共查询到20条相似文献,搜索用时 15 毫秒
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This paper addresses robust constrained model predictive control (MPC) for a class of nonlinear systems with structured time‐varying uncertainties. First, the Takagi‐Sugeno (T‐S) fuzzy model is employed to represent a nonlinear system. Then, we develop some techniques for designing fuzzy control which guarantees the system stabilization subject to input and output constraints. Both parallel and nonparallel distributed compensation control laws (PDC and non‐PDC) are considered. Sufficient conditions for the solvability of the controller design problem are given in the form of linear matrix inequalities. A simulation example is presented to illustrate the design procedures and performances of the proposed methods. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society 相似文献
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In this paper, we present a new technique to address constrained robust model predictive control. The main advantage of this new approach with respect to other well-known techniques is the reduced conservativeness. Specifically, the technique described in this paper can be applied to polytopic uncertain systems and is based on the use of several Lyapunov functions each one corresponding to a different vertex of the uncertainty's polytope. 相似文献
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A minimum variance performance map is introduced for constrained linear model predictive control (MPC). The minimum variance performance map provides a demonstration of the effect of constraints in an MPC on the best achievable controller performance. The constrained minimum variance controller is formulated for the MPC system to be monitored. Using multi-parametric quadratic programming (mp-QP), the linear, piecewise control law is obtained for the constrained minimum variance controller. The linear, piecewise control law is used with a Kalman filter to obtain the minimum output variance in each region of the state space partition. The minimum variance performance map is demonstrated on a second order process with a constraint on the input amplitude. 相似文献
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航天器在轨服务对近距离相对运动精确控制的需求越来越强.通过引入集合理论,采用鲁棒可变时域模型预测控制和混合整数线性规划,解决了航天器近距离相对运动的鲁棒控制问题,便于处理控制约束和状态约束,对未知有界干扰、推力误差和导航误差具有鲁棒性.首先,针对航天器近距离相对运动过程中向任意目标集的有限时间机动问题,采用离散化C-W(Clohessy-Wiltshire)动力学模型、时间一能量组合优化目标函数和线性约束表示建立了控制问题模型;其次,给出了基于约束压缩的鲁棒可变时域模型预测控制算法,可以确保鲁棒可行和鲁棒完成;引入i-步鲁棒可控集分析问题可行性,通过集合运算将导航误差处理成有界干扰,采用混合整数线性规划完成了控制器设计.最后,数值仿真验证了模型的有效性. 相似文献
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A stabilizing control method, which does not require on-line optimizations, is developed for linear systems with polytopic model uncertainties and hard input constraints. This work is motivated by the constrained robust MPC (CRMPC) approach (IEEE Trans. Automat. Control 45 (2000a) 1765) which adopts the dual mode prediction strategy (i.e. free control moves and invariant set) and minimizes a worst case performance criterion. Based on the observation that, a feasible control sequence for a particular state can be found as a linear combination of feasible sequences for other states, we suggest a stabilizing control algorithm providing sub-optimal and feasible control sequences using pre-computed optimal sequences for some canonical states. The on-line computation of the proposed method reduces to simple matrix multiplication. 相似文献
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This paper addresses the development of stabilizing state and output feedback model predictive control (MPC) algorithms for constrained continuous-time nonlinear systems with discrete observations. Moreover, we propose a nonlinear observer structure for this class of systems and derive sufficient conditions under which this observer provides asymptotically convergent estimates. The MPC scheme proposed consists of a basic finite horizon nonlinear MPC technique with the introduction of an additional state constraint, which has been called a contractive constraint. The resulting MPC scheme has been denoted contractive MPC. This is a Lyapunov-based approach in which a Lyapunov function chosen a priori is decreased, not continuously, but discretely; it is allowed to increase at other times. We show in this work that the implementation of this additional constraint into the online optimization makes it possible to prove strong nominal stability properties of the closed-loop system 相似文献
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Efficient robust constrained model predictive control with a time varying terminal constraint set 总被引:7,自引:0,他引:7
An efficient robust constrained model predictive control algorithm with a time varying terminal constraint set is developed for systems with model uncertainty and input constraints. The approach is novel in that it off-line constructs a continuum of terminal constraint sets and on-line achieves robust stability by using a relatively short control horizon (even N=0) with a time varying terminal constraint set. This algorithm not only dramatically reduces the on-line computation but also significantly enlarges the size of the allowable set of initial conditions. Moreover, this control scheme retains the unconstrained optimal performance in the neighborhood of the equilibrium. The controller design is illustrated through a benchmark problem. 相似文献
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A non-fragile robust model predictive control (RMPC) is designed in the uncertain systems under bounded control signals. To this aim, a class of the nonlinear systems with additive uncertainty is considered in its general form. The RMPC synthesis could lead to the proper selection of the controller’s gains. Thus, the non-fragile RMPC design is translated into a minimization problem subjected to some constraints in terms of linear matrix inequality (LMI). Hence, the controller’s gains are computed by solving such a minimization problem. In some numerical examples, the suggested non-fragile RMPC is compared with the other methods. The simulation results demonstrate the effectiveness of the proposed RMPC in comparison with similar techniques. 相似文献
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Min-max feedback formulations of model predictive control are discussed, both in the fixed and variable horizon contexts. The control schemes the authors discuss introduce, in the control optimization, the notion that feedback is present in the receding-horizon implementation of the control. This leads to improved performance, compared to standard model predictive control, and resolves the feasibility difficulties that arise with the min-max techniques that are documented in the literature. The stabilizing properties of the methods are discussed as well as some practical implementation details 相似文献
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We address the distributed model predictive control (MPC) for a set of linear local systems with decoupled dynamics and a coupled global cost function. By the decomposition of the global cost function, the distributed control problem is converted to the MPC for each local system associated with a cost involving neighboring system states and inputs. For each local controller, the infinite horizon control moves are parameterized as N free control moves followed by a single state feedback law. An interacting compatibility condition is derived, disassembled and incorporated into the design of each local control so as to achieve the stability of the global closed‐loop system. Each local system exchanges with its neighbors the current states and the previous optimal control strategies. The global closed‐loop system is shown to be exponentially stable provided that all the local optimizers are feasible at the initial time. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
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In this paper, we develop an algorithm to compute robust MPC explicit solutions for constrained MIMO systems with internal uncertainties and external disturbances. Our approach is based on a recursive closed‐loop prediction strategy to realize a finite horizon robust MPC regulator, which has the feature that only one‐step state prediction is sufficient to realize robust MPC with an arbitrary prediction horizon. The paper defines a set of recursive sub‐optimization problems as multiple‐parametric sub‐quadratic programming (mp‐SQP), and shows that the optimal solution to the mp‐SQP problem is piecewise affine functions of states, associated with piece objectives and state critical regions. Asymptotic closed‐loop stability can be guaranteed by a terminal weighting and a terminal feedback gain; also by introducing two tuning variables, the algorithm is capable of adjusting the trade‐off between system performance and robustness. The state admissible set, which is not easily derived from physical vision, is constructed by two methods: a piecewise linear norm of signals, and polyhedral Voronoi sets. Finally, two simulation examples demonstrate that the algorithm is efficient, feasible and flexible, and can be applied to both slow and fast industrial MIMO systems. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
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《Journal of Process Control》2014,24(8):1237-1246
In this paper, we develop a tube-based economic MPC framework for nonlinear systems subject to unknown but bounded disturbances. Instead of simply transferring the design procedure of tube-based stabilizing MPC to an economic MPC framework, we rather propose to consider the influence of the disturbance explicitly within the design of the MPC controller, which can lead to an improved closed-loop average performance. This will be done by using a specifically defined integral stage cost, which is the key feature of our proposed robust economic MPC algorithm. Furthermore, we show that the algorithm enjoys similar properties as a nominal economic MPC algorithm (i.e., without disturbances), in particular with respect to bounds on the asymptotic average performance of the resulting closed-loop system, as well as stability and optimal steady-state operation. 相似文献
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Melanie N. Zeilinger Davide M. Raimondo Alexander Domahidi Manfred Morari Colin N. Jones 《Automatica》2014
High-speed applications impose a hard real-time constraint on the solution of a model predictive control (MPC) problem, which generally prevents the computation of the optimal control input. As a result, in most MPC implementations guarantees on feasibility and stability are sacrificed in order to achieve a real-time setting. In this paper we develop a real-time MPC approach for linear systems that provides these guarantees for arbitrary time constraints, allowing one to trade off computation time vs. performance. Stability is guaranteed by means of a constraint, enforcing that the resulting suboptimal MPC cost is a Lyapunov function. The key is then to guarantee feasibility in real-time, which is achieved by the proposed algorithm through a warm-starting technique in combination with robust MPC design. We address both regulation and tracking of piecewise constant references. As a main contribution of this paper, a new warm-start procedure together with a Lyapunov function for real-time tracking is presented. In addition to providing strong theoretical guarantees, the proposed method can be implemented at high sampling rates. Simulation examples demonstrate the effectiveness of the real-time scheme and show that computation times in the millisecond range can be achieved. 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 proposes a quadratic programming (QP) approach to robust model predictive control (MPC) for constrained linear systems having both model uncertainties and bounded disturbances. To this end, we construct an additional comparison model for worst-case analysis based on a robust control Lyapunov function (RCLF) for the unconstrained system (not necessarily an RCLF in the presence of constraints). This comparison model enables us to transform the given robust MPC problem into a nominal one without uncertain terms. Based on a terminal constraint obtained from the comparison model, we derive a condition for initial states under which the ultimate boundedness of the closed loop is guaranteed without violating state and control constraints. Since this terminal condition is described by linear constraints, the control optimization can be reduced to a QP problem. 相似文献