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- 저자 : Baocang Ding (검색결과 3 건)
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An Iterative Optimization Approach for Fuzzy Predictive Control
Yuanqing Yang,Baocang Ding 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.8
This paper proposes an iterative approach in fuzzy model predictive control. When the prediction modelis nonlinear or uncertain, non-convex optimization is often encountered which has to be solved by iterative approximation. An alternative is to convert the original issue into a min-max robust MPC problem, where the knowledge of the predictive membership function is not utilized. In this paper, based on the robust MPC approach, we further enhance the model prediction by iteratively applying the optimal control move and state sequences in order to improve the performance. A numerical example is provided to illustrate the effectiveness of the proposed 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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Event-triggered Synchronous Distributed Model Predictive Control for Multi-agent Systems
Xiaoming Tang,Mengyue Li,Shanbi Wei,Baocang Ding 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.3
An event-triggered distributed model predictive control (DMPC) approach for a type of dynamically decoupled, independently constrained systems with a coupled performance objective, is presented. The approach employs, for each agent, a compatibility constraint (in the spirit of Dunbar and Murray) in the optimization problem. An event-triggering condition, based-on the overall stability condition of the system, is developed. If the triggering condition for an agent is satisfied, then the agent solves its optimization problem; otherwise, then the agent retainfeasibility and stability by simply adopting the tail of its previous solutions. A simulation example is provided to illustrate the effectiveness of the provided approach.
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