Neural Network Composite Adaptive Antidisturbance Control for a Class of Unknown Pure-feedback Switched Nonlinear Systems

作者：Chen, Longsheng^*; Ren, Yong

来源：INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2023, 21(8): 2726-2738.

DOI：10.1007/s12555-022-0387-y

• Average dwell time
• filtering method
• neural network composite switched disturbance observer
• neural network composite switched state observer
• pure-feedback switched nonlinear systems

In this study, a neural network (NN) composite adaptive antidisturbance control scheme is investigated for a class of unknown pure-feedback switched nonlinear systems. First, radial basis function NNs are employed to identify unknown nonlinearities by employing a Butterworth low-pass filter to eliminate the algebraic loop problem. Subsequently, an NN composite switched state observer and an NN composite switched disturbance observer are presented by coupled design to estimate immeasurable states and compounded disturbances. Next, an improved composite control strategy is developed for the investigated problem with the help of a filtering method to avoid the "explosion of complexity" problem, and compensating signals are set up to alleviate the filter errors. By utilizing the Lyapunov stability theorem, the proposed control scheme can guarantee that all signals in the closed-loop system are bounded under a class of switching signals with the average dwell time, while the tracking error can converge to within a small neighbourhood of the origin. Simulation results are provided to demonstrate the effectiveness of the presented approach.

• 单位
  南昌航空大学