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输入状态稳定的鲁棒预测控制 总被引:1,自引:0,他引:1
以有界干扰非线性系统为研究对象,设计一种基于近似可达集的鲁棒预测控制方法。该方法以鲁棒控制不变集作为终端约束集,采用一种简单的三次多项式逼近预测控制的待优化控制律,通过在线优化求解三次多项式的各项系数,并从理论上证明了所设计的鲁棒预测控制律可以使系统输入状态稳定。最后通过仿真实例验证了所提出的鲁棒预测控制方法的可行性和有效性。 相似文献
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《Journal of Process Control》1999,9(4):271-278
In this paper, we show that a constrained Model Predictive Controller, based on one plant model, stabilizes another plant if and only if some corresponding constrained Model Predictive Controller, based on the plant, stabilizes the plant. Thus strong nominal stability results can be used to analyze robust stability properties of some existing Model Predictive Control algorithms, without introducing any additional on-line computations and without modifying any of their attractive features. How the results may be used to synthesize robust controllers is also discussed. Examples are shown to illustrate the key ideas behind the approach. 相似文献
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大型复杂化工程过程控制中,常规的集中式控制方式不利于实时性、灵活性和容错控制。而采用多预测控制器协调的分布式控制是解决这一问题的有效方法:。针对子系统间的动态耦合行为严重影响多预测控制器协调以及稳定性的问题,提出一种鲁棒区域控制策略。即在每个子系统的目标函数中加入松弛因子增加控制器间协调时的余量来达到分布式预测控制的稳定性。通过以反应器-存储器分馏器组成的过程为事例,仿真结果:表明该方法:的可行性和有效性。 相似文献
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This paper presents a formulation for distributed model predictive control (DMPC) of systems with coupled constraints. The approach divides the single large planning optimization into smaller sub-problems, each planning only for the controls of a particular subsystem. Relevant plan data is communicated between sub-problems to ensure that all decisions satisfy the coupled constraints. The new algorithm guarantees that all optimizations remain feasible, that the coupled constraints will be satisfied, and that each subsystem will converge to its target, despite the action of unknown but bounded disturbances. Simulation results are presented showing that the new algorithm offers significant reductions in computation time for only a small degradation in performance in comparison with centralized MPC. 相似文献
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This paper presents a novel decentralised model predictive control for a plant consisting of interconnected systems. A constructive technique for online stabilisation that is applicable to the model predictive controllers (MPC) is developed. The plant-wise stability is achievable by the newly introduced asymptotically positive realness constraint (APRC) for MPC. Simulations are provided to demonstrate the efficiency of the presented APRC. 相似文献
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Effective optimization for fuzzy model predictive control 总被引:4,自引:0,他引:4
Mollov S. Babuska R. Abonyi J. Verbruggen H.B. 《Fuzzy Systems, IEEE Transactions on》2004,12(5):661-675
This paper addresses the optimization in fuzzy model predictive control. When the prediction model is a nonlinear fuzzy model, nonconvex, time-consuming optimization is necessary, with no guarantee of finding an optimal solution. A possible way around this problem is to linearize the fuzzy model at the current operating point and use linear predictive control (i.e., quadratic programming). For long-range predictive control, however, the influence of the linearization error may significantly deteriorate the performance. In our approach, this is remedied by linearizing the fuzzy model along the predicted input and output trajectories. One can further improve the model prediction by iteratively applying the optimized control sequence to the fuzzy model and linearizing along the so obtained simulated trajectories. Four different methods for the construction of the optimization problem are proposed, making difference between the cases when a single linear model or a set of linear models are used. By choosing an appropriate method, the user can achieve a desired tradeoff between the control performance and the computational load. The proposed techniques have been tested and evaluated using two simulated industrial benchmarks: pH control in a continuous stirred tank reactor and a high-purity distillation column. 相似文献
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针对一类具有范数有界时变参数不确定性的离散非线性系统,研究其具有圆盘极点约束的鲁棒模糊控制问题.采用T—S模糊模型逼近实际的离散非线性系统,结合并行分布补偿法和二次型性能指标,导出了保证闭环系统鲁棒稳定且所有极点配置在预先指定圆盘中的二次圆域稳定保性能控制器的存在条件,并将最优保性能控制器的设计问题可归结为求解一个线性矩阵不等式的凸优化问题.最后的仿真结果验证了设计方法的有效性. 相似文献
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Robust model predictive control using tubes 总被引:1,自引:0,他引:1
W. Langson Author Vitae Author Vitae S.V. Rakovi? Author Vitae Author Vitae 《Automatica》2004,40(1):125-133
A form of feedback model predictive control (MPC) that overcomes disadvantages of conventional MPC but which has manageable computational complexity is presented. The optimal control problem, solved on-line, yields a ‘tube’ and an associated piecewise affine control law that maintains the controlled trajectories in the tube despite uncertainty; computational complexity is linear (rather than exponential) in horizon length. Asymptotic stability of the controlled system is established. 相似文献
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This work deals with state estimation and process control for nonlinear systems, especially when nonlinear model predictive control (NMPC) is integrated with extended Kalman filter (EKF) as the state estimator. In particular, we focus on the robust stability of NMPC and EKF in the presence of plant-model mismatch. The convergence property of the estimation error from the EKF in the presence of non-vanishing perturbations is established based on our previous work [1]. In addition, a so-called one way interaction is shown that the EKF error is not influenced by control action from the NMPC. Hence, the EKF analysis is still valid in the output-feedback NMPC framework, even though there is no separation principle for general nonlinear systems. With this result, we study the robust stability of the output-feedback NMPC under the impact of the estimation error. It turns out the output-feedback NMPC with EKF is Input-to-State practical Stable (ISpS). Finally, two offset-free strategies of output-feedback NMPC are presented and illustrated through a simulation example. 相似文献
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Robust fuzzy control for a plant with fuzzy linear model 总被引:5,自引:0,他引:5
A robust complexity reduced proportional-integral-derivative (PID)-like fuzzy controllers is designed for a plant with fuzzy linear model. The plant model is described with the expert's linguistic information involved. The linguistic information for the plant model is represented as fuzzy sets. In order to design a robust fuzzy controller for a plant model with fuzzy sets, an approach is developed to implement the best crisp approximation of fuzzy sets into intervals. Then, Kharitonov's Theorem is applied to construct a robust fuzzy controller for the fuzzy uncertain plant with interval model. With the linear combination of input variables as a new input variable, the complexity of the fuzzy mechanism of PID-like fuzzy controller is significantly reduced. The parameters in the robust fuzzy controller are determined to satisfy the stability conditions. The robustness of the designed fuzzy controller is discussed. Also, with the provided definition of relative robustness, the robustness of the complexity reduced fuzzy controller is compared to the classical PID controller for a second-order plant with fuzzy linear model. The simulation results are included to show the effectiveness of the designed PID-like robust fuzzy controller with the complexity reduced fuzzy mechanism. 相似文献
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This paper investigates the robust tracking control problem for a class of nonlinear networked control systems (NCSs) using the Takagi-Sugeno (T-S) fuzzy model approach. Based on a time-varying delay system transformed from the NCSs, an augmented Lyapunov function containing more useful information is constructed. A less conservative sufficient condition is established such that the closed-loop systems stability and time-domain integral quadratic constraints (IQCs) are satisfied while both time-varying network-induced delays and packet losses are taken into account. The fuzzy tracking controllers design scheme is derived in terms of linear matrix inequalities (LMIs) and parallel distributed compensation (PDC). Furthermore, robust stabilization criterion for nonlinear NCSs is given as an extension of the tracking control result. Finally, numerical simulations are provided to illustrate the effectiveness and merits of the proposed method. 相似文献
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The stability and robustness of input-constrained model predictive control can be analyzed using the theory of integral quadratic constraints. We demonstrate the existence of improved multipliers when there are only staged input or box input constraints. This can significantly reduce the conservatism of any stability analysis, and we illustrate the improved performance with a simple numerical example. 相似文献
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Sufficient conditions for the stability of stochastic model predictive control without terminal cost and terminal constraints are derived. Analogous to stability proofs in the nominal setup, we first provide results for the case of optimization over general feedback laws and exact propagation of the probability density functions of the predicted states. We highlight why these results, being based on the principle of optimality, do not directly extend to currently used computationally tractable approximations such as optimization over parameterized feedback laws and relaxation of the chance constraints. Based thereon, for both cases, stability results are derived under stronger assumptions. A third approach is presented for linear systems where propagation of the mean value and the covariance matrix of the states instead of the complete distribution is sufficient, and hence, the principle of optimality can be used again. The main results are presented for nonlinear systems along with examples and computational simplifications for linear systems. 相似文献
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In this paper, a robust model predictive control (MPC) is designed for a class of constrained continuous-time nonlinear systems with bounded additive disturbances. The robust MPC consists of a nonlinear feedback control and a continuous-time model-based dual-mode MPC. The nonlinear feedback control guarantees the actual trajectory being contained in a tube centred at the nominal trajectory. The dual-mode MPC is designed to ensure asymptotic convergence of the nominal trajectory to zero. This paper extends current results on discrete-time model-based tube MPC and linear system model-based tube MPC to continuous-time nonlinear model-based tube MPC. The feasibility and robustness of the proposed robust MPC have been demonstrated by theoretical analysis and applications to a cart-damper springer system and a one-link robot manipulator. 相似文献
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This paper provides a new method of robust maneuver control with guaranteed finite‐time arrival and satisfaction of constraints, despite the action of an unknown but bounded disturbance. The new method extends the constraint tightening approach to robust model predictive control of constrained linear systems by combining it with a variable horizon. This relaxes the requirement for the target to be an invariant set, which is assumed by many stabilizing MPC formulations but can be restrictive in vehicle maneuvering applications. The target sets for vehicle maneuvers are typically determined by the mission requirements and are not generally invariant sets. The new controller guarantees finite‐time arrival within an arbitrary target set, i.e. not necessarily invariant, and is therefore applicable when the target is predetermined by other factors. Several simulation examples are presented including spacecraft rendezvous control with sensor visibility constraints and UAV guidance through obstacle fields. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
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Pantelis Sopasakis Panagiotis Patrinos Haralambos Sarimveis 《Computer methods and programs in biomedicine》2014
In this paper the model predictive control (MPC) technology is used for tackling the optimal drug administration problem. The important advantage of MPC compared to other control technologies is that it explicitly takes into account the constraints of the system. In particular, for drug treatments of living organisms, MPC can guarantee satisfaction of the minimum toxic concentration (MTC) constraints. A whole-body physiologically-based pharmacokinetic (PBPK) model serves as the dynamic prediction model of the system after it is formulated as a discrete-time state-space model. Only plasma measurements are assumed to be measured on-line. The rest of the states (drug concentrations in other organs and tissues) are estimated in real time by designing an artificial observer. The complete system (observer and MPC controller) is able to drive the drug concentration to the desired levels at the organs of interest, while satisfying the imposed constraints, even in the presence of modelling errors, disturbances and noise. 相似文献