Gene

Quantum mechanics

Adaptive control

Control (management)

Engineering

Robust control

Control system

Control engineering

Robustness (evolution)

Biochemistry

Computer science

Terminal sliding mode

Physics

Nonlinear system

Sliding mode control

Artificial intelligence

Chemistry

Control theory (sociology)

Electrical engineering

IEEE Transactions on Cybernetics

In this article, a robust adaptive fixed-time sliding-mode control method is proposed for robotic systems with parameter uncertainties and input saturation. First, a model-based fixed-time controller is designed under the premise that the system parameters are known. Moreover, the unknown dynamics of robotic systems and the boundary of compounded disturbance are synthesized into a compounded uncertainty. Then, the Gaussian radial basis function neural networks (NNs) are selected to approximate the compounded uncertainty. In addition, the nonsingular fast terminal sliding-mode (NFTSM) control is incorporated into the proposed fixed-time control framework to enhance the robustness and convergence speed of unknown robotic systems. Finally, a comparative simulation based on a rigid manipulator shows the superiority and efficacy of the designed methods.

Robust Adaptive Fixed-Time Sliding-Mode Control for Uncertain Robotic Systems With Input Saturation