TY - JOUR

T1 - Observer-Based Adaptive Robust Actor–Critic Learning Saturated PID Controller for a Class of Euler–Lagrange Robotic Systems With Guaranteed Performance

T2 - Theory and Practice

AU - Elhaki, Omid

AU - Shojaei, Khoshnam

AU - Chatraei, Abbas

AU - Montazeri, Allahyar

PY - 2025/2/28

Y1 - 2025/2/28

N2 - This article addresses the output-feedback reinforcement learning (RL)-based saturated proportional-integral-derivative (PID) control design for fully actuated Euler–Lagrange (EL) systems which are uncertain subject to actuator saturation with prescribed performance. It is assumed that the actuator input nonlinearity, uncertain nonlinearities and unmeasurable external disturbances have a significant impact on the system. The presence of actuator saturation and complex uncertainties may inevitably give rise to the breakdown of the EL control system. The lack of prior knowledge of the system dynamics renders the presented technique to achieve a robust prescribed tracking performance without using velocity sensors. To conquer mentioned obstacles, a novel RL saturated PID controller, which is not dependent on the system’s dynamics and only requires measurable output signals is designed via actor–critic structure to deeply estimate and compensate complex unknowns. An adaptive robust controller is used to reduce external disturbances effects adaptively. The prescribed performance funnel control way is considered to guarantee predetermined output constraints. The high-gain observer (HGO) is used to estimate velocities and derivatives free of system dynamics, and generalized saturation functions are utilized to efficiently decrease actuator saturation danger. It is proved that suggested technique ensures a robust prescribed performance with input constraints in the absence of velocity sensors and the existence of considerable complicated model uncertainties. A semi-global uniform ultimate boundedness (SGUUB) stability for tracking deviation errors and state estimation deviation is ensured through a Lyapunov stability study. Finally, experimental results on a real robotic arm is carried out to further demonstrate the effectiveness of all theoretical findings.

AB - This article addresses the output-feedback reinforcement learning (RL)-based saturated proportional-integral-derivative (PID) control design for fully actuated Euler–Lagrange (EL) systems which are uncertain subject to actuator saturation with prescribed performance. It is assumed that the actuator input nonlinearity, uncertain nonlinearities and unmeasurable external disturbances have a significant impact on the system. The presence of actuator saturation and complex uncertainties may inevitably give rise to the breakdown of the EL control system. The lack of prior knowledge of the system dynamics renders the presented technique to achieve a robust prescribed tracking performance without using velocity sensors. To conquer mentioned obstacles, a novel RL saturated PID controller, which is not dependent on the system’s dynamics and only requires measurable output signals is designed via actor–critic structure to deeply estimate and compensate complex unknowns. An adaptive robust controller is used to reduce external disturbances effects adaptively. The prescribed performance funnel control way is considered to guarantee predetermined output constraints. The high-gain observer (HGO) is used to estimate velocities and derivatives free of system dynamics, and generalized saturation functions are utilized to efficiently decrease actuator saturation danger. It is proved that suggested technique ensures a robust prescribed performance with input constraints in the absence of velocity sensors and the existence of considerable complicated model uncertainties. A semi-global uniform ultimate boundedness (SGUUB) stability for tracking deviation errors and state estimation deviation is ensured through a Lyapunov stability study. Finally, experimental results on a real robotic arm is carried out to further demonstrate the effectiveness of all theoretical findings.

U2 - 10.1109/tsmc.2024.3506695

DO - 10.1109/tsmc.2024.3506695

M3 - Journal article

VL - 55

SP - 1400

EP - 1412

JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems

JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems

SN - 2168-2216

IS - 2

ER -