Approximation-Based Adaptive Neural Tracking Control of Nonlinear MIMO Unknown Time-Varying Delay Systems With Full
IEEE Transactions on Cybernetics
|June 15, 2017
Summary
This study introduces a novel adaptive control method for nonlinear systems with unknown time-varying delays and state constraints. The approach ensures tracking performance while maintaining state constraints and signal boundedness.
Area of Science:
- Control Theory
- Nonlinear Systems Engineering
- Systems Dynamics
Background:
- Nonlinear multiple input multiple output (MIMO) systems with unknown time-varying delays and full state constraints present significant control challenges.
- Existing control methods often struggle to simultaneously address both unknown delays and state constraints.
Purpose of the Study:
- To develop a novel adaptive control method for nonlinear MIMO systems with unknown time-varying delays and full state constraints.
- To ensure tracking control performance while maintaining all system states within predefined constraints.
Main Methods:
- An adaptive control strategy is proposed, incorporating Lyapunov-Krasovskii functions and a separation technique to manage unknown time-varying delays.
- Barrier Lyapunov functions are utilized to enforce full state constraints, preventing constraint violation.
- A signal function is introduced to effectively handle potential singular problems within the control design.
Main Results:
- The proposed control method guarantees satisfactory tracking performance for the system's output.
- All system states are proven to remain within the specified constrained intervals throughout the control process.
- All closed-loop signals are demonstrated to be bounded, ensuring system stability.
Conclusions:
- The presented adaptive control technique offers a robust solution for a challenging class of nonlinear systems.
- The method effectively overcomes the complexities introduced by simultaneous unknown time-varying delays and full state constraints.
- Simulation results validate the practical applicability and effectiveness of the proposed control strategy.
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