具有执行器故障的非线性系统指定性能控制

Prescribed Performance Control for a Class of Nonlinear Purefeedback Systems with Actuator Faults

针对一类带有全状态约束和执行器故障的非线性系统,提出一种具有指定性能的自适应神经网络输出反馈容错控制方案。首先,建立状态观测器估计系统中的不可测状态,利用径向基神经网络(RBF NNs)逼近系统中的未知非线性函数。其次,引入非线性映射将状态约束系统转化为一个没有约束的新系统。然后,采用新的性能函数,不仅能使跟踪误差在预先设定的时间内收敛,还可以利用设计参数改变误差的收敛速度。最后证明,该控制方法能够保证闭环系统中所有信号都是半全局一致最终有界的,并通过一个数值仿真验证该方法的有效性。

In this paper,considering a class of nonlinear systems with full state constraints and actuator faults,an adaptive neural network output feedback fault-tolerant control algorithm with prescribed performance is proposed.A state observer is constructed to solve the unmeasurable states problem.Unknown nonlinear functions in the systems are approximated by radial basis function neural networks(RBF NNs).By introducing the nonlinear mapping,the systems with state constraints are transformed into novel systems without state constraints.Moreover,a novel performance function is utilized to guarantee that the tracking error converges within a preset time.Meanwhile,the convergence speed can be adjusted through the parameter design.Finally,it is proved that the control algorithm ensures that all signals in the closed-loop systems are semi-globally uniformly ultimately bounded.The effectiveness of the algorithm is verified by a numerical simulation.