Online neuroadaptive control of a rotary crane system

This paper is concerned with the control of a rotary crane system which is perturbed by a strong and sudden disturbance. Since the payload of the crane system is affected strongly by inertia, it is hardly stabilized quickly, particularly when there exists disturbance. An adaptive adjustment of the controller against the disturbance is thus needed to maintain the desired performance. The problem becomes more challenging when using evolutionary algorithms based techniques as they are usually computationally demanding. In this study, an online control method using neural network (NN) and genetic algorithm (GA) is proposed where a state is predicted and then used as a new initial condition for GA to perform re-designing the controller. Simulations show that the method works effectively to regulate the perturbed system to the desired state.