Iterative Learning Tracking Control for a Vehicle-Manipulator System With Input Constraint

In this study, tracking control and saturation nonlinearities problems of a flexible mobile vehicle-manipulator with disturbance uncertainty are investigated. Based on differential equations model, a novel iterative learning control (ILC) algorithm is designed, where two saturation functions and two hyperbolic tangent functions are developed to deal with actuator saturation constraint and an adaptive law is developed to reduce the impact of disturbance uncertainty. With the ILC algorithm, the target of tracking control and vibration attenuation of mobile manipulator can come true. Simulations are executed to verify the effectiveness of designed algorithm.