Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

Abstract EN

An adaptive neural network control problem is addressed for a class of nonlinear hydraulic servo-systems with time-varying state constraints.

In view of the low precision problem of the traditional hydraulic servo-system which is caused by the tracking errors surpassing appropriate bound, the previous works have shown that the constraint for the system is a good way to solve the low precision problem.

Meanwhile, compared with constant constraints, the time-varying state constraints are more general in the actual systems.

Therefore, when the states of the system are forced to obey bounded time-varying constraint conditions, the high precision tracking performance of the system can be easily realized.

In order to achieve this goal, the time-varying barrier Lyapunov function (TVBLF) is used to prevent the states from violating time-varying constraints.

By the backstepping design, the adaptive controller will be obtained.

A radial basis function neural network (RBFNN) is used to estimate the uncertainties.

Based on analyzing the stability of the hydraulic servo-system, we show that the error signals are bounded in the compacts sets; the time-varying state constrains are never violated and all singles of the hydraulic servo-system are bounded.

The simulation and experimental results show that the tracking accuracy of system is improved and the controller has fast tracking ability and strong robustness.