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.     

Robust analysis and synthesis of linear time-delay systems with norm-bounded time-varying uncertainty

The problems of robust analysis and synthesis of systems with state-delay and norm-bounded time-varying parameter uncertainty are studied. It is shown that these problems are equivalent to the H_∞ analysis and synthesis problems of an auxiliary system, which is independent of the time-delay and the uncertainty in the system. The necessary and sufficient conditions for the equivalence are developed. Thus any standard H_∞ analysis and synthesis method can be used to solve this problem.     

多面体不确定系统时滞依赖鲁棒预测控制

将线性状态变换引入连续时间多面体不确定时滞系统中,利用线性矩阵不等式(LMI)方法,设计时滞相关型鲁棒预测控制器;通过适当选择Lyapunov函数,推导出闭环系统渐近稳定的充分条件,并且该条件是时滞相关的.仿真算例验证了该方法的有效性.     

时变不确定系统鲁棒控制器设计的新方法

基于包含两个二次项的分段Lyapunov函数,研究了线性时变不确定系统的鲁棒控制器设计问题.所考虑的系统由两个矩阵的凸组合构成,通过引入一个附加矩阵,推导出鲁棒控制器存在的充分条件.该控制器的状态反馈增益的求解问题可以转化为一组带有两个比例参数的线性矩阵不等式的凸优化问题.最后的数值示例说明了该设计方法的可行性.     

A switching robust model predictive control approach for nonlinear systems

This work considers enhancing the stability and improving the economic performance of nonlinear model predictive control in the presence of disturbances or model uncertainties. First, a robust control Lyapunov function (RCLF)-based predictive control strategy is proposed. Second, the approximate dynamic programming (ADP) is employed to further improve regulation performance. Finally, the ADP and RCLF-MPC are combined to provide a switching control scheme, which is illustrated on a CSTR example to show its effectiveness.     

A minimal k-step delay controller for robust tracking of non-minimum phase systems

This paper considers the problem of look-ahead robust tracking for a non-minimum phase system under a sensitivity constraint. Because of non-minimum phase zeros, the exact tracking of arbitrary reference input is impossible. However, with an introduction of some delay, tracking is possible within any given “tolerance” ?₁ > 0. It is shown in the paper that the minimum delay required to satisfy the tracking performance is decoupled from a sensitivity/disturbance rejection constraint. Moreover, it is shown that calculation of the minimum delay reduces to a binary search problem.     

Robust mixed time-optimal control for third-order systems with model uncertainty

In this paper, a robust near time-optimal controller for some third-order uncertain systems with constrained input is presented. The response is usually composed of two parts. At first, the states move towards the goal state from arbitrary initial conditions by pure time-optimal control (bang-bang) and then there is sliding along a predetermined surface within an ellipsoid. The sliding surface is chosen in such a way that the integral of the absolute value of the system error is minimized. Using a combined control algorithm, robustness and stability of the system is guaranteed even with parameter uncertainty and disturbance. The error convergence rate is also improved compared with pure time-optimal control. Simulation results demonstrate the advanced performance of the proposed control algorithm.     

Handling uncertainty in economic nonlinear model predictive control: A comparative case study

In the last years, the use of an economic cost function for model predictive control (MPC) has been widely discussed in the literature. The main motivation for this choice is that often the real goal of control is to maximize the profit or the efficiency of a certain system, rather than tracking a predefined set-point as done in the typical MPC approaches, which can be even counter-productive. Since the economic optimal operation of a system resulting from the application of an economic model predictive control approach drives the system to the constraints, the explicit consideration of the uncertainties becomes crucial in order to avoid constraint violations. Although robust MPC has been studied during the past years, little attention has yet been devoted to this topic in the context of economic nonlinear model predictive control, especially when analyzing the performance of the different MPC approaches. In this work, we present the use of multi-stage scenario-based nonlinear model predictive control as a promising strategy to deal with uncertainties in the context of economic NMPC. We make a comparison based on simulations of the advantages of the proposed approach with an open-loop NMPC controller in which no feedback is introduced in the prediction and with an NMPC controller which optimizes over affine control policies. The approach is efficiently implemented using CasADi, which makes it possible to achieve real-time computations for an industrial batch polymerization reactor model provided by BASF SE. Finally, a novel algorithm inspired by tube-based MPC is proposed in order to achieve a trade-off between the variability of the controlled system and the economic performance under uncertainty. Simulations results show that a closed-loop approach for robust NMPC increases the performance and that enforcing low variability under uncertainty of the controlled system might result in a big performance loss.     

A robust model for multi-floor layout problem

Multi-floor layout problem (MFLP) can be categorized as one of the most crucial basic problems in the real world especially in the field of plant layout design. Specially, the aim of this problem is to find the locations of each department, storage, and elevator in the floors of a multi-floor building and without overlapping to minimize the total material handling cost. In order to consider practical issues in theory and presenting an applicable problem, for the first time in the published literature for MFLP, we develop a robust mathematical model indiscrete and uncertain environment in which some parameters can be changed in predetermined intervals. In fact, one of the most considerable factors is material flow among departments and storages which is assumed to have uncertain values rather than fixed amounts. In this study, we develop a Mixed Integer Programming (MIP) robust model for a new form of MFLP, we called MFDLP where the cellar is considered to contain main storages and upper floors in which departments will be located in predetermined locations. Moreover, there is an elevator set for transferring materials between floors and should be located without overlapping with departments and storages. Finally, the presented model is tested and assessed on some problems and its efficiency is shown.     

Robust feedforward design for a two-degrees of freedom controller

In this paper we address the problem of designing the feedforward block of a two degrees of freedom controller in the case of single input single output (SISO) discrete-time linear systems with model uncertainty. In this work the design of the feedforward filter is formulated as a robust model matching problem. First, a closed form solution of the optimal noncausal filter, which minimizes the worst-case model matching error for each ω[0,2π], is obtained. Then, suitable rational stable approximations of the optimal solution are derived either by means of causal filters or, when a preview of the reference signal is available, by means of noncausal FIR filters. The efficiency of the proposed method is tested on a simulated example.     

A systematic approach for robust repetitive controller design

In this paper, a methodology for the synthesis of repetitive controllers to ensure periodic reference tracking and harmonic disturbance rejection is cast in a robust control framework. Specifically, the Lyapunov–Krasovskii theory is applied to derive LMI-based conditions for designing a state feedback control law with guaranteed stability and performance properties for system parameter variations. Practical experiments in commercial uninterruptible power supplies – UPS are considered to illustrate and discuss some practical implementation aspects of the proposed method.     

基于H∞性能的不确定多速率系统鲁棒预测控制

研究含有不确定性的输入多采样率控制系统的鲁棒预测控制问题,提出了基于Hoo性能的鲁棒预测控制算法.该算法采用线性矩阵不等式(LMI)的方法,得出闭环多采样率系统具有H∞性能指标的上界γ,并给出保证闭环系统鲁棒稳定的判据.仿真结果表明了该算法的有效性.     

一类不确定非线性纯反馈系统的自适应鲁棒模糊控制

针对一类带有不确定性的非线性MIMO纯反馈系统,提出一种自适应鲁棒模糊控制方法,该方法放宽了已有文献对系统模型的限制条件,基于李雅普诺夫分析方法获得了控制输入和自适应律．在控制输入设计中,鲁棒控制项用于补偿逼近误差向量．通过选择适当的设计参数。提出的控制方法使得闭环系统的所有信号是一致有界的和跟踪误差向量的范数收敛到小的零邻域内．仿真结果表明了所提出方法的有效性．     

一类不确定性系统鲁棒H∞控制器

研究一类具有匹配不确定性系统鲁棒H∞控制问题,第一,基于矩阵不等式给出了二次稳定的条件并且对系统的H∞性能进行了分析;第二,给出了系统的鲁棒H∞控制器,该控制器不仅满足系统二次稳定的条件,而且也满足H∞性能约束条件;最后,数值算例说明了控制器的有效性和可行性。     

An ellipsoid algorithm for probabilistic robust controller design

In this paper, a new iterative approach to probabilistic robust controller design is presented, which is applicable to any robust controller/filter design problem that can be represented as an LMI feasibility problem. Recently, a probabilistic Subgradient Iteration algorithm was proposed for solving LMIs. It transforms the initial feasibility problem to an equivalent convex optimization problem, which is subsequently solved by means of an iterative algorithm. While this algorithm always converges to a feasible solution in a finite number of iterations, it requires that the radius of a non-empty ball contained into the solution set is known a priori. This rather restrictive assumption is released in this paper, while retaining the convergence property. Given an initial ellipsoid that contains the solution set, the approach proposed here iteratively generates a sequence of ellipsoids with decreasing volumes, all containing the solution set. At each iteration a random uncertainty sample is generated with a specified probability density, which parameterizes an LMI. For this LMI the next minimum-volume ellipsoid that contains the solution set is computed. An upper bound on the maximum number of possible correction steps, that can be performed by the algorithm before finding a feasible solution, is derived. A method for finding an initial ellipsoid containing the solution set, which is necessary for initialization of the optimization, is also given. The proposed approach is illustrated on a real-life diesel actuator benchmark model with real parametric uncertainty, for which a robust state-feedback controller is designed.     

Computation delay compensation for real time implementation of robust model predictive control

The implementation of model predictive control (MPC) requires to solve an optimization problem online. The computation time, often not negligible especially for nonlinear MPC (NMPC), introduces a delay in the feedback loop. Moreover, it impedes fast sampling rate setting for the controller to react to uncertainties quickly. In this paper, a dual time scale control scheme is proposed for linear/nonlinear systems with external disturbances. A pre-compensator works at fast sampling rate to suppress uncertainty, while the outer MPC controller updates the open loop input sequence at a slower rate. The computation delay is explicitly considered and compensated in the MPC design. Four robust MPC algorithms for linear/nonlinear systems in the literature are adopted and tailored for the proposed control scheme. The recursive feasibility and stability are rigorously analysed. Three simulation examples are provided to validate the proposed approaches.     

A note on robust stabilization for systems with parameters

We consider the problem of stabilizing a linear time invariant multivariable system whose transfer function admits a factorization which contains parametric type uncertainties. The parameters are known to take values in some region Ω_a. We first give a sufficient condition for robust stability when a proper feedback compensator is used. Here it is important to note that no assumption is made about the number of unstable plant poles. Following this we focus attention on how to compute an appropriate robustly stabilizing compensator. This is done in a two step procedure. Initially the solutions of a Diophantine equation generate a class of proper compensators, each of which stabilizes the ‘nominal’ closed loop system. This is followed by a Nyquist Theorem inspired minimization which yields the final choice. An illustrative physical example is given. The compensator thus constructed guarantees stability over . A discussion about how to compute the size of the attainable robust stability region is also included.     

基于鲁棒可靠性的不确定时滞系统最优H∞控制器设计

用区间变量描述控制系统参数的不确定性,提出了不确定时滞系统鲁棒H_∞控制的鲁棒可靠性方法,基于鲁棒可靠性的不确定时滞系统最优状态反馈H_∞控制器设计方法,将系统的最优控制器设计归结为基于线性矩阵不等式(LMI)的优化问题．所设计的控制器可以在满足对所有不确定性鲁棒可靠的前提条件下,具有最优的H_∞鲁棒性能,并能在控制系统的设计中综合考虑控制性能、控制代价和鲁棒可靠性．数值算例说明了所提方法的有效性和可行性．     

内模控制系统鲁棒跟踪控制器的参数化及优化

针对最常用的2自由度内模控制系统,利用基于传递函数互质分解的频域理论,推导了存在模型失配且参考输入和扰动为任意已知函数时,所有能实现稳态无差跟踪的前馈控制器和反馈滤波器的参数化表达式.所得结论同时适用干连续和离散时间系统,并可在参数化基础上实现频域的优化控制.