Constrained optimal control: an application to semiactive suspension systems

This article applies three different control techniques to the design of a quarter-car semiactive suspension system. The three techniques, originally developed to solve a constrained optimal control problem, are optimal gain switching, discontinuous variable structure control and explicit model predictive control. All of them divide the state space into convex regions and assign a linear or affine state feedback controller to each region. The partition of the state space is computed off-line. During the on-line phase, the controller switches between the subcontrollers according to the current state. All the above techniques gave satisfactory results when applied to the design of semiactive suspension systems. A detailed comparison in terms of computational complexity, performance and simplicity of the design is proposed in the article.     

Constrained output feedback model predictive control for nonlinear systems

A constrained output feedback model predictive control approach for nonlinear systems is presented in this paper. The state variables are observed using an unscented Kalman filter, which offers some advantages over an extended Kalman filter. A nonlinear dynamic model of the system, considered in this investigation, is developed considering all possible effective elements. The model is then adaptively linearized along the prediction horizon using a state-dependent state space representation. In order to improve the performance of the control system as many linearized models as the number of prediction horizons are obtained at each sample time. The optimum results of the previous sample time are utilized for linearization at the current sample time. Subsequently, a linear quadratic objective function with constraints is formulated using the developed governing equations of the plant. The performance and effectiveness of the proposed control approach is validated both in simulation and through real-time experimentation using a constrained highly nonlinear aerodynamic test rig, a twin rotor MIMO system (TRMS).     

Constrained predictive control for multivariable systems with application to power systems

Generalized predictive control (GPC) and dynamic performance predictive control (DPC) algorithms are introduced for industrial applications. Constraints on plant input rate, plant absolute input and plant absolute output can be implemented and are demonstrated on an application of these algorithms. A standard quadratic programming algorithm performs the calculation of the optimal control. A MATLAB/Simulink toolbox environment has been developed where controllers can be designed, linear and non-linear plant models can be embedded, discrete- and continuous-time loop parts can be mixed and simulation results can be managed and evaluated by graphical and statistical tools. This package utilises a graphical user interface. Finally, a case study design example is presented where a linear gas turbine model for power generation is examined with constrained GPC and DPC, and the advantages and drawbacks of the approach are the discussed. Copyright © 1999 John Wiley & Sons, Ltd.     

Constrained intermittent model predictive control

The generalised hold formulation of intermittent control is re-examined and shown to have some useful theoretical and practical properties. It is shown that this provides a foundation for constrained model predictive control in an intermittent context. The method is illustrated using an example and verified with experimental results.     

Robust model predictive control for continuous uncertain systems with state delay

This paper is concerned with robust model predictive control for linear continuous uncertain systems with state delay and control constraints. A piecewise constant control sequence is calculated by minimizing the upper-bound of the infinite horizon quadratic cost function. At each sampling time, the sufficient conditions for the existence of the model predictive control are derived, and expressed as a set of linear matrix inequalities. The robust stability of the closed-loop systems is guaranteed by the proposed design method. A numerical example is given to illustrate the main results.     

Robust model predictive control for continuous uncertain systems with state delay

This paper is concerned with robust model predictive control for linear continuous uncertain systems with state delay and control constraints, A piecewise constant control sequence is calculated by minimizing the upper-bound of the infinite horizon quadratic cost function, At each sampling time, the sufficient conditions for the existence of the model predictive control are derived, and expressed as a set of linear matrix inequalities. The robust stability of the closed-loop svstems is guaranteed bv the proposed design method. A numerical example is given to illustrate the main results.     

Constrained receding horizon predictive control for nonlinear systems

The paper concerns the receding horizon predictive control of constrained nonlinear systems and presents an algorithm which relies on the online solution of a simple linear program (LP). Use is made of a finite control horizon in conjunction with a terminal inequality constraint and a predicted cost that includes a terminal penalty term. The optimization procedure is based on predictions made by linearized incremental models at points of a given seed trajectory and the effects of linearization error are taken into account to give a bound on the predicted tracking error. The algorithm is posed in the form of an LP and the proper selection of the terminal penalty term of the predicted cost guarantees the asymptotic stability. The results of the paper are illustrated by means of a simple example.     

Constrained model predictive force control of an electrohydraulic actuator

An analytical MPC controller was designed for force control of a single-rod electrohydraulic actuator. The controller based on a difference equation uses short control horizon. The constraints on both input and output variables are taken into consideration by the controller. The mechanism of output constraints satisfaction uses output prediction and makes possible to constrain the output values many sampling instants ahead. Thus, it extends capabilities of the analytical MPC controllers to the field reserved so far for much more computationally expensive numerical MPC algorithms. Results of real life experiments illustrate efficiency of the proposed controller. The results also show that the MPC controller has better tracking performance than conventional P and PI controllers. The MPC controller with the constraint handling mechanisms, though relatively simple, offers very good performance. As the design process is detailed, it is possible to relatively easy adapt the proposed approach to other control plants.     

时变不确定系统的变时域离线鲁棒预测控制

给出多包描述约束系统的鲁棒调节器的一种新方法.现有的离线方案离线构造一系列的状态反馈控制律,其中每一个控制律是通过将无穷时域的控制输入固定为唯一的状态反馈控制律而得到的.而本文在优化较大椭圆内的控制律时,使下个时刻的状态进入临近的更小的椭圆———在较小的椭圆内部,相应的控制律序列作为局部控制器.因此,本文新方法相当于给出了变时域的预测控制器,可给出更优的控制作用.仿真例子说明了新方法的有效性.     

Fuzzy neural-based control for nonlinear time-varying delay systems.

In this paper, a partially known nonlinear dynamic system with time-varying delays of the input and state is approximated by N fuzzy-based linear subsystems described by a state-space model with average delay. To shape the response of the closed-loop system, a set of fuzzy reference models is established. Similarly, the same fuzzy sets of the system rule are employed to design a fuzzy neural-based control. The proposed control contains a radial-basis function neural network to learn the uncertainties caused by the approximation error of the fuzzy model (e.g., time-varying delays and parameter variations) and the interactions resulting from the other subsystems. As the norm of the switching surface is inside of a defined set, the learning law starts; in this situation, the proposed method is an adaptive control possessing an extra compensation of uncertainties. As it is outside of the other set, which is smaller than the aforementioned set, the learning law stops; under this circumstance, the proposed method becomes a robust control without the compensation of uncertainties. A transition between robust control and adaptive control is also assigned to smooth the possible discontinuity of the control input. No assumption about the upper bound of the time-varying delays for the state and the input is required. However, two time-average delays are needed to simplify the controller design: 1) the stabilized conditions for every transformed delay-free subsystem must be satisfied; and 2) the learning uncertainties must be relatively bounded. The stability of the overall system is verified by Lyapunov stability theory. Simulations as compared with a linear transformed state feedback with integration control are also arranged to consolidate the usefulness of the proposed control.     

On control for linear systems with interval time-varying delay

This paper deals with the problem of delay-dependent robust H_∞ control for linear time-delay systems with norm-bounded, and possibly time-varying, uncertainty. The time-delay is assumed to be a time-varying continuous function belonging to a given interval, which means that the lower and upper bounds for the time-varying delay are available, and no restriction on the derivative of the time-varying delay is needed, which allows the time-delay to be a fast time-varying function. Based on an integral inequality, which is introduced in this paper, and Lyapunov–Krasovskii functional approach, a delay-dependent bounded real lemma (BRL) is first established without using model transformation and bounding techniques on the related cross product terms. Then employing the obtained BRL, a delay-dependent condition for the existence of a state feedback controller, which ensures asymptotic stability and a prescribed H_∞ performance level of the closed-loop systems for all admissible uncertainties, is proposed in terms of a linear matrix inequality (LMI). A numerical example is also given to illustrate the effectiveness of the proposed method.     

Constrained receding horizon predictive control for systems with disturbances

A receding horizon predictive control method for systems with input constraints and disturbances is proposed. A polyhedral feasible set of states which is invariant with respect to a given state feedback control law is derived in the presence of bounded disturbances. The proposed predicted control algorithm deploys a strategy in which the current state is steered into the polyhedral invariant feasible set within a finite number N of feasible control moves, despite the presence of disturbances. The future control moves over the horizon N are represented as the sum of the state feedback control and a perturbation; the perturbation term provides the degrees of freedom with which to enlarge the stabilizable set of initial states. The control algorithm is formulated in linear matrix inequalities so that it can be solved using semidefinite programming.     

基于IGPC的时变大时滞系统自适应控制

针对工业过程中常见的参数时变和大时滞问题,研究了广义预测控制算法在其中的应用问题。为了克服普通广义预测控制算法计算复杂的缺点,采用隐式广义预测控制算法(IGPC)通过直接辩识控制器参数求解最优控制增量,具有计算量小、计算速度快的特点。仿真结果表明,在不需要关于被控对象的先验知识的情况下,隐式广义预测自校正控制器能很好地跟踪设定值的变化,当参数时变时仍具有很好的控制性能,适合于实现时变大时滞系统的自适应控制。     

Oscillation control for n-dimensional congestion control model via time-varying delay

Time delay or round trip time (RTT) is an important parameter in the model of Internet congestion control. On the one hand, the delay may induce oscillation via the Hopf bifurcation. In the present paper, a congestion control model of n dimensions is considered to study the delay-induced oscillation. By linear analysis of the n-dimensional system, the critical delay for the Hopf bifurcation is obtained. To describe the relation between the delay and oscillation analytically, the method of multiple scales (M...     

饱和非线性时滞系统约束预测控制

针对一类具有执行器饱和与输出约束的离散非线性时滞系统,提出新的模糊预测控制方法。首先,采用T-S模糊模型来逼近实际非线性系统,运用平行分步补偿（PDC）原理将该系统转化为一系列线性系统的凸组合。其次,通过每个采样时刻优化无穷时域的“min-max”性能指标来求解状态反馈预测控制器,得到系统满足Lyapunov渐近稳定的充分条件,并进一步将该条件转化为基于线性矩阵不等式（LMI）技术的半正定规划（SDP）问题。最后,通过数值仿真验证该方法的有效性。     

Robust distributed model predictive control of linear systems with structured time-varying uncertainties

In this work, synthesis of robust distributed model predictive control (MPC) is presented for a class of linear systems subject to structured time-varying uncertainties. By decomposing a global system into smaller dimensional subsystems, a set of distributed MPC controllers, instead of a centralised controller, are designed. To ensure the robust stability of the closed-loop system with respect to model uncertainties, distributed state feedback laws are obtained by solving a min–max optimisation problem. The design of robust distributed MPC is then transformed into solving a minimisation optimisation problem with linear matrix inequality constraints. An iterative online algorithm with adjustable maximum iteration is proposed to coordinate the distributed controllers to achieve a global performance. The simulation results show the effectiveness of the proposed robust distributed MPC algorithm.     

Robust model predictive control with time-varying tubes

This paper focuses on the problem of robustly stabilizing uncertain discrete-time systems subject to bounded disturbances. The proposed tube-based model predictive controller ensures that all possible realizations of the state trajectory lie in the time-varying tubes so robust stability and satisfaction of the state and input constraints are guaranteed. The time-varying tubes are computed off-line so the on-line computational time is tractable. At each sampling time, the precomputed time-varying tubes are included in the optimal control problem as the constraints in the prediction horizon and only a quadratic programming problem is solved. In comparison to the algorithm that calculates the time-varying tubes on-line, the proposed algorithm can achieve the same level of control performance while the on-line computational time is greatly reduced.