Multiapproximator-Based Fault-Tolerant Tracking Control for Unmanned Autonomous Helicopter With Input Saturation
针对中规模无人自主直升机，提出一种自适应神经容错控制方案，处理外部干扰、执行器故障和输入饱和，通过多逼近器设计保证系统信号有界，仿真验证了有效性。
In this article, an adaptive neural fault-tolerant control (FTC) scheme is proposed for the medium-scale unmanned autonomous helicopter subject to external disturbance, actuator fault, and input saturation. Multiple approximators are constructed to handle the unknown terms and promote the control design. The nonlinear coupled function terms are approximated by virtue of the radial basis function neural networks. The unknown disturbance is tackled by the developed disturbance observer. Meanwhile, two auxiliary systems are introduced to handle the actuator fault and input saturation, respectively. In the framework of the backstepping method, a multiapproximator-based adaptive FTC strategy is presented, which assures the boundedness of all closed-loop system signals. Simulation results are presented to validate the availability of the designed controller.