This paper focuses on the high performance pointing control of tank servo systems with parametric uncertainties and uncertain nonlinearities including nonlinear friction, backlash and structural flexibility. A comprehensive dynamic nonlinear mathematical model of the two-DOF tank servo system is established. Specifically, to accurately describe the nonlinear friction characteristics in actual systems, a continuous friction model is employed. Moreover, a hybrid nonlinear model combining structural flexibility and transmission backlash is constructed to characterize the nonlinear characteristics of the backlash and flexible coupling between the input and output shafts of the drive end for the tank servo system. By using the backstepping method, a nonlinear adaptive robust controller is presented. In the controller, the adaptive law is compounded to dispose of parametric uncertainties and a well-designed continuous nonlinear robust control law is developed for the purpose of coping with unmodeled disturbances. The closed-loop system stability analysis indicates that the presented controller achieves an asymptotic tracking performance with parametric uncertainties and ensures the robustness against unmodeled disturbances theoretically. The effectiveness of the proposed control strategy is verified by a large number of comparative simulation results.
Multiaxial hydraulic manipulators are complicated systems with highly nonlinear dynamics and various modeling uncertainties, which hinders the development of high‐performance controller. In this paper, a neural network feedforward with a robust integral of the sign of the error (RISE) feedback is proposed for high precise tracking control of hydraulic manipulator systems. The established nonlinear model takes three‐axis dynamic coupling, hydraulic actuator dynamics, and nonlinear friction effects into consideration. A radial basis function neural network (RBFNN) is synthesized to approximate the uncertain system dynamics and external disturbance, which can greatly reduce the dependence on accurate system model. In addition, a continuous RISE feedback law is judiciously integrated to deal with the residual unknown dynamics. Since the major unknown dynamics can be estimated by the RBFNN and then compensated in the feedforward design, the high‐gain feedback issue in RISE feedback control will be avoided. The proposed RISE‐based neural network robust controller theoretically guarantees an excellent semi‐global asymptotic stability. Comparative simulation is performed on a 3‐DOF hydraulic manipulator, and the obtained results verify the effectiveness of the proposed controller.
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