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To ensure the authority of sliding mode control, model parameter uncertainties of AMB systems are analysed and reachability to sliding surface has been verified.
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In this paper a sliding mode predictive control (SMPC) is obtained by combining the design technique of a sliding mode control with a model based predictive control (MPC).
This paper focuses on robust autopilot design for bank-to-turn (BTT) missiles by using adaptive dual-layer sliding mode control (ADSMC) under model uncertainties and external disturbances.
The algorithm combines the design technique of sliding mode control (SMC) with model-based predictive control (MPC).
A technique based on sliding mode control, called sliding generic model control, is developed here to take into account the process uncertainties and to keep the system close to the GMC performance surface.
Finally, this linear model is employed for determining a sliding mode control for tracking a reference model.
To attenuate the multifrequency gearbox vibrations and resulting noise, a new scheme of discrete model predictive sliding mode control has been proposed based on controlled auto-regressive moving average model.
The proposed WMRC utilizes sliding mode control to cope with the model imprecision of a conventional reduced first-order HVDC model and selects the area center of inertia (COI) signals as the feedback signals.
The proposed scheme combines the advantages of the adaptive control, neural network and sliding mode control strategies without precise system model information.
Sequently, based on the established mathematical model, sliding mode control laws are designed to ensure the asymptotical stability of the whole dynamical system.
Since the hydraulic actuating suspension system has nonlinear and time-varying behavior, it is difficult to establish an accurate dynamic model for a model-based sliding mode control design.
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