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- Title
Neural Adaptive Control for a Class of Uncertain Switched Nonlinear Systems with Input and Output Constraints.
- Authors
Lei Zhou; Lidong Wang; Yonghui Yang
- Abstract
A neural network adaptive control scheme for a class of uncertain switched nonlinear systems with input and output constraints is presented in this paper. A smooth function is adopted to approximate the input dead zone and saturation function, which can be used to solve the non differentiable phenomenon in system equations. Radial basis function (RBF) based neural networks are introduced to estimate the nonlinear functions of the system and the barrier Lyapunov function is selected to solve the problem of system output constraint, based on which a neural adaptive controller is designed by backstepping technique. Under the condition that the switching system meets a certain average dwell time, the designed controller can ensure that all signals in the closed-loop system are bounded and the tracking error of the system can converge to a compact set. The effectiveness of the designed controller is verified by the simulation example.
- Subjects
ADAPTIVE control systems; NONLINEAR systems; NONHOLONOMIC dynamical systems; BACKSTEPPING control method; RADIAL basis functions; SMOOTHNESS of functions; CLOSED loop systems
- Publication
IAENG International Journal of Computer Science, 2023, Vol 50, Issue 4, p1420
- ISSN
1819-656X
- Publication type
Article