A novel adaptive switching function on fault tolerable sliding mode control for uncertain stochastic systems

Area of Science:

• Control Systems Engineering
• Stochastic Systems Analysis
• Robotics and Automation

Background:

• Traditional sliding mode control (SMC) often relies on proportional or proportional-integral state functions for sliding surfaces.
• Actuator degradation and system uncertainties pose significant challenges in achieving robust control.
• Existing methods may lack flexibility in adapting to specific performance objectives.

Purpose of the Study:

• To develop a novel switching function for sliding mode control (SMC) in uncertain stochastic systems.
• To address challenges posed by actuator degradation while ensuring global asymptotic stability.
• To provide designers with a tunable parameter for achieving specific control objectives.

Main Methods:

• A novel switching function based on an optimization strategy is proposed for SMC.
• The switching function incorporates a designer-adjustable parameter for tailored control.
• A sliding-mode controller is synthesized to guarantee the reachability of the switching surface.
• The closed-loop system is designed for global asymptotic stability with probability one.

Main Results:

• The proposed method ensures global asymptotic stability for the closed-loop system with probability one.
• The controller effectively handles actuator degradation and system uncertainties.
• Simulation results validate the superior performance and robustness of the developed SMC strategy.
• The designer-tunable parameter allows for optimization of system objectives.

Conclusions:

• The novel optimization-based switching function enhances the robustness of SMC for uncertain stochastic systems with actuator degradation.
• The proposed control strategy guarantees system stability and offers design flexibility.
• This research provides an effective solution for complex control problems in dynamic environments.