LAUSR

Traditional disturbance observer techniques have limitations for multi-input multi-output-coupled systems, time-varying systems with large parameters, and complex systems which are difficult to obtain accurate model, such as the human musculoskeletal arm system. The neural network has advantages of generalized approximation ability, learning ability, and self-adaptive ability. Therefore, this paper develops an adaptive neural fuzzy inference system disturbance observer-based control to achieve the point-to-point control and the path tracking control of the end point of the musculoskeletal arm model. The adaptivity is improved by adjusting the learning algorithm of neural network parameters and the network structure in real time. The uncertainty of the inverse model of the system is solved by constructing a pseudo-system. The adaptive neural fuzzy inference system is used to identify the inverse model of the pseudo-system and to design a compound controller based on feedback control method. The stability is analyzed by Lyapunov function in detail. Furthermore, internal disturbances are suppressed by the learning algorithm of the adaptive neural fuzzy inference system network while external disturbances may be estimated by the multi-input multi-output disturbance observer at the same time. Simulations are performed to verify the point-to-point control and the path tracking control. Results demonstrate that both of the adaptivity and the accuracy are enhanced so that the system can achieve accurate tracking control of any arbitrary trajectory.