Robust output feedback model predictive control of constrained linear systems

This paper provides a solution to the problem of robust output feedback model predictive control of constrained, linear, discrete-time systems in the presence of bounded state and output disturbances. The proposed output feedback controller consists of a simple, stable Luenberger state estimator and a recently developed, robustly stabilizing, tube-based, model predictive controller. The state estimation error is bounded by an invariant set. The tube-based controller ensures that all possible realizations of the state trajectory lie in a simple uncertainty tube the ‘center’ of which is the solution of a nominal (disturbance-free) system and the ‘cross-section’ of which is also invariant. Satisfaction of the state and input constraints for the original system is guaranteed by employing tighter constraint sets for the nominal system. The complexity of the resultant controller is similar to that required for nominal model predictive control.     

具有方差和极点约束的不确定系统鲁棒H∞输出反馈控制

针对一类具有范数有界不确定性的连续系统和二次矩阵不等式区域,考虑系统具有方差和区域极点约束的输出反馈控制器设计问题．为此首先导出闭环系统区域稳定的充分必要条件．然后用线性矩阵不等式方法给出输出反馈控制器存在的一个充分条件．在此充分条件下闭环系统是鲁棒区域稳定的且具有H∞性能以及当干扰为白噪声信号时其稳态状态方差有限．接下来用矩阵分解方法给出输出反馈控制器增益矩阵的求解过程．最后通过一个仿真实例说明本文所提出的控制器设计方法的有效性．     

具有方差和极点约束的不确定系统鲁棒H∞输出反馈控制

针对一类具有范数有界不确定性的连续系统和二次矩阵不等式区域, 考虑系统具有方差和区域极点约束 的输出反馈控制器设计问题. 为此首先导出闭环系统区域稳定的充分必要条件. 然后用线性矩阵不等式方法给出输出反馈控制器存在的一个充分条件. 在此充分条件下闭环系统是鲁棒区域稳定的且具有H-infinity性能以及当干扰为白噪声信号时其稳态状态方差有限. 接下来用矩阵分解方法给出输出反馈控制器增益矩阵的求解过程. 最后通过一个仿真实例说明本文所提出的控制器设计方法的有效性.     

An improved state-space model structure and a corresponding predictive functional control design with improved control performance

Conventional state-space model predictive control requires a state estimator/observer to access the state information for feedback controller design. Its drawbacks are the numerical convergence stability of the observer and closed-loop control performance deterioration with activated plant input/output constraints. The recent direct use of measured input and output variables to formulate a non-minimal state-space (NMSS) model overcomes these problems, but the subsequent controller is too sensitive to model mismatch. In this article, an improved structure of NMSS model that incorporates the output-tracking error is first formulated and then a subsequent predictive functional control design is proposed. The proposed controller is tested on both model match and model mismatch cases for comparison with previous controllers. Results show that control performance is improved. In addition, a linear programming method for constraints dealing and a closed form of transfer function representation of the control system are provided for further insight into the proposed method.     

Output feedback MPC with guaranteed robust stability

This paper focuses on the issues of robust stability of model predictive control (MPC). The control problem is formulated as linear matrix inequalities (LMI) optimization problem. A suboptimal solution for the output feedback control problem is proposed. The size of the resulting MP controller is reduced by using a suitable state-space representation of the process. Guaranteed stability conditions for the output feedback MPC are enforced via a Lyapunov type constraint. An iterative algorithm is developed resulting in a pair of coupled LMI optimization problems which provide a robustly stable output feedback gain. Model uncertainties are considered via a polytopic set of process models. The methodology is illustrated with the simulation of the control problem of two chemical processes. The results show that the proposed strategy eliminates the need to detune the MP controller improving the performance for most of the cases considered.     

Robust output feedback model predictive control using off-line linear matrix inequalities

A fundamental question about model predictive control (MPC) is its robustness to model uncertainty. In this paper, we present a robust constrained output feedback MPC algorithm that can stabilize plants with both polytopic uncertainty and norm-bound uncertainty. The design procedure involves off-line design of a robust constrained state feedback MPC law and a state estimator using linear matrix inequalities (LMIs). Since we employ an off-line approach for the controller design which gives a sequence of explicit control laws, we are able to analyze the robust stabilizability of the combined control laws and estimator, and by adjusting the design parameters, guarantee robust stability of the closed-loop system in the presence of constraints. The algorithm is illustrated with two examples.     

基于状态观测器的约束鲁棒模型预测控制

模型不确定情况下的鲁棒问题是模型预测控制的一个根本问题。本文采用线性矩阵不等式(LMI)，研究多模型不确定性描述情况下的鲁棒模型预测控制问题。在输入输出约束条件下，最小化最坏情况下的无穷时域目标函数，获得保证系统稳定的基于状态观测器的状态反馈增益并且给出观测器增益的设计方法。实例说明算法可行且保证闭环系统渐近稳定。     

Output feedback model predictive control of uncertain norm‐bounded linear systems

A constrained output feedback model predictive control (MPC) scheme for uncertain Norm‐Bounded discrete‐time linear systems is presented. This scheme extends recent results achieved by the authors under full‐state availability to the more interesting case of incomplete and noisy state information. The design procedure consists of an off‐line step where a state feedback and an asymptotic observer (dynamic primal controller) are designed via bilinear matrix inequalities and used to robustly stabilize a suitably augmented state plant. The on‐line moving horizon procedure adds N free control moves to the action of the primal controller which are computed by solving a linear matrix inequality optimization problem whose numerical complexity grows up only linearly with the control horizon N. The effectiveness of the proposed MPC strategy is illustrated by a numerical example. Copyright © 2010 John Wiley & Sons, Ltd.     

Output feedback robust MPC with one free control move for the linear polytopic uncertain system with bounded disturbance

In this paper a previous approach, for the robust model predictive control (MPC) for a linear polytopic uncertain system, is extended to the case with bounded disturbance and unmeasurable state. The controller on-line optimizes a free control move followed by an output feedback control law based on the pre-specified state estimator. A key technique for this controller is an appropriate formulation of the estimation error bound which accounts for recursive feasibility of the optimization problem. The quadratic boundedness (QB) of the augmented state is guaranteed by the proposed approach. A numerical example is given to illustrate the effectiveness of the proposed controller.     

MPC for stable linear systems with model uncertainty

In this paper, we developed a model predictive controller, which is robust to model uncertainty. Systems with stable dynamics are treated. The paper is mainly focused on the output-tracking problem of a system with unknown steady state. The controller is based on a state-space model in which the output is represented as a continuous function of time. Taking advantage of this particular model form, the cost functions is defined in terms of the integral of the output error along an infinite prediction horizon. The model states are assumed perfectly known at each sampling instant (state feedback). The controller is robust for two classes of model uncertainty: the multi-model plant and polytopic input matrix. Simulations examples demonstrate that the approach can be useful for practical application.     

A note on stability, robustness and performance of output feedback nonlinear model predictive control

In recent years, nonlinear model predictive control (NMPC) schemes have been derived that guarantee stability of the closed loop under the assumption of full state information. However, only limited advances have been made with respect to output feedback in the framework of nonlinear predictive control. This paper combines stabilizing instantaneous state feedback NMPC schemes with high-gain observers to achieve output feedback stabilization. For a uniformly observable MIMO system class it is shown that the resulting closed loop is asymptotically stable. Furthermore, the output feedback NMPC scheme recovers the performance of the state feedback in the sense that the region of attraction and the trajectories of the state feedback scheme can be recovered to any degree of accuracy for large enough observer gains, thus leading to semi-regional results. Additionally, it is shown that the output feedback controller is robust with respect to static sector bounded nonlinear input uncertainties.     

基于神经网络的一类非线性系统自适应输出跟踪

针对一类未知非线性系统,提出了一种输出反馈控制方法.首先,在假设系统状态已知情况下设计状态反馈控制器,实现跟踪性能;然后,在系统状态不完全可测的情况下,通过设计高增益观测器对系统的状态进行估计,实现输出反馈控制器设计,证明了所设计的输出反馈控制器可以获得状态反馈控制器的性能.     

Robust end-point optimizing feedback for nonlinear dynamic processes

The paper suggests two novel approaches to the synthesis of robust end-point optimizing feedback for nonlinear dynamic processes. Classically, end-point optimization is performed only for the nominal process model using optimal control methods, and the question of performance robustness to disturbances and model-plant mismatch remains unaddressed. The present contribution addresses the end-point optimization problem for nonlinear affine systems with fixed final time through robust optimal feedback methods. In the first approach, a nonlinear state feedback is derived that robustly optimizes the final process state. This solution is obtained through series expansion of the Hamilton-Jacobi-Bellman PDE with an active opponent disturbance. As reliable measurements or estimates of all states may not always be available, the second approach also robustly optimizes the process end-point, but uses output rather than state information. This direct use of measurement information is preferred since the choice of a state estimator for robust state feedback is non-trivial even when the observability issue is addressed. A linear time-variant output corrector is obtained by feedback parametrization and numerical optimization of a nonlinear H _∞ cost functional. A number of possible variations and alternatives to both approaches are also discussed. As model-plant mismatch is particularly common with chemical batch processes, the suitability of the robust optimizing feedback is demonstrated on a semi-batch reactor simulation example, where robustness to several realistic mismatches is investigated and the results are compared against those for the optimal open-loop policy and the optimal feedback designed for the nominal model.     

数值界不确定性关联大系统分散鲁棒H∞控制

针对一类状态阵,控制输入阵及关联阵中存在数值界不确定性的关联大系统,研究其分散鲁棒H∞状态反馈和输出反馈控制器设计问题.基于有界实引理,推导出了其存在分散鲁棒H∞控制器的充分条件,即一组矩阵不等式有解.利用Schur补引理,通过固定不同变量,提出了一种构建分散控制器的同伦迭代线性矩阵不等式方法.所获得的控制器使闭环大系统鲁棒稳定,并且达到给定的H∞性能指标.最后用数值例子说明了所提的设计方法的有效性.     

不确定关联大系统分散鲁棒H∞输出反馈控制

针对一类状态矩阵和控制矩阵存在参数不确定性关联大系统,研究其分散鲁棒H∞输出反馈控制问题.基于有界实引理将其鲁棒分散H∞动态输出反馈控制器的解归结为一个非线性矩阵不等式(NLMI),先通过选取适当的同伦函数来表示该非线性矩阵不等式,再通过Schur补引理将其化为两个双线性矩阵不等式,最后通过迭代算法求解该控制器,使闭环大系统鲁棒渐进稳定,并且满足给定的H∞性能指标.     

Two-step output feedback predictive control for Hammerstein systems with networked-induced time delays

This paper is mainly concerned with the design problem of two-step model predictive control (MPC) for nonlinear systems represented by Hammerstein model, where the network-induced time delays exist between sensor to controller (S2C) and controller to actuator (C2A) links. We assume that the system state is not measurable, so the state observer is employed to estimate the state. The intermediate variable for the linear part of the system is calculated by minimising the quadratic performance function. The time-delay compensation algorithm of two-step output feedback predictive control (TSOFPC) for Hammerstein systems is presented and validated by a numerical example.     

Output Feedback Control of Surge and Rotating Stall in Axial Compressors

Active control has been widely studied to expand the stable operating range of axial flow compressors in aero‐engines.In this paper, a nonlinear active state feedback controller is proposed first based on bifurcation analysis results of MG model.Then by taking pressure ratio as output parameter, a state observer is designed to estimate the compressor's flow coefficient. An output feedback controller is thus established by combining the observer with the state feedback controller.Finally, digital simulation shows that the proposed output feedback controller not only eliminates the hysteresis loop associated with rotating stall and emergence of surge, but also extends the stable operating range of axial flow compressors effectively.Furthermore, being flexible in using pressure transducer and throttle valve as sensor and actuator respectively, which have been equipped in common engines, the proposed controller has good applicability.     

Robust control of a family of uncertain nonminimum‐phase systems via continuous‐time and sampled‐data output feedback

The problem of global robust stabilization is studied by both continuous‐time and sampled‐data output feedback for a family of nonminimum‐phase nonlinear systems with uncertainty. The uncertain nonlinear system considered in this paper has an interconnect structure consisting of a driving system and a possibly unstable zero dynamics with uncertainty, ie, the uncertain driven system. Under a linear growth condition on the uncertain zero dynamics and a Lipschitz condition on the driving system, we show that it is possible to globally robustly stabilize the family of uncertain nonminimum‐phase systems by a single continuous‐time or a sampled‐data output feedback controller. The sampled‐data output feedback controller is designed by using the emulated versions of a continuous‐time observer and a state feedback controller, ie, by holding the input/output signals constant over each sampling interval. The design of either continuous‐time or sampled‐data output compensator uses only the information of the nominal system of the uncertain controlled plant. In the case of sampled‐data control, global robust stability of the hybrid closed‐loop system with uncertainty is established by means of a feedback domination method together with the robustness of the nominal closed‐loop system if the sampling time is small enough.     

Robust output feedback stabilization of a class of time-varying non-linear systems

This paper deals with the problem of robust output feedback stabilization of a class of time-varying non-linear systems. This class of systems involves two kinds of time-varying uncertainties: those norm-bounded and those bounded by a smooth non-linear function of the output. Under the assumption that the zero dynamics of the system are uniformly asymptotically stable and some additional mild conditions, we show via a Lyapunov function approach that the uncertain system can be robustly stabilized by a time-varying non-linear output feedback controller. The order of this controller turns out to be one less than the relative degree of the uncertain system. A systematic design procedure is given for constructing the controller. Illustrative examples are given. Note that the results generalize several previous results on robust output feedback stabilization.