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RISS 인기검색어
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- 저자 : Xubin Ping (검색결과 3 건)
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Xubin Ping,Zhiwu Li,Abdulrahman Al-Ahmari 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.3
For linear parameter varying (LPV) systems with unknown scheduling parameters and bounded disturbance,a synthesis approach of dynamic output feedback robust model predictive control (OFRMPC) with inputsaturation is investigated. By pre-specifying partial controller parameters, a main optimization problem is solvedby convex optimization to reduce the on-line computational burden. The main optimization problem guarantees thatthe estimated state and estimation error converge within the corresponding invariant sets such that recursive feasibilityand robust stability are guaranteed. The consideration of input saturation in the main optimization problemimproves the control performance. Two numerical examples are given to illustrate the effectiveness of the approach.
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Xubin Ping,Peng Wang,Jia-Feng Zhang 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.5
This paper considers a multi-step output feedback robust model predictive control (OFRMPC) approach for the linear parameter varying (LPV) systems with bounded changes of scheduling parameters and bounded disturbance. Less conservative bounds of future estimation error sets and system parametric uncertain sets are predicted by considering bounded changes of scheduling parameters in LPV systems. In the multi-step OFRMPC approach, an optimization problem is solved to obtain a sequence of controller gains, which considers predictions of future bounds of estimation error sets and system parametric uncertain sets. The optimized sequence of controller gains corresponding to a sequence of Lyaponov matrices have less constraint conditions and also introduce more degree of freedom for the optimization. The proposed multi-step OFRMPC guarantees robust uniform ultimately bounded of the estimation error and robust stability of the observer system. A numerical example is given to demonstrate the effectiveness of the approach.
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A Convexity Approach to Dynamic Output Feedback Robust MPC for LPV Systems with Bounded Disturbances
Xubin Ping,Sen Yang,Baocang Ding,Tarek Raïssi,Zhiwu Li 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.6
A convexity approach to dynamic output feedback robust model predictive control (OFRMPC) is proposed for linear parameter varying (LPV) systems with bounded disturbances. At each sampling time, the model parameters and disturbances are assumed to be unknown but bounded within pre-specified convex sets. Robust stability conditions on the augmented closed-loop system are derived using the techniques of robust positively invariant (RPI) set and the S-procedure. A convexity method reformulates the non-convex bilinear matrix inequalities (BMIs) problem as a convex optimization one such that the on-line computational burden is significantly reduced. The on-line optimized dynamic output feedback controller parameters steer the augmented states to converge within RPI sets and recursive feasibility of the optimization problem is guaranteed. Furthermore, bounds of the estimation error set are refreshed by updating the shape matrix of the future ellipsoidal estimation error set. The dynamic OFRMPC approach guarantees that the disturbance-free augmented closed-loop system (without consideration ofdisturbances) converges to the origin. In addition, when the system is subject to bounded disturbances, the augmented closed-loop system converges to a neighborhood of the origin. Two simulation examples are given to verify the effectiveness of the approach.
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