scholarly journals Sliding mode-based online fault compensation control for modular reconfigurable robots through adaptive dynamic programming

2021 ◽  
Author(s):  
Hongbing Xia ◽  
Ping Guo
Keyword(s):  
Dynamic Programming ◽  
Sliding Mode ◽  
Adaptive Dynamic Programming ◽  
Fault Detection And Isolation ◽  
Iteration Algorithm ◽  
Adaptive Dynamic ◽  
Compensation Control ◽  
Reconfigurable Robots ◽  
Control Scheme ◽  
Fault Compensation

AbstractIn this paper, a sliding mode (SM)-based online fault compensation control scheme is investigated for modular reconfigurable robots (MRRs) with actuator failures via adaptive dynamic programming. It consists of a SM-based iterative controller, an adaptive robust term and an online fault compensator. For fault-free MRR systems, the SM surface-based Hamilton–Jacobi–Bellman equation is solved by online policy iteration algorithm. The adaptive robust term is added to guarantee the reachable condition of SM surface. For faulty MRR systems, the actuator failure is compensated online to avoid the fault detection and isolation mechanism. The closed-loop MRR system is guaranteed to be asymptotically stable under the developed fault compensation control scheme. Simulation results verify the effectiveness of the present fault compensation control approach.

scholarly journals Model-free optimal decentralized sliding mode control for modular and reconfigurable robots based on adaptive dynamic programming

2019 ◽  
Vol 11 (12) ◽  
pp. 168781401989692 ◽  
Author(s):  
Bo Dong ◽  
Tianjiao An ◽  
Fan Zhou ◽  
Weibo Yu
Keyword(s):  
Dynamic Programming ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Dynamic Programming Algorithm ◽  
Adaptive Dynamic Programming ◽  
Programming Algorithm ◽  
Adaptive Dynamic ◽  
Mode Control ◽  
Model Free ◽  
Reconfigurable Robots

In this article, a model-free decentralized sliding mode control method is proposed based on adaptive dynamic programming algorithm to solve the problem of optimal trajectory tracking control of modular and reconfigurable robots. The dynamic model of modular and reconfigurable robot is formulated by a synthesis of joint subsystems with interconnected dynamic couplings. Based on sliding mode control technique, the optimal control problem of the modular and reconfigurable robot systems is transformed into an optimal compensation issue of unknown dynamics of each joint subsystems, in which the interconnected dynamic couplings effects among the subsystems are approximated by using the developed neural network identifier. Based on policy iteration scheme and the adaptive dynamic programming algorithm, the Hamilton–Jacobi–Bellman equation can be solved by using the critic neural network, so that optimal control policy can be obtained. The closed-loop system is proved to be asymptotically stable by using the Lyapunov theory. Finally, simulation results are provided to demonstrate the effectiveness of the method.

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