A DSC approach to adaptive dynamic region-based tracking control for strict-feedback non-linear systems

As an extension of the conventional set-point control problem, the dynamic region-based tracking control scheme with obstacle avoidance is proposed for a class of uncertain strict-feedback non-linear systems. A novel adaptive tracking controller is designed by a fusion of artificial potential field, recursive backstepping approach, neural networks, dynamic surface control technique, calculus method, and Lyapunov stability theory. In the proposed control scheme, the objective region cannot be required to have a regular shape or a fixed size for the passibility of the system in constrained space. The region tracking error is transformed into a new virtual error variable for recursively designing a dynamic surface controller, and the dimension of neural network inputs can be greatly reduced, especially for high-order systems. The Lyapunov theorem is used to confirm the stability and uniform boundedness of all closed-loop signals. Simulation results are provided to demonstrate the effectiveness of the proposed controller.