PID Control for Robot Manipulators with Neural Compensation

In order to minimize steady-state error with respect to uncertainties in robot control, the integral gain of PID control should be increased. Another method is to add a compensator to PD control, such as neural compensator, but the derivative gain of this PD control should be large enough. These two approaches deteriorate transient performances. In this paper, the popular neural PD is extended to neural PID control. The semiglobal asymptotic stability of the neural PID control is proven. The conditions give explicit selection methods for the gains of the linear PID control. A experimental study on an upper limb exoskeleton with this neural PID control is addressed.