Adaptive Dynamic Programming-Based Sliding Mode Optimal Position-Force Control for Reconfigurable Manipulators with Uncertain Disturbance

Zhu, Xinye; Ma, Bing; Dong, Bo; Liu, Keping

doi:10.1109/ccdc49329.2020.9164058

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…Based on different simulation analysis, we select α i F as 0.9. Figures 13,14,15,16,17,18,19,20,21 and 22 are position tracking curves, position error, velocity curves, velocity error, control torque and contact force, force error curves for configuration B. The results are similar with configuration A.…”

Section: Simulation Resultsmentioning

confidence: 58%

“…Optimal control method developed 6 decades ago by Pontryagin [6], Bellman [7], and many papers have been reported [8][9][10][11]. Recently, adaptive dynamic programming (ADP) have been used in discrete-time systems [12][13][14], continuous-time systems [15][16][17], data driven systems [18][19][20], input/output constraints [21,22], uncertain external disturbance [23][24][25] as well as actuator failures [26], etc. Some investigations reported ADP-based manipulator systems.…”

Section: Introductionmentioning

confidence: 99%

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Critic-observer-based decentralized force/position approximate optimal control for modular and reconfigurable manipulators with uncertain environmental constraints

Zhu

et al. 2021

Complex Intell. Syst.

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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.

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Section: Simulation Resultsmentioning

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Critic-observer-based decentralized force/position approximate optimal control for modular and reconfigurable manipulators with uncertain environmental constraints

Zhu

et al. 2021

Complex Intell. Syst.

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“…Optimal control method has been proposed nearly 70 years ago (Bellman and Dreyfus, 2013; Pontryagin, 1959), and plenty of literatures are recorded (An et al, 2021; An et al, 2021; Wei et al, 2021). Adaptive dynamic programming (ADP) theory is a key direction to undertake approximate optimal control system issues of discrete-time (Hu et al, 2021; Luo et al, 2020; Zhu et al, 2020a), continuous-time (An et al, 2021; Shan et al, 2020; Wei et al, 2020), data driven (Gao et al, 2018; Li et al, 2020; Su et al, 2020), and furthermore robot systems with input/output constraints (An et al, 2020; Lu et al, 2020; Ren et al, 2019), external disturbance (An et al, 2019; Song and Lewis, 2020; Xia et al, 2020), actuator failures (Dai et al, 2018; Jiao et al, 2018; Ma et al, 2020), and so on. Kong et al (2021b) developed n -link manipulator approximate optimal law via saturation nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

“…In order to improve the robustness, a neuro-optimal method is proposed in Kong et al (2021). Zhu et al (2020) developed position–force optimal control of manipulator under uncertain disturbance.…”

Section: Introductionmentioning

confidence: 99%

Decentralized position–force zero-sum approximate optimal control for reconfigurable robots with unmodeled dynamic

Zhu

Wang

2022

Transactions of the Institute of Measurement and Control

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In this paper, the position–force–based approximate optimal control method is developed for reconfigurable robots using zero-sum game strategy. By utilizing the Newton–Euler iteration technique, the robotic system’s dynamic model is formulated and the state space equation is derived. According to adaptive dynamic programming (ADP) and neural network algorithm, the trajectory tracking control problem is transformed into a zero-sum game-based optimal control issue. The optimal control policy and worst disturbance policy are obtained by Hamilton–Jacobi–Issacs (HJI) function, respectively. Unlike the conventional learning–based robotic control method, the proposed zero-sum game-based method no need extra sub-controller that can reduce the computational burden. The reconfigurable robot system’s tracking error is uniformly ultimately bounded by the Lyapunov theorem. Finally, simulation experiments demonstrate the advantages of the proposed method.

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Design of intelligent optimal controller for hybrid position/force control of constrained reconfigurable manipulators

Rani

Kumar

2022

J Ambient Intell Human Comput

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Adaptive Dynamic Programming-Based Sliding Mode Optimal Position-Force Control for Reconfigurable Manipulators with Uncertain Disturbance

Cited by 5 publications

References 12 publications

Critic-observer-based decentralized force/position approximate optimal control for modular and reconfigurable manipulators with uncertain environmental constraints

Critic-observer-based decentralized force/position approximate optimal control for modular and reconfigurable manipulators with uncertain environmental constraints

Decentralized position–force zero-sum approximate optimal control for reconfigurable robots with unmodeled dynamic

Design of intelligent optimal controller for hybrid position/force control of constrained reconfigurable manipulators

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