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Self-learning sliding mode control based on adaptive dynamic programming for nonholonomic mobile robots.

Qingwen Ma1, Xinglong Zhang1, Xin Xu1

  • 1College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China.

ISA Transactions
|August 20, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a self-learning sliding mode control (SlSMC) for nonholonomic mobile robots (NMRs). The novel approach enhances trajectory tracking performance and ensures stability, even with uncertainties and disturbances.

Keywords:
Adaptive dynamic programming (ADP)Nonholonomic mobile robots (NMRs)Receding horizonSliding mode control (SMC)Trajectory tracking control

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Nonholonomic mobile robots (NMRs) face challenges in trajectory tracking due to uncertainties like skidding, slipping, and environmental noise.
  • Traditional sliding mode control (SMC) offers robustness but can be further optimized for enhanced performance.

Purpose of the Study:

  • To develop a self-learning sliding mode control (SlSMC) strategy with guaranteed stability for NMR trajectory tracking.
  • To improve control performance by optimizing the reaching law and sliding mode surface of SMC.
  • To ensure finite-time convergence and robustness against matched uncertainties.

Main Methods:

  • Reconstruction of the NMR kinematic model considering adverse factors.
  • Design of an integral terminal sliding mode controller.
  • Formulation of optimization problems for reaching law and sliding mode surface under stability constraints.
  • Implementation of an online continuous-time receding-horizon optimization mechanism using an actor-critic algorithm for asynchronous problem-solving.

Main Results:

  • Theoretical and simulation-based validation of the proposed SlSMC strategy's stability and convergence.
  • Demonstration of superior control performance compared to three recent methods through extensive simulations.
  • Experimental validation on a real intelligent vehicle confirming the method's practical applicability for NMRs.

Conclusions:

  • The proposed self-learning SMC strategy effectively enhances trajectory tracking for NMRs.
  • The receding horizon learning-based approach offers significant improvements in control performance and robustness.
  • The method is suitable for real-world applications, meeting practical control needs of intelligent vehicles.