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This paper proposes a decentralized variable impedance control method of modular robot manipulators (MRM) with physical human-robot interaction (pHRI) using Gaussian process-based motion intention estimation. The dynamic model of MRM subsystem is established by using joint torque feedback (JTF) technique. Human limb dynamic model is regarded as mechanical impedance model, and human motion intention is estimated online based on Gaussian process. A variable impedance control method is proposed to make the MRM comply with human motion intention in the process of pHRI. A decentralized sliding mode control strategy is designed to achieve high performance position tracking and compensate the uncertainty of the controller. Based on Lyapunov theory, the uniform ultimately bounded of tracking error of each joint is proved. Finally, the effectiveness of the proposed control method under pHRI is verified by experiments. The experimental results show that the proposed method reduces the position tracking error by ∼ 10% and the interaction force by ∼ 20% compared with the existing control methods. [ABSTRACT FROM AUTHOR]

Summary: This article presents a multiplayer zero‐sum game optimal control for modular robot manipulators (MRMs) via adaptive dynamic programming (ADP). The dynamic model of MRMs with interconnected dynamic couplings (IDCs) is formulated. The control law and IDC effect are regarded as players in each module. The optimal tracking control problem of the MRM system can be transformed into a multiplayer zero‐sum game. Base on ADP algorithm, the Hamilton–Jacobi–Issacs (HJI) equation is solved via critic neural network. Then, the optimal control law can be derivated. The closed‐loop robotic system is proved to be asymptotic stable and experiments are conducted to verify the effectiveness of the control method. [ABSTRACT FROM AUTHOR]

A Stackelberg game-based approximate optimal interaction control approach is presented for human-centered modular robot manipulator (MRM) systems. Joint torque feedback (JTF) technique is utilized to form the MRM dynamic model. The major objective of optimal control with human–robot collaboration (HRC) is transformed into approximating Stackelberg equilibrium by adopting Stackelberg game governed between the human and the MRM that are regarded as players with different hierarchical level in interaction process. On the basis of the adaptive dynamic programming (ADP), the approximate optimal interaction control policy with HRC task is developed via critic neural network (NN)-based Stackelberg game manner for addressing Hamilton–Jacobian (HJ) equations. The position tracking error is ultimately uniformly bounded (UUB) according to the Lyapunov theory. Experiment results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A critic-observer decentralized force/position approximate optimal control method is presented to address the joint trajectory and contacted force tracking problem of modular and reconfigurable manipulators (MRMs) with uncertain environmental constraints. The dynamic model of the MRM systems is formulated as an integration of joint subsystems via extensive state observer (ESO) associated with the effect of interconnected dynamic coupling (IDC). A radial basis function neural network (RBF-NN) is developed to deal with the IDC effects among the independent joint subsystems. Based on adaptive dynamic programming (ADP) approach and policy iteration (PI) algorithm, the Hamilton–Jacobi–Bellman (HJB) equation is approximately solved by establishing critic NN structure and then the approximated optimal control policy can be derived. The closed-loop manipulator system is proved to be asymptotic stable by using the Lyapunov theory. Finally, simulation results are provided to demonstrate the effectiveness and advantages of the proposed control method. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy logic nonzero-sum game-based distributed approximated optimal control scheme is presented for modular robot manipulators (MRMs) with human-robot collaboration (HRC) tasks. The MRM dynamic model is formulated by using joint torque feedback (JTF) technique. Based on the differential game strategy, the optimal control problem of HRC task-oriented MRM systems is transformed into a nonzero-sum game problem of multiple robotic subsystems. By taking advantage of the adaptive dynamic programming (ADP) algorithm, the distributed approximate optimal control policy under HRC tasks is developed by a novel fuzzy logic nonzero-sum game manner for solving the coupled Hamilton–Jacobian (HJ) equation. The trajectory tracking error under HRC task of the closed-loop MRM system is proved to be ultimately uniformly bounded (UUB) using the Lyapunov theory. Finally, experiment results have been presented, which demonstrate the advantage and effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

• Identification-critic control scheme address the issues of dynamic compensation. • Adaptive dynamic programming algorithm is based on critic only policy iteration. The zero-game game is implemented by the experimental platform. In this paper, a zero-sum differential game strategy-based neuro-optimal control method is presented via critic only policy iteration-adaptive dynamic programming (COPI-ADP) approach to address optimal trajectory tracking control problem of modular robot manipulators (MRMs) with uncertain disturbance. The dynamic model of modular robot manipulator systems is formulated as an integration of joint subsystems and unknown robotic model uncertainties are identified by the developed linear extension state observer. Then, the optimal control issue of the modular robot manipulator systems with uncertain disturbance is transformed into a two-player zero-sum differential game one. Based on adaptive dynamic programming and policy iteration algorithms, the Hamilton-Jacobi-Issacs (HJI) equation is approximately solved using only critic neural network and thus facilitating the feasible derivation of the approximated optimal control policy. The trajectory of tracking errors of modular robot manipulator system is guaranteed to be uniform ultimate bounded by using the Lyapunov theory. Finally, experiments are provided to demonstrate the advantage and effectiveness of the developed control method. [ABSTRACT FROM AUTHOR]