Adaptive Optimal Control for Prescribed Constraints 
Robotic Manipulators Based On Approximate Dynamic 
Programming

Xiang Wang; Zhinan Peng

doi:10.70711/aitr.v2i4.4868

Adaptive Optimal Control for Prescribed Constraints Robotic Manipulators Based On Approximate Dynamic Programming

Xiang Wang, Zhinan Peng

Abstract

In this paper, an approximate dynamic programming (ADP) approach integrated with concurrent learning is proposed to cope with
the optimal control problem in the 2-Degree-of-Freedom (2-DOF) Robot Manipulator (RM) dynamic system. Prescribed constraints are introduced from realistic applications and bound the system states for safe operation. Using a space transformation system, the constrained state
space is mapped onto an unconstrained one, facilitating the control design process. Moreover, the online ADP algorithm with critic neural
network is implemented in the transformed RM system to find the optimal control strategy, with an auxiliary term introduced to ensure initial
admissible control requirements. By using the concurrent learning method, the persistence of excitation condition is relaxed and the control
performance is enhanced. Finally, simulations of the 2-DOF RM are conducted and the simulation results validate the feasibility of the refined
ADP control algorithm in optimal control of RM systems.

Keywords

Robot Manipulator; Development of robot; Approximate Dynamic Programming

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DOI: http://dx.doi.org/10.70711/aitr.v2i4.4868

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