For practical plants, there are not only inevitable endogenous and exogenous disturbances, but also constraint demands for control input amplitude, output performance, and system states. The existing controllers are difficult to handle the aforementioned control issues simultaneously and may be affected by “explosion of complexity.” In this work, we will develop a neuroadaptive learning algorithm for constrained nonlinear systems with disturbance rejection. In detail, the performance prescribed function and time‐varying barrier Lyapunov functions will be constructed to achieve the prescribed output performance and time‐varying state constraints, respectively. Furthermore, the neural network adaptive control and the extended state observers will be combined to respectively estimate the endogenous uncertainties and exogenous disturbances on‐line and meanwhile compensate for them feedforwardly. In particular, a filter will be introduced to estimate the virtual control law at each procedure, and this filtered value will replace its actual value in synthesizing the controller. In addition, the filtering errors and input saturation nonlinearity will be compensated by introducing an auxiliary system. Eventually, the whole closed‐loop stability is strictly guaranteed and the achievable control performance is verified by the application results.
This technical note focus on the tracking control problem of uncertain nonlinear systems with multiple states constraints. Based on the Barrier Lyapunov function and backstepping technology, a new continuous smooth control solution can be ultimately synthesized to realize asymptotic tracking control in presence of multiple states constraints and modeling uncertainties. Firstly, the modeling uncertainties are divided into periodic and un-periodic components and Fourier expansion technology is employed to translate the periodic disturbance into the form which can be easily compensated. Then the Barrier Lyapunov function are flexibly utilized to design the virtual control law of every step and the final controller, which can guarantee the specified states within certain bounds regardless of the amplitude of system output. Meanwhile, a novel nonlinear control technology is introduced to each design step to realize the final asymptotic tracking control despite the matched and mismatched uncertainties. By analyzing the choice of the control parameters, the backstepping cross-term is skillfully dealt with and the stability of the whole system is proved rigorously. Finally, the simulation results on a three-order nonlinear hydraulic system demonstrate the satisfactory performance of the proposed control method.
This paper focuses on the problem of tracking control of a chain of integrator nonlinear systems with input constraint and hysteresis nonlinearity. Input constraint, always existing in physical systems, has been proved a source of performance degradation. To handle this issue, an effective hyperbolic saturation function is employed, which is bounded no matter how the disturbances and error signals change. Furthermore, hysteresis nonlinearity, which may also limit the system performance, is modelled as a combination of a linear term with constant slope and a bounded disturbance term, which makes it possible to be integrated in the model based controller design. The robust integral of the sign of error (RISE) control is synthesized to guarantee the asymptotic tracking performance in the presence of parametric uncertainties and unmodelled nonlinearities such as external disturbances and unmodelled hysteresis nonlinearity. The closed-loop stability is proved via Lyapunov analysis. Some simulations are carried out to verify the effectiveness of the proposed controller.
This article mainly concerns the high-performance motion control of valve-controlled hydraulic actuators with input saturation and modelling uncertainties. The nonlinear mathematic model including a continuously differentiable static friction model is constructed, and then adaptive robust design framework is adopted to cope with the modelling uncertainties, which always impede the progress of high-performance motion controller. Input saturation, which frequently exists in most physical systems, has been found to be prone to performance decay. To address this specific issue, an embedded anti-windup block containing two adjusting mechanisms is properly designed to improve the motion controller to ensure the stability and performance preservation in circumstance of input saturation, which is proved via rigorous Lyapunov analysis. Typical simulation is implemented to illustrate the availability of the proposed control method.
This research focuses on motion control of hydraulic servo-mechanism and presents a novel quasi-adaptive sliding mode control algorithm with barrier function-based control gain. The mathematical model of the system is established in integral series format to contribute to the controller design. The utilized sliding mode control gain is designed to be adapted with the change of design error related to tracking error. It can first increase until the design error reaches to a small domain at a designed time by utilizing constant gain. And then the control gain will automatically switch to barrier function form to hold design error within a predefined domain un-depending on the modeling uncertainties theoretically. Correspondingly, the tracking error will converge to a small domain. The system stability is proved via Lyapunov analysis. By comparing to three classic controllers with motion tracking experiments, the achievable higher tracking accuracy of the proposed new control law are validated sufficiently.
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