldsymbol{{u}}}}_{k-1,k-N}= \left[\begin{array}{c}{\boldsymbol{{u}}}_{k-1}\\ \\[-8pt]{ \boldsymbol{{u}}}_{k-2}\\ \\[-8pt]\vdots \\ \\[-8pt]{\boldsymbol{{u}}}_{k-N} \end{array}\right]\in \mathbb{R}^{mN}, \qquad \overline{{\boldsymbol{{y}}}}_{k-1,k-N}= \left[\begin{array}{c}{\boldsymbol{{y}}}_{k-1}\\ \\[-8pt]{\boldsymbol{{y}}}_{k-2}\\ \\[-8pt]\vdots \\ \\[-8pt]{\boldsymbol{{y}}}_{k-N} \end{array}\right]\in \mathbb{R}^{pN}, \end{equation}

where $\overline{{\boldsymbol{{u}}}}_{k-1,k-N}$ and $\overline{{\boldsymbol{{y}}}}_{k-1,k-N}$ are the measured input and output sequences over the time interval $[k-N,k-1]$ , respectively, and $\overline{{\boldsymbol{{z}}}}_{k-1,k-N}=[\overline{{\boldsymbol{{u}}}}_{k-1,k-N}^{\mathrm{T}},\overline{{\boldsymbol{{y}}}}_{k-1,k-N}^{\mathrm{T}}]\in \mathbb{R}^l$ , $l=N(m+p)$ , ${\boldsymbol{\Theta }}=[E_u,E_y]$ , and $\boldsymbol{{E}}_{\boldsymbol{{u}}}$ , $\boldsymbol{{E}}_{\boldsymbol{{y}}}$ are parameter matrices of two sequences, respectively, which is derived in ref. [Reference Lewis and Vamvoudakis31].

Substituting (22) into (17) yields

(24) \begin{equation} {{{\boldsymbol{{V}}}}}^{\mu }({\boldsymbol{\xi }}_k)=\overline{{\boldsymbol{{z}}}}_{k-1,k-N}^{\mathrm{T}} \left[\begin{array}{c}{\boldsymbol{{E}}_{\boldsymbol{{u}}}}^{\mathrm{T}}\\ \\[-8pt]{\boldsymbol{{E}}_{\boldsymbol{{y}}}}^{\mathrm{T}} \end{array}\right]{\boldsymbol{{P}}_{\boldsymbol{{d}}}} \!\left[\begin{array}{@{}c@{\quad}c@{}}{\boldsymbol{{E}}_{\boldsymbol{{u}}}} &{\boldsymbol{{E}}_{\boldsymbol{{y}}}} \end{array}\right] \overline{{\boldsymbol{{z}}}}_{k-1,k-N}\equiv \overline{{\boldsymbol{{z}}}}_{k-1,k-N}^{\mathrm{T}}\overline{{\boldsymbol{{P}}_{\boldsymbol{{d}}}}}\overline{{\boldsymbol{{z}}}}_{k-1,k-N}, \end{equation}

where ${\overline{\boldsymbol{{P}}}_{\boldsymbol{{d}}}}={\boldsymbol{\Theta }}^{\mathrm{T}}{\boldsymbol{{P}}_{\boldsymbol{{d}}}}{\boldsymbol{\Theta }}\in \mathbb{R}^{(m+p)N\times (m+p)N}$ . Then, the LQR Bellman equation is written in the form of measured data:

(25) \begin{equation} \overline{{\boldsymbol{{z}}}}_{k-1,k-N}^{\mathrm{T}}{\overline{\boldsymbol{{P}}}_{\boldsymbol{{d}}}}{{\overline{\boldsymbol{{z}}}}}_{k-1,k-N}={\boldsymbol{{y}}}_k^{\mathrm{T}}{\boldsymbol{{Q}}_{\boldsymbol{{d}}}}{\boldsymbol{{y}}}_k+{\boldsymbol{{u}}}_k^{\mathrm{T}}{\boldsymbol{{R}}_{\boldsymbol{{d}}}}{\boldsymbol{{u}}}_k+{{\overline{\boldsymbol{{z}}}}}_{k,k-N+1}^{\mathrm{T}}{\overline{\boldsymbol{{P}}}_{\boldsymbol{{d}}}}{{\overline{\boldsymbol{{z}}}}}_{k,k-N+1}. \end{equation}

The optimal control can be determined online timely using temporal difference (TD) RL method to minimize the following Bellman TD error online [Reference Lewis and Vamvoudakis31]. Then, the Bellman TD error is defined based on Eq. (14), which can be rewritten as

(26) \begin{equation} {\boldsymbol{{e}}}_k=-{{\overline{\boldsymbol{{z}}}}}_{k-1,k-N}^{\mathrm{T}}{\overline{\boldsymbol{{P}}}_{\boldsymbol{{d}}}}{{\overline{\boldsymbol{{z}}}}}_{k-1,k-N}+{\boldsymbol{{y}}}_k^{\mathrm{T}}{\boldsymbol{{Q}}_{\boldsymbol{{d}}}}{\boldsymbol{{y}}}_k+{\boldsymbol{{u}}}_k^{\mathrm{T}}{\boldsymbol{{R}}_{\boldsymbol{{d}}}}{\boldsymbol{{u}}}_k+{{\overline{\boldsymbol{{z}}}}}_{k,k-N+1}^{\mathrm{T}}{\overline{\boldsymbol{{P}}}_{\boldsymbol{{d}}}}{{\overline{\boldsymbol{{z}}}}}_{k,k-N+1}. \end{equation}

The optimal policy is obtained by minimizing value function in terms of the measured data.

(27) \begin{equation} {\boldsymbol{\mu }}({\boldsymbol{\xi }}_k)=arg\min _{\mu _k}\!(\,{\boldsymbol{{y}}}_k^{\mathrm{T}}{\boldsymbol{{Q}}_{\boldsymbol{{d}}}}{\boldsymbol{{y}}}_k+{\boldsymbol{{u}}}_k^{\mathrm{T}}{\boldsymbol{{R}}_{\boldsymbol{{d}}}}{\boldsymbol{{u}}}_k+{{\overline{\boldsymbol{{z}}}}}_{k,k-N+1}^{\mathrm{T}}{{\overline{\boldsymbol{{P}}}}_d}{{\overline{\boldsymbol{{z}}}}}_{k,k-N+1}). \end{equation}

Decomposing ${{\overline{\boldsymbol{{z}}}}}_{k,k-N+1}^{\mathrm{T}}{{\overline{\boldsymbol{{P}}}}_d}{{\overline{\boldsymbol{{z}}}}}_{k,k-N+1}$ , we have

(28) \begin{equation} {{\overline{\boldsymbol{{z}}}}}_{k,k-N+1}^{\mathrm{T}}{\overline{\boldsymbol{{P}}}_{\boldsymbol{{d}}}}{{\overline{\boldsymbol{{z}}}}}_{k,k-N+1}= \left[\begin{array}{c}{\boldsymbol{{u}}}_k\\ \\[-8pt]{{\overline{\boldsymbol{{u}}}}}_{k-1,k-N+1}\\ \\[-8pt]{{\overline{\boldsymbol{{y}}}}}_{k-1,k-N} \end{array}\right]^{\mathrm{T}} \left[\begin{array}{c@{\quad}c@{\quad}c}{\boldsymbol{{p}}}_0 &{\boldsymbol{{p}}}_u &{\boldsymbol{{p}}}_y \\ \\[-8pt]{\boldsymbol{{p}}}_u^{\mathrm{T}} &{\boldsymbol{{P}}}_{22} &{\boldsymbol{{P}}}_{23} \\ \\[-8pt]{\boldsymbol{{p}}}_y^{\mathrm{T}} &{\boldsymbol{{P}}}_{32} &{\boldsymbol{{P}}}_{33} \end{array}\right] \left[\begin{array}{c}{\boldsymbol{{u}}}_k\\ \\[-8pt]{{\overline{\boldsymbol{{u}}}}}_{k-1,k-N+1}\\ \\[-8pt]{{\overline{\boldsymbol{{y}}}}}_{k-1,k-N} \end{array}\right], \end{equation}

where ${\boldsymbol{{p}}}_0\in \mathbb{R}^{m\times m}$ , ${\boldsymbol{{p}}}_u\in \mathbb{R}^{m\times m(N-1)}$ , ${\boldsymbol{{p}}}_y\in \mathbb{R}^{m\times pn}$ . Then, differentiating with respect to ${\boldsymbol{{u}}}_k$ to perform minimization in (27) yields

(29) \begin{equation} {\boldsymbol{{u}}}_k=-({\boldsymbol{{R}}_{\boldsymbol{{d}}}}+{\boldsymbol{{p}}}_0)^{-1}(\,{\boldsymbol{{p}}}_u{{\overline{\boldsymbol{{u}}}}}_{k-1,k-N+1}+{\boldsymbol{{p}}}_y\,{{\overline{\boldsymbol{{y}}}}}_{k,k-N+1}). \end{equation}

Theorem 1. [Reference Liu, Ge, Zhao and Mei15]: Let $\delta ^j,(\,j=1,2,\ldots )$ be the maximum difference between ${{{\boldsymbol{{V}}}}}^j({\boldsymbol{\xi }}_k)$ and ${{{\boldsymbol{{V}}}}}^*({\boldsymbol{\xi }}_k)$ . When adding a discount factor $\gamma (0\leq \gamma \leq 1)$ to the VI algorithm, the convergence rate of the algorithm to the optimal value ${\boldsymbol{{P}}}^*$ is

(30) \begin{equation} \delta ^{j+1}\leq \gamma \!\delta ^j. \end{equation}

Considering the convergence speed in the iteration process of control law, a discount factor is added to the controller. Then LQR Bellman equation with discount factor is expressed as

(31) \begin{equation} {{\overline{\boldsymbol{{z}}}}}_{k-1,k-N}^{\mathrm{T}}{\overline{\boldsymbol{{P}}}_{\boldsymbol{{d}}}}{{\overline{\boldsymbol{{z}}}}}_{k-1,k-N}={\boldsymbol{{y}}}_k^{\mathrm{T}}{\boldsymbol{{Q}}_{\boldsymbol{{d}}}}{\boldsymbol{{y}}}_k+{\boldsymbol{{u}}}_k^{\mathrm{T}}{\boldsymbol{{R}}_{\boldsymbol{{d}}}}{\boldsymbol{{u}}}_k+ \gamma{{\overline{\boldsymbol{{z}}}}}_{k,k-N+1}^{\mathrm{T}}{\overline{\boldsymbol{{P}}}_{\boldsymbol{{d}}}}{{\overline{\boldsymbol{{z}}}}}_{k,k-N+1}. \end{equation}

The optimal control law with discount factor is written as follows:

(32) \begin{equation} {\boldsymbol{{u}}}_k^*=-({\boldsymbol{{R}}_{\boldsymbol{{d}}}}/\gamma +{\boldsymbol{{p}}}_0)^{-1}(\,{\boldsymbol{{p}}}_u{{\overline{\boldsymbol{{u}}}}}_{k-1,k-N+1}+{\boldsymbol{{p}}}_y{\,{\overline{\boldsymbol{{y}}}}}_{k,k-N+1}). \end{equation}

Thus, the optimal interaction force is obtained as ${\boldsymbol{{f}}}={\boldsymbol{{u}}}_k^{*}$ .

3.3. ABLF-based NN inner loop controller

Referring to (1), and taking ${\boldsymbol{{x}}}_1={\boldsymbol{{q}}}$ , ${\boldsymbol{{x}}}_2=\dot{{\boldsymbol{{q}}}}$ , the robot dynamic system can be rewritten as

(33) \begin{align} &\dot{{\boldsymbol{{x}}}}_1={\boldsymbol{{x}}}_2, \nonumber\\[3pt]&\dot{{\boldsymbol{{x}}}}_2={\boldsymbol{{M}}}^{-1}({\boldsymbol{{x}}}_1)[{\boldsymbol{\tau }}+{\boldsymbol{{J}}}^{\mathrm{T}}({\boldsymbol{{x}}}_1){\boldsymbol{{f}}}-{\boldsymbol{{C}}}({\boldsymbol{{x}}}_1,{\boldsymbol{{x}}}_2){\boldsymbol{{x}}}_2-{\boldsymbol{{G}}}({\boldsymbol{{x}}}_1)],\nonumber\\[3pt] &{\boldsymbol{{y}}}={\boldsymbol{{x}}}_1,\end{align}

where ${\boldsymbol{{x}}}_1=[x_{11},x_{12},\cdots,x_{1n}]^{\mathrm{T}}$ , and the desired trajectory is ${\boldsymbol{{x}}_{\boldsymbol{{d}}}}(t)=[q_{d1}(t),q_{d2}(t),\cdots,q_{dn}(t)]^{\mathrm{T}}$ . We need to ensure all states and outputs are within the limits when tracking the desired trajectory, $i.e.$ , $|{\boldsymbol{{x}}}_{1i}|\lt k_{c1i}$ , $|x_{2i}|\lt k_{c2i}$ , $\forall{t}\geq 0$ with $k_{c1i}$ , $k_{c2i}$ being positive constants, where ${\boldsymbol{{k}}_{c1}}=[k_{c11},k_{c12},\cdots,k_{c1n}]^{\mathrm{T}}$ , ${\boldsymbol{{k}}_{c2}}=[k_{c21},k_{c22},\cdots,k_{c2n}]^{\mathrm{T}}$ are positive constant vectors.

Lemma 1: There exists any positive constant vector ${\boldsymbol{{k}}_b}\in \mathbb{R}$ , for $\forall{{\boldsymbol{{x}}}}\in \mathbb{R}$ in the interval $|{\boldsymbol{{x}}}|\lt |{\boldsymbol{{k}}_{\boldsymbol{{b}}}}|$ , then the following inequality holds

(34) \begin{equation} \ln \frac{{\boldsymbol{{k}}_{\boldsymbol{{b}}}}^{2p}}{{\boldsymbol{{k}}_{\boldsymbol{{b}}}}^{2p}-{\boldsymbol{{x}}}^{2p}}\leq \frac{{\boldsymbol{{x}}}^{2p}}{{\boldsymbol{{k}}_{\boldsymbol{{b}}}^{2p}}-{\boldsymbol{{x}}}^{2p}}, \end{equation}

where $p$ is a positive integer.

For the convenience of expression, the controller proposed in the inner loop is expressed in a continuous way. In the real experiment, the torque calculated from the asymmetric constrained controller is exerted to the robot in a discrete way. So it is corresponding to the discrete outer loop.

In order to achieve the requirements of tracking constraint, an ABLF-based controller is employed in the system. The tracking errors are designed as ${\boldsymbol{{z}}}_1=[z_{11},z_{12},\cdots,z_{1n}]^{\mathrm{T}}={\boldsymbol{{x}}}_1-{\boldsymbol{{x}}_{\boldsymbol{{d}}}}$ , ${\boldsymbol{{z}}}_2=[z_{21},z_{22},\cdots,z_{2n}]^{\mathrm{T}}={\boldsymbol{{x}}}_2-{\boldsymbol{\alpha }}$ , and we have $\dot{{\boldsymbol{{z}}}}_1={\boldsymbol{{x}}}_2-{\dot{\boldsymbol{{x}}}_{\boldsymbol{{d}}}}$ . The virtual control $\boldsymbol{\alpha }$ is defined as

(35) \begin{equation} {\boldsymbol{\alpha }}={{\dot{\boldsymbol{{x}}}_{\boldsymbol{{d}}}}}-{\boldsymbol{{T}}}, \end{equation}

where

(36) \begin{equation} \boldsymbol{{T}}= \left[\begin{array}{c} (k_{a1}+z_{11})(k_{b1}-z_{11})k_1z_{11}\\ \\[-8pt](k_{a2}+z_{12})(k_{b2}-z_{12})k_2z_{12}\\ \\[-8pt]\vdots \\ \\[-8pt](k_{an}+z_{1n})(k_{bn}-z_{1n})k_nz_{1n} \end{array}\right]. \end{equation}

Positive constants ${{\boldsymbol{{k}}}_{\boldsymbol{{a}}}}$ and ${{\boldsymbol{{k}}}_{\boldsymbol{{b}}}}$ denote the lower and upper bounds of tracking error where $-{\boldsymbol{{k}}_{\boldsymbol{{a}}}}\lt{{{\boldsymbol{{z}}}_{\textbf{1,2}}}}\lt{\boldsymbol{{k}}_{\boldsymbol{{b}}}}$ , and $k_i$ $\!(i=1,2,\ldots,n)$ denotes a positive constant. Taking the derivation of $\boldsymbol{{z}}_{\textbf{1}}$ and $\boldsymbol{{z}}_{\textbf{2}}$ with respect to time, we have

(37) \begin{equation} {{\dot{\boldsymbol{{z}}}_{\textbf{1}}}}={\boldsymbol{{z}}_{\textbf{2}}}+{\boldsymbol{\alpha }}-{{\dot{\boldsymbol{{x}}}_{\boldsymbol{{d}}}}}={\boldsymbol{{z}}_{\textbf{2}}}-{\boldsymbol{{T}}}, \end{equation}

(38) \begin{equation} {\dot{\boldsymbol{{z}}}_{\textbf{2}}}={\boldsymbol{{M}}}^{-1}({\boldsymbol{{x}}_{\textbf{1}}})[{\boldsymbol{\tau }}+{\boldsymbol{{J}}}^{\mathrm{T}}({\boldsymbol{{x}}_{\textbf{1}}}){\boldsymbol{{f}}}-{\boldsymbol{{C}}}({\boldsymbol{{x}}_{\textbf{1}}},{\boldsymbol{{x}}_{\textbf{2}}}){\boldsymbol{{x}}_{\textbf{2}}}-{\boldsymbol{{G}}}({\boldsymbol{{x}}_{\textbf{1}}})]-{\boldsymbol{\dot{\alpha }}}. \end{equation}

The ABLF is defined as follows:

(39) \begin{equation} {\boldsymbol{{V}}_{\textbf{1}}}=\frac{h_1({\boldsymbol{{z}}_{\textbf{1}}})}{2p}\sum _{i=1}^n\log \frac{k_{bi}^{2p}}{k_{bi}^{2p}-z_{1i}^{2p}}+\frac{1-h_1({\boldsymbol{{z}}_{\textbf{1}}})}{2p}\sum _{i=1}^n\log \frac{k_{ai}^{2p}}{k_{ai}^{2p}-z_{1i}^{2p}}, \end{equation}

where $k_{ai}=x_{di}-\underline{k}_{ci}$ , $k_{bi}=\overline{k}_{ci}-x_{di}$ , $\underline{k}_{ai}\leq k_{ai}\leq \overline{k}_{ai}$ , $\underline{k}_{bi}\leq k_{bi}\leq \overline{k}_{bi}$ , and $h_1\!(z_1)$ is given as

(40) \begin{equation} h_1\!({\boldsymbol{{z}}_{\textbf{1}}})= \begin{cases} 1&{z_i\gt 0}\\ 0&{z_i\leq 0} \end{cases}. \end{equation}

The time derivative of (39) is

(41) \begin{align} {\dot{\boldsymbol{{V}}}_{\textbf{1}}}&=h_1({\boldsymbol{{z}}_{\textbf{1}}})\sum _{i=1}^n\frac{z_{1i}^{2p-1}\dot{z}_{1i}}{k_{bi}^{2p}-z_{1i}^{2p}}+(1-h_1({\boldsymbol{{z}}_{\textbf{1}}}))\sum _{i=1}^n\frac{z_{1i}^{2p-1}\dot{z}_{1i}}{k_{ai}^{2p}-z_{1i}^{2p}} \nonumber\\ &=-h_1({\boldsymbol{{z}}_{\textbf{1}}})\sum _{i=1}^nk_1z_{1i}^{2p}+h_1({\boldsymbol{{z}}_{\textbf{1}}})\sum _{i=1}^n\frac{z_{1i}^{2p-1}z_{2i}}{k_{bi}^{2p}-z_{1i}^{2p}}-(1-h_1({\boldsymbol{{z}}_{\textbf{1}}}))\sum _{i=1}^nk_iz_{1i}^{2p}+(1-h_1({\boldsymbol{{z}}_{\textbf{1}}}))\sum _{i=1}^n\frac{z_{1i}^{2p-1}z_{2i}}{k_{ai}^{2p}-z_{1i}^{2p}} \nonumber\\ &=-\sum _{i-1}^nk_iz_{1i}^{2p}+h_1({\boldsymbol{{z}}_{\textbf{1}}})\sum _{i=1}^n\frac{z_{1i}^{2p-1}z_{2i}}{k_{bi}^{2p}-z_{1i}^{2p}}+(1-h_1({\boldsymbol{{z}}_{\textbf{1}}}))\sum _{i=1}^n\frac{z_{1i}^{2p-1}z_{2i}}{k_{ai}^{2p}-z_{1i}^{2p}}. \end{align}

Then, a second Lyapunov function is designed as

(42) \begin{equation} {\boldsymbol{{V}}_{\textbf{2}}}={\boldsymbol{{V}}_{\textbf{1}} }+\frac{1}{2}{\boldsymbol{{z}}_{\textbf{2}}}^{\mathrm{T}}{\boldsymbol{{M}}}({\boldsymbol{{x}}_{\textbf{1}}}){\boldsymbol{{z}}_{\textbf{2}}}. \end{equation}

Take the time derivation of $\boldsymbol{{V}}_{\textbf{2}}$ to obtain

(43) \begin{equation} {\dot{\boldsymbol{{V}}}_{\textbf{2}}}={\dot{\boldsymbol{{V}}}_{\textbf{1}} }+{\boldsymbol{{z}}_{\textbf{2}}}^{\mathrm{T}}[{\boldsymbol{\tau }}+{\boldsymbol{{J}}}^{\mathrm{T}}({\boldsymbol{{x}}_{\textbf{1}}}){\boldsymbol{{f}}}-{\boldsymbol{{C}}}({\boldsymbol{{x}}_{\textbf{1}}},{\boldsymbol{{x}}_{\textbf{2}}}){\boldsymbol{\alpha }}+{\boldsymbol{{G}}}({\boldsymbol{{x}}_{\textbf{1}}})-{\boldsymbol{{M}}}({\boldsymbol{{x}}_{\textbf{1}}}){\boldsymbol{\dot \alpha }}]. \end{equation}

When ${\boldsymbol{{z}}_{\textbf{2}}}=[0,0,\ldots,0]^{\mathrm{T}}$ , ${\dot{\boldsymbol{{V}}}_{\textbf{2}}}=-\sum _{i=1}^nk_iz_{1i}^{2p}\lt 0$ . According to Lyapunov stability theory, the system is asymptotically stable. On the contrary, according to the property of the Moore-Penrose pseudoinverse, if ${\boldsymbol{{z}}_{\textbf{2}}}\neq [0,0,\ldots,0]^{\mathrm{T}}$ , ${\boldsymbol{{z}}_{\textbf{2}}}^{\mathrm{T}}({\boldsymbol{{z}}_{\textbf{2}}}^{\mathrm{T}})^+=1$ . $\boldsymbol{{K}}_{\textbf{2}}$ is a designed positive gain matrix with $\boldsymbol{{K}}_{\textbf{2}}={\boldsymbol{{K}}_{\textbf{2}}}^{\mathrm{T}}\gt 0$ . Then the model-based controller is designed as

(44) \begin{align}{\boldsymbol{\tau}_{0}}& =-{\boldsymbol{{J}}}^{\mathrm{T}}({\boldsymbol{{x}}_{\textbf{1}}}){\boldsymbol{{f}}}+{\boldsymbol{{C}}}({\boldsymbol{{x}}_{\textbf{1}}},{\boldsymbol{{x}}_{\textbf{2}}}){\boldsymbol{\alpha }}+{\boldsymbol{{G}}}({\boldsymbol{{x}}_{\textbf{1}}})+{\boldsymbol{{M}}}({\boldsymbol{{x}}_{\textbf{1}}}){\boldsymbol{\dot \alpha }}-{\boldsymbol{{K}}_{\textbf{2}}}{\boldsymbol{{z}}_{\textbf{2}}} \nonumber\\[3pt]&\quad -h_1({\boldsymbol{{z}}_{\textbf{1}}})({\boldsymbol{{z}}_{\textbf{2}}}^{\mathrm{T}})^+\sum _{i=1}^n\frac{z_{1i}^{2p-1}z_{2i}}{k_{bi}^{2p}-z_{1i}^{2p}}-(1-h_1({\boldsymbol{{z}}_{\textbf{1}}}))({\boldsymbol{{z}}_{\textbf{2}}}^{\mathrm{T}})^+\sum _{i=1}^n\frac{z_{1i}^{2p-1}z_{2i}}{k_{ai}^{2p}-z{1i}^{2p}}. \end{align}

Substituting (44) into (43) yields

(45) \begin{equation} {\dot{\boldsymbol{{V}}}_{\textbf{2}}}=-\sum _{i=1}^nk_iz_{1i}^{2p}-{\boldsymbol{{z}}_{\textbf{2}}}^{\mathrm{T}}{\boldsymbol{{K}}_{\textbf{2}}}{\boldsymbol{{z}}_{\textbf{2}}}=-({\boldsymbol{{z}}_{\textbf{1}}}^{\mathrm{T}})^p{\boldsymbol{{K}}_{\textbf{1}}}{\boldsymbol{{z}}_{\textbf{1}}}^p-{\boldsymbol{{z}}_{\textbf{2}}}^{\mathrm{T}}{\boldsymbol{{K}}_{\textbf{2}}}{\boldsymbol{{z}}_{\textbf{2}}}\lt 0. \end{equation}

where ${\boldsymbol{{K}}_{\textbf{2}}}={\boldsymbol{{K}}_{\textbf{2}}}^{\mathrm{T}}=diag([k_1,k_2,\ldots,k_n])\gt 0$ is a diagonal matrix.

Eq.(44) is model-based, in real applications, the dynamic parameters ${\boldsymbol{{C}}}({\boldsymbol{{x}}_{\textbf{1}}},{\boldsymbol{{x}}_{\textbf{2}}})$ , ${\boldsymbol{{G}}}({\boldsymbol{{x}}_{\textbf{1}}})$ , ${\boldsymbol{{M}}}({\boldsymbol{{x}}_{\textbf{1}}})$ , and $\boldsymbol{{f}}$ are difficult to be obtained. To address uncertainties in the dynamic model, an adaptive NN method is employed to fit the uncertain terms. The adaptive NN control is proposed as

(46) \begin{align}{{{\boldsymbol{\tau }}}_{1}}&=-{h_1({\boldsymbol{{z}}_{\textbf{1}}}){({\boldsymbol{{z}}_{\textbf{2}}}^{T})}^{+}}\sum \limits _{i=1}^{n}{\frac{{{k}_{i}}z_{1i}^{2p}}{k_{bi}^{2p}-z_{1i}^{2p}}}-{(1-h_1({\boldsymbol{{z}}_{\textbf{1}}})){({\boldsymbol{{z}}_{\textbf{2}}}^{T})}^{+}}\sum \limits _{i=1}^{n}{\frac{{{k}_{i}}z_{1i}^{2p}}{k_{ai}^{2p}-z_{1i}^{2p}}}-{{h}_{1}}({{\boldsymbol{{z}}_{\textbf{1}}}}){{({\boldsymbol{{z}}_{\textbf{2}}}^{T})}^{+}}\sum \limits _{i=1}^{n}{\frac{z_{_{1i}}^{2p-1}{{z}_{2i}}}{k_{bi}^{2p}-z_{1i}^{2p}}} \nonumber\\[3pt]&\quad -(1-{{h}_{1}}({{\boldsymbol{{z}}_{\textbf{1}}}})){{({\boldsymbol{{z}}_{\textbf{2}}}^{T})}^{+}}\sum \limits _{i=1}^{n}{\frac{z_{1i}^{2p-1}{{z}_{2i}}}{k_{ai}^{2p}-z_{1i}^{2p}}}-{{\boldsymbol{{K}}_{\textbf{{2}}}}}{{\boldsymbol{{z}}_{\textbf{2}}}}-{{{\hat{{\boldsymbol{{W}}}}}}^{T}}{\boldsymbol{{S}}}({\boldsymbol{{Z}}})-{{{\boldsymbol{{J}}}}^{T}}({{\boldsymbol{{x}}_{\textbf{1}}}}){\boldsymbol{{f}}}. \end{align}

where ${\boldsymbol{{S}}}({\cdot})$ is the basis function, $\hat{{\boldsymbol{{W}}}}\in{{R}^{l\times n}}$ is the estimation weight in which $l$ is the node number of NN, and ${\boldsymbol{{Z}}}={{\left [{\boldsymbol{{x}}_{\textbf{1}}}^{T},{\boldsymbol{{x}}_{\textbf{2}}}^{T},{{{\boldsymbol{\alpha }}}^{T}},{{{\dot{{\boldsymbol{\alpha }}}}}^{T}} \right ]}^{T}}$ is the input of the NN basis function. With the property of approximating any nonlinearity, ${{\hat{{\boldsymbol{{W}}}}}^{T}}{\boldsymbol{{S}}}({\boldsymbol{{Z}}})$ is the estimation of ${{{\boldsymbol{{W}}}}^{*T}}{\boldsymbol{{S}}}({\boldsymbol{{Z}}})$ , while the ${{{\boldsymbol{{W}}}}^{*T}}{\boldsymbol{{S}}}({\boldsymbol{{Z}}})$ represents the real value of unknown term in model-based control (45) with

(47) \begin{equation} {{{\boldsymbol{{W}}}}^{*T}}{\boldsymbol{{S}}}({\boldsymbol{{Z}}})+{{\varepsilon }} =-{\boldsymbol{{C}}}({{\boldsymbol{{x}}_{\textbf{1}}}},{{\boldsymbol{{x}}_{\textbf{2}}}}){\boldsymbol{\alpha }} -{\boldsymbol{{G}}}({{\boldsymbol{{x}}_{\textbf{1}}}})-{\boldsymbol{{M}}}({{\boldsymbol{{x}}_{\textbf{1}}}})\dot{{\boldsymbol{\alpha }}}. \end{equation}

The adaptive law is designed as

(48) \begin{equation} {{\dot{\hat{{\boldsymbol{{W}}}}}}_{i}}={{{\boldsymbol{\Gamma }}}_{i}}\left [{{\boldsymbol{{S}}}_{i}}({\boldsymbol{{Z}}}){{z}_{2i}}-{{\boldsymbol{{e}}_{i}}}\left |{{z}_{2i}} \right |{{{\dot{\hat{{\boldsymbol{{W}}}}}}}_{i}} \right ]. \end{equation}

Then, the BLF $V_3$ is designed as

(49) \begin{equation} {{\boldsymbol{{V}}_{\textbf{3}}}}={{\boldsymbol{{V}}_{\textbf{3}}}}+\frac{1}{2}\sum \limits _{i=1}^{n}{\tilde{{\boldsymbol{{W}}}}_{i}^{T}{\boldsymbol{\Gamma }} _{i}^{-1}{{{\tilde{{\boldsymbol{{W}}}}}}_{i}}}, \end{equation}

where ${{\tilde{{\boldsymbol{{W}}}}}_{i}}={{\hat{{\boldsymbol{{W}}}}}_{i}}-{\boldsymbol{{W}}}_{i}^{*}$ and ${\tilde{{\boldsymbol{{W}}}}}_{i}$ , ${\hat{{\boldsymbol{{W}}}}}_{i}$ , ${\boldsymbol{{W}}}_{i}^{*}$ are the NN weight error, approximation, and ideal value, respectively. Differentiating (49), we have

(50) \begin{align} {\dot{\boldsymbol{{V}}}_{\textbf{3}}}&=-\sum _{i=1}^{n}{{{k}_{i}}z_{1i}^{2p}}+{{h}_{1}}({{\boldsymbol{{z}}_{\textbf{1}}}})\sum _{i=1}^{n}{\frac{z_{1i}^{2p-1}{{z}_{2i}}}{k_{bi}^{2p}-z_{1i}^{2p}}}+(1-{{h}_{1}}({{\boldsymbol{{z}}_{\textbf{1}}}}))\sum _{i=1}^{n}{\frac{z_{1i}^{2p-1}{{z}_{2i}}}{k_{ai}^{2p}-z_{1i}^{2p}}} \nonumber\\[3pt]&\quad +\sum _{i=1}^{n}{\tilde{{\boldsymbol{{W}}}}_{i}^{T}{\boldsymbol{\Gamma }} _{i}^{-1}{{{\dot{\hat{{\boldsymbol{{W}}}}}}}_{i}}}+{\boldsymbol{{z}}}_{2}^{T}[{{{\boldsymbol{\tau }}}_{1}}+{{{\boldsymbol{{J}}}}^{T}}({{\boldsymbol{{x}}_{\textbf{1}}}}){\boldsymbol{{f}}}-{\boldsymbol{{C}}}({{\boldsymbol{{x}}_{\textbf{1}}}},{{\boldsymbol{{x}}_{\textbf{2}}}}){\boldsymbol{\alpha }} -{\boldsymbol{{G}}}({{\boldsymbol{{x}}_{\textbf{1}}}})-{\boldsymbol{{M}}}({{\boldsymbol{{x}}_{\textbf{1}}}})\dot{{\boldsymbol{\alpha }}}. \end{align}

If ${{\boldsymbol{{z}}_{\textbf{2}}}}\ne{{\left [ 0,0,\ldots,0 \right ]}^{T}}$ , according to (46)–(48), we have

(51) \begin{equation} \begin{aligned}{\dot{\boldsymbol{{V}}}_{\textbf{3}}}&\leq -\sum _{i=1}^{n}{{{k}_{i}}z_{1i}^{2p}}-{\boldsymbol{{z}}}_{2}^{T}{{\boldsymbol{{K}}_{\textbf{2}}}}{{\boldsymbol{{z}}_{\textbf{2}}}}-{{h}_{1}}({{\boldsymbol{{z}}_{\textbf{1}}}})\sum _{i=1}^{n}{\frac{{{k}_{i}}z_{1i}^{2p}}{k_{bi}^{2p}-z_{1i}^{2p}}}-(1-{{h}_{1}}({{\boldsymbol{{z}}_{\textbf{1}}}}))\sum _{i=1}^{n}{\frac{{{k}_{i}}z_{1i}^{2p}}{k_{ai}^{2p}-z_{1i}^{2p}}}\\ &+\sum _{i=1}^{n}{\left[\tilde{{\boldsymbol{{W}}}}_{i}^{T}{{{\boldsymbol{{S}}}}_{i}}({\boldsymbol{{Z}}}){{z}_{2i}}-{{e}_{i}}|{{z}_{2i}} |\tilde{{\boldsymbol{{W}}}}_{i}^{T}{{{\hat{{\boldsymbol{{W}}}}}}_{i}} \right]}+{\boldsymbol{{z}}_{\textbf{2}}}^{T}{{\varepsilon }} ({\boldsymbol{{Z}}})+{\boldsymbol{{z}}_{\textbf{2}}}^{T}[\!-\!{{{\hat{{\boldsymbol{{W}}}}}}^{T}}{\boldsymbol{{S}}}({\boldsymbol{{Z}}})+{{{\boldsymbol{{W}}}}^{*}}^{T}{\boldsymbol{{S}}}({\boldsymbol{{Z}}})\\ & \leq -{\boldsymbol{{z}}_{\textbf{2}}}^{T}\left({{\boldsymbol{{K}}_{\textbf{2}}}}-\frac{3}{4}{{{\boldsymbol{{I}}}}}\right){{\boldsymbol{{z}}_{\textbf{2}}}}-{{h}_{1}}({{\boldsymbol{{z}}_{\textbf{1}}}})\sum _{i=1}^{n}{{{k}_{i}}\ln \frac{k_{bi}^{2p}}{k_{bi}^{2p}-z_{1i}^{2p}}-(1-{{h}_{1}}({{\boldsymbol{{z}}_{\textbf{1}}}}))}\sum _{i=1}^{n}{{{k}_{i}}\ln \frac{k_{ai}^{2p}}{k_{ai}^{2p}-z_{1i}^{2p}}}\\ &+\frac{1}{2}{{\|{{\varepsilon }} ({\boldsymbol{{Z}}})\|}^{2}}+\sum _{i=1}^{n}{\frac{{\boldsymbol{{e}}}_{i}^{2}}{4}}({{\|{\boldsymbol{{W}}}_{i}^{*}\|}^{4}}+{{\|{{{\tilde{{\boldsymbol{{W}}}}}}_{i}} \|}^{4}}-2{{\|{\boldsymbol{{W}}}_{i}^{*}\|}^{2}}{{\|{{{\tilde{{\boldsymbol{{W}}}}}}_{i}} \|}^{2}}) \leq -\rho{{\boldsymbol{{V}}_{\textbf{3}}}}+{\boldsymbol{{C}}}, \end{aligned} \end{equation}

where

(52) \begin{equation} \rho =\min\!\left ( \min\!(2{{k}_{i}}),\min \!(2(1-{{h}_{1}}({{\boldsymbol{{z}}_{\textbf{1}}}})){{k}_{i}}),\frac{2{{\lambda }_{\min }}({{\boldsymbol{{K}}_{\textbf{2}}}}-\tfrac{3}{4}{{{\boldsymbol{{I}}}}})}{{{\lambda }_{\max }}({\boldsymbol{{M}}})},\min \!\left ( \frac{{\boldsymbol{{z}}}_{i}^{2}{{\left \|{\boldsymbol{{W}}}_{i}^{*} \right \|}^{2}}}{{{\lambda }_{\max }}({\boldsymbol{\Gamma }} _{i}^{-1})} \right ) \right ),\end{equation}

(53) \begin{equation} {\boldsymbol{{C}}}=\frac{1}{2}{{\left \|{{\varepsilon }} ({\boldsymbol{{Z}}}) \right \|}^{2}}+\sum \limits _{i=1}^{n}{\left ( \frac{{\boldsymbol{{z}}}_{i}^{2}}{4}{{\left \|{\boldsymbol{{W}}}_{i}^{*} \right \|}^{4}}+\frac{{\boldsymbol{{z}}}_{i}^{2}}{4}{{N}^{4}} \right )}. \end{equation}

Here ${{\lambda }_{\min }}({\bullet})$ and ${{\lambda }_{\max }}({\bullet})$ denote the minimum and maximum eigenvalues of the matrix, respectively, and

(54) \begin{equation} \min \!(2{{k}_{i}})\gt 0,\text{ }{{\lambda }_{\min }}\!\left({{\boldsymbol{{K}}_{\textbf{2}}}}-\frac{3}{4}{{{\boldsymbol{{I}}}}}\right)\gt 0, \text{ }N=\left (\frac{\lambda _{\max }({\boldsymbol{\Gamma }}_i)}{\lambda _{\min }({\boldsymbol{\Gamma }}_i)}\right )^{\frac{1}{2}}\frac{{\left \|{\boldsymbol{{s}}}_i \right \|}}{{\boldsymbol{{z}}}_i}+\left \|{\boldsymbol{{W}}}_i^* \right \| \end{equation}

with $\left \|{\boldsymbol{{S}}}_i({\boldsymbol{{Z}}}) \right \| \le \left \|{\boldsymbol{{s}}}_i \right \|$ and ${\boldsymbol{{s}}}_i\gt 0$ .

Theorem 2 Consider the dynamic system described in (1). If the initial conditions satisfy $\left |{{x}_{1i}}(0) \right |\lt{{k}_{c1i}}$ , $\left |{{x}_{2i}}(0) \right |\lt{{k}_{c2i}}$ , the control law (46) ensures that all error signals are semi-globally uniformly bounded. Then the position and velocity constraints are not violated, that is, $\forall t\gt 0$ , $\text{ }\left |{{x}_{1i}}(t) \right |\le{{k}_{c1i}}$ , $\text{ }\left |{{x}_{2i}}(t) \right |\le{{k}_{c2i}}$ . The closed-loop error signals $\boldsymbol{{z}}_1$ , $\boldsymbol{{z}}_2$ and $\tilde{{\boldsymbol{{W}}}}$ remain in compact sets ${\boldsymbol{\Omega }}_{{\boldsymbol{{z}}_{\textbf{1}}}}$ , ${\boldsymbol{\Omega }}_{{\boldsymbol{{z}}_{\textbf{2}}}}$ , ${\boldsymbol{\Omega }}_{{\boldsymbol{{w}}}}$ , respectively.

(55) \begin{align} &{{\boldsymbol{\Omega }}_{{\boldsymbol{{z}}_{\textbf{1}}}}}:=\left \{{{\boldsymbol{{z}}_{\textbf{1}}}}\in{{R}^{n}}|-\sqrt [2p]{k_{ai}^{2p}\left ( 1-{{e}^{-{\boldsymbol{{D}}}}} \right )}\le{{z}_{1i}} \le \sqrt [2p]{k_{bi}^{2p}\left ( 1-{{e}^{-{\boldsymbol{{D}}}}} \right )},i=1,\ldots,n \right \} \nonumber \\[3pt] &{{\boldsymbol{\Omega }}_{{\boldsymbol{{z}}_{\textbf{2}}}}}:=\left \{{{\boldsymbol{{z}}_{\textbf{2}}}}\in{{R}^{n}}|\left \|{{\boldsymbol{{z}}_{\textbf{2}}}} \right \|\le \sqrt{\frac{{\boldsymbol{{D}}}}{{{\lambda }_{\min }}({\boldsymbol{{M}})}}} \right \}, \text{ }{{\boldsymbol{\Omega }}_{\boldsymbol{{w}}}}:=\left \{ \tilde{{\boldsymbol{{W}}}}\in{{R}^{l\times n}}|\left \|{\tilde{{\boldsymbol{{W}}}}} \right \|\le \sqrt{\frac{{\boldsymbol{{D}}}}{{{\lambda }_{\min }}({{\boldsymbol{\Gamma }}^{-1}})}} \right \}, \end{align}

where ${\boldsymbol{{D}}}=2\left ({\boldsymbol{{V}}_{\textbf{3}}}(0)+{\boldsymbol{{C}}}/\rho \right )$ .

Proof: Multiplying $e^{\rho t}$ on both sides of (51), we have

(56) \begin{equation} e^{\rho t}\dot{{{{\boldsymbol{{V}}}}}}_3\leq -\rho{{{\boldsymbol{{V}}}}}_3e^{\rho t}+{\boldsymbol{{C}}}e^{\rho t}, \end{equation}

and

(57) \begin{equation} d(e^{\rho t}{{{\boldsymbol{{V}}}}_3})/dt\leq{\boldsymbol{{C}}}e^{\rho t} \end{equation}

Then, we have

(58) \begin{equation} {{{\boldsymbol{{V}}}}_3}\leq \left({{{\boldsymbol{{V}}}}_3}(0)-\frac{{\boldsymbol{{C}}}}{\rho }\right)e^{-\rho t}+\frac{{\boldsymbol{{C}}}}{\rho }\leq{{{\boldsymbol{{V}}}}_3}(0)+\frac{{\boldsymbol{{C}}}}{\rho }. \end{equation}

According to (58) and the property of inequality in ref. [Reference Zhang, Dong, Ouyang, Yin and Peng32], we have

(59) \begin{align} -\frac{1}{2}\sum _{i=1}^n\ln{\frac{k_{ai}^{2p}}{k_{ai}^{2p}-z_{1i}^{2p}}}\leq &{{{\boldsymbol{{V}}}}_3}(0)+{\boldsymbol{{C}}}/\rho \leq \frac{1}{2}\sum _{i=1}^n\ln{\frac{k_{bi}^{2p}}{k_{bi}^{2p}-z_{1i}^{2p}}} \nonumber\\[3pt]\frac{1}{2}\|{\boldsymbol{{z}}_{\textbf{2}}}\|^2&\leq \frac{{{{\boldsymbol{{V}}}}_3}(0)+C/\rho }{\lambda _{min}({\boldsymbol{{M}}})} \nonumber\\[3pt] \frac{1}{2}\|\tilde{{\boldsymbol{{W}}}}\|^2&\leq \frac{{{{\boldsymbol{{V}}}}_3}(0)+{\boldsymbol{{C}}}/\rho }{\lambda _{min}({\boldsymbol{\Gamma }}^{-1})} \end{align}

4.1. Inner loop

In order to validate the effectiveness and feasibility of the designed asymmetric constrained controller. Simulations using the proposed controller, PD controller, and controller in ref. [Reference Zheng and Fang33] are carried in MATLAB.

The initial positions of the robot are given as ${{\boldsymbol{{q}}_{\textbf{1}}}}(0)=[0.4,-0.1](rad)$ , ${{\boldsymbol{{q}}_{\textbf{2}}}}(0)=[0,0](rad/s)$ . The interaction force with end-effector is ${\boldsymbol{{f}}}(t)=[\!\sin\!(t)+1,\cos\!(t)+0.5](N)$ , where $t\in [0,{{t}_{f}}]$ and $t_f=20s$ . The desired trajectory is set as ${{{\boldsymbol{{q}}}_{\boldsymbol{{r}}}}}=[0.1\sin\!(t)+0.5\cos\!(t),0.1\cos\!(t)+0.5\sin\!(t)](rad)$ . The limits of tracking errors are ${{{\boldsymbol{{k}}_{\boldsymbol{{a}}}}}}=[\!-\!1.0,-1.0](rad)$ , ${{{{\boldsymbol{{k}}}_{\boldsymbol{{b}}}}}}=[1.2,1.2](rad)$ , and the physical limit of robot joints is ${{{\boldsymbol{{k}}}_{\boldsymbol{{c}}}}}=[1.8,1.8](rad)$ .

Figure 3.

Tracking performance of different controllers of Joints 1 and 2.

Figure 4.

Tracking error of different controllers of Joints 1 and 2.

For PD control, the controller is designed as ${\boldsymbol{\tau }} =-{{\boldsymbol{{G}}_{\textbf{1}}}}({\boldsymbol{{q}}_{\textbf{1}}}-{{\boldsymbol{{q}}}_{\boldsymbol{{r}}}})-{\boldsymbol{{G}}_{\textbf{2}}}({\boldsymbol{{q}}_{\textbf{2}}}-{\dot{\boldsymbol{{q}}}_{\textbf{r}}})$ where ${\boldsymbol{{G}}_{\textbf{1}}}=diag(50,30)$ and ${\boldsymbol{{G}}_{\textbf{2}}}=diag(30,3)$ .

For the proposed controller, Eq. (46) is the control law applied in the inner loop. ${\boldsymbol{{S}}}({\cdot})$ is defined as a Gaussian function. As for the gain of the NN, $\sigma$ is chosen as 0.2, the number of the NN nodes is $2^8$ , and the centers are selected in the area of $[\!-\!1,1]\times 5$ . The variance of centers is chosen as 0.5, and the initial value of the NN weight matrix is defined as 0. The control parameters are set as ${\boldsymbol{{K}}_{\textbf{2}}}=diag(3,3)$ , ${\boldsymbol{{k}}_{\textbf{1}}}={\boldsymbol{{k}}_{\textbf{2}}}=50$ , ${\boldsymbol{{\Gamma }}_{\textbf{1}}}={\boldsymbol{{\Gamma }}_{\textbf{2}}}=100{{{{\boldsymbol{{I}}}}}_{256\times 256}}$ after regulating.

The tracking results are shown in Figs. 3 and 4. From Fig. 4, the tracking errors of PD controller remain relatively large, and the tracking errors of the controller in ref. [Reference Zheng and Fang33] are smaller. It is seen that the tracking errors are almost close to zero using our proposed method, which is better than the other methods. The comparison results show that our proposed method improves the tracking performance compared with PD controller and the controller in ref. [Reference Zheng and Fang33]. The control input of the three methods is shown in Fig. 5. The torque of the proposed controller is relatively stable.

Figure 5.

Control input of three control methods.

4.2. Outer loop

In the outer loop, a robot manipulator with two revolute joints physically interacting with the human/environment is considered. The simulation is implemented in MATLAB.

In the simulation, the sample interval is set to 1 ms. The adaptation procedure includes three steps. Firstly, the exploration noise is added to the initial control input to guarantee the PE conditions [Reference Vrabie and Lewis10], which is chosen as ${{e}_{k}}=\sum \nolimits _{\omega =1}^{10}{(0.06/\omega )\sin\!(k\omega )}$ . Then, optimal impedance learning is conducted until the criterion is satisfied, which is $|{\bar{\boldsymbol{{P}}}_{\boldsymbol{{d}}}}^{\,\,\,j+1}-{\bar{\boldsymbol{{P}}}_{\boldsymbol{{d}}}}^{\,\,\,j}|\lt 0.001$ . Finally, the optimized impedance model is acquired through iterations.

The parameters in (10) are defined as $Q_1=1$ , $Q_2=0.3$ , $R=0.1$ . In the simulation, the environment model is selected as $\ddot{x}+10\dot{x}+100(x-{{x}_{e}})=-F$ . The environment position and robot desired trajectory in the Cartesian space are given by (12), with $F_1=-1$ , $G_1=0.3$ , $F_2=-1$ , $G_2=1$ . All the elements of the initial matrix $\overline{\boldsymbol{{P}}}_{\boldsymbol{{d}}}$ are selected as 0.01, and $\gamma$ is set as 0.3. Figures 6 and 7 show the convergence of $\overline{\boldsymbol{{P}}}_{\boldsymbol{{d}}}$ . ${\boldsymbol{p}}_{\textbf{0}}$ , ${{\boldsymbol{{p}}}_{\boldsymbol{{y}}}}$ , $\boldsymbol{{p}}_{\boldsymbol{{u}}}$ reach convergence after 17 steps.

Figure 6.

Convergence of the parameters ${{\boldsymbol{{p}}}_{\boldsymbol{0}}}$ and $\boldsymbol{{p}}_{\boldsymbol{{u}}}$ .

Figure 7.

Convergence of the parameters ${{\boldsymbol{{p}}}_{\boldsymbol{{y}}\boldsymbol{0}}}$ , ${{\boldsymbol{{p}}}_{\boldsymbol{{y}}\boldsymbol{1}}}$ , ${{\boldsymbol{{p}}}_{\boldsymbol{{y}}\boldsymbol{2}}}$ and ${{\boldsymbol{{p}}}_{\boldsymbol{{y}}\boldsymbol{3}}}$ .

The optimal values of $\overline{\boldsymbol{{P}}}_{\boldsymbol{{d}}}$ are shown as follows:

\begin{align*} & p_{0}^{*}=15.9258, \text { }p_{u1}^{*}=-32.8697, \text { }p_{u2}^{*}=42.5815, \text { }p_{u3}^{*}=-29.1587 \\[3pt]& {{\boldsymbol{{p}}}_{\boldsymbol{{y}0}}}^{*}=[\!-4.2895,-2.2678,0], \text { }{{\boldsymbol{{p}}}_{\boldsymbol{{y}1}}}^{*}=[8.8085,-2.0308,0] \\[3pt] & {{\boldsymbol{{p}}}_{\boldsymbol{{y}2}}}^{*}=[1.0536,-1.5435,0], \text { }{{\boldsymbol{{p}}}_{\boldsymbol{{y}3}}}^{*}=[5.7937,-1.1972,0]. \end{align*}

Then, the optimal matrix $\boldsymbol{{P}}_{\boldsymbol{{d}}}$ is obtained from $\overline{\boldsymbol{{P}}}_{\boldsymbol{{d}}}$ according to (24) as follows:

(60) \begin{equation} {{\boldsymbol{{P}}}_{\boldsymbol{{d}}}}=\left [ \begin{array}{c@{\quad}c@{\quad}c@{\quad}c} -7.5863 & -18.5025 & 9.2325 & 1.9694 \\ \\[-8pt]-18.5025 & -4.2168 & -18.8880 & 11.1250 \\ \\[-8pt]9.2325 & -18.8880 & 19.6568 & -3.7325 \\ \\[-8pt]1.9694 & 11.1250 & -3.7325 & -1.7415 \end{array} \right ]. \end{equation}

For comparison, the ideal optimal matrix ${\boldsymbol{{P}}_{\boldsymbol{{d}}}}^{*}$ is calculated by using the model information as follows:

(61) \begin{equation} {{\boldsymbol{{P}}}_{\boldsymbol{{d}}}}^{*}=\left [ \begin{array}{c@{\quad}c@{\quad}c@{\quad}c} -7.26 & -18.3032 & 9.2331 & 1.8680 \\ \\[-8pt]-18.3032 & -3.9880 & -18.9033 & 11.0891 \\ \\[-8pt]9.2331 & -18.9033 & 19.64 & -3.875 \\ \\[-8pt]1.8680 & 11.0891 & -3.875 & -1.731 \end{array} \right ]. \end{equation}

As we know, the matrix ${\boldsymbol{{P}}_{\boldsymbol{{d}}}}^{*}$ is the optimal solution to minimize the cost function. From the two matrices, it is obvious that the optimal matrix from our method is close to the real solution, which validates the correctness of the proposed algorithm.