Design Of Adaptive Controller For A Class Of Uncertain Nonlinear Systems With Input Saturation

Saturation is one of the most common nonlinearities in many practical control systems, which is unavoidable in a majority of actuators. If an actuator reached an input limit, it is said to be"saturated", since further increasing the input would not result in any variation in the actuator output. The existence of actuator saturation can debase the system dynamic performance, lead the closed-loop systems to an unstable state, and have a strong impact on system normal operation. So, the study on input saturation has an important theoretical and practical significance. Combining neural networks and backstepping method, this paper designs three controllers for a class of uncertain nonlinear systems with input saturtion.For a class of uncertain nonlinear systems with Brunovsky canonical form and input saturation, an adaptive control algorithm based on neural networks together with a new saturation compensatory method is proposed. A controller is designed which consists of tracking controller and saturation compensator. Under proper model assumption, using radial basis function neural networks to approximate the unknown nonlinear function and the portion that exceeds saturation, at the same time considering the network reconstruction error and the system's external disturbance, the saturation compensator can compensate the influence of the input saturation nonlinearity effectively. The adaptive law is derived on the basis of Lyapunov function. The closed-loop system is semi-global uniformly ultimately bounded(SGUUB) which is proved by Lyapunov stability theory. A simulation example is provided to illustrate the validity of the proposed controller.An adaptive controller is designed for a class of uncertain nonlinear systems with input saturation. The saturation compensator of the controller is obtained by multilayer neural networks(MNNs). MNNs can approximate the nonlinear systems nicely. The weights of MNNs can be adjusted online in case of lacking system priori knowledge. The adaptive law is obtained from Lyapunov function. It can be proved that the designed adaptive controller can ensure the stability of the closed-loop system by Lyapunov stability theory.Through backstepping design procedure, a new adaptive controller is designed for a class of strict feedback nonlinear systems with input saturation which is composed of backstepping controller and robust controller. The adaptive law is got from Lyapunov function and Barbalat lemma. In order to settle the problem of actuator saturation, an input constraints error dynamic enlargement method is adopted. The simulation results show that the controller is robust to the systems with uncertain parameters and can guarantee the global stability of the closed-loop systems.