An optimal control method for linear systems with time delay

An optimal control method for linear systems with time delay is developed in this paper. In the proposed control method, the differential equation with time delay of the system dynamics is first rewritten into a form without any time delay through a particular transformation. Then, the optimal controller is designed by using the classical optimal control theory. A numerical algorithm for control implementation is presented, since the obtained expression of the optimal controller contains an integral term that is not convenient for online calculation. The time delay is considered at the very beginning of the control design, and no approximation and estimation are made in the control system. Thus the system performance and stability are prone to be guaranteed. Instability in responses might occur only if a system with time delay is controlled by the optimal controller that was designed with no consideration of time delay. The effectiveness of the proposed optimal controller is demonstrated by simulation studies.     

Robust optimal control of time‐delay systems based on Razumikhin theorem

This paper presents an optimal control approach for the general robust control design problem of linear time delay systems, which considers parameter uncertainties as well as state delay. It is shown that the robust control problem can be transformed into an optimal control problem with the amouof plant uncertainties involved in the performance index. A stability criterion has been developed under which the uncertain dynamical system can not only achieve stability, but also acquire the guaranteed level of performance for regulation. A suitable linear state feedback control law is also characterized via Lyapunov stability theory to ensure performance robustness of the closed‐loop system. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Wiener theory of digital linear-quadratic control

The linear quadratic regulator problem is considered for discrete time systems with time delay. The theory forms a close parallel to that for stationary prediction and filtering. For coloured noise disturbance at the plant input it is shown that the optimal control decomposes into the solution for white noise disturbance plus a feedforward controller. For unit time delay the solution becomes equivalent to that given by the stationary Kalman theory.     

非线性广义最小方差控制律综述

如何设计简单的控制策略对复杂非线性系统进行控制是控制界还未解决的难题．非线性广义最小方差控制律的提出使得非线性控制器的设计可以基于更为一般的非线性模型，并且控制器易于实现．整个系统包含时滞环节，稳定的非线性输入子系统和一个可以用多项式或者状态空间描述的子系统．通过最小化由误差加权项、状态加权项和输入加权项组成的信号的方差得到优化控制器．在系统为开环稳定的情况下，可用史密斯预估器进行控制．本文首先介绍了非线性广义最小方差控制的发展进程，然后综述了基于状态空间和多项式描述的系统的非线性广义最小方差控制器的设计以及其现状和今后的发展方向．     

Optimal control of linear time-delay systems

A method is presented whereby an optimal control may be obtained for a linear time-varying system with time delay. The performance criterion is quadratic with a fixed, finite upper limit, and results in a set of differential equations with boundary conditions whose solution yields an optimal feedback control. A numerical technique is developed for the solution of the differential equations, and two examples are worked.     

On exponential stability of linear networked control systems

This paper addresses exponential stability of linear networked control systems. More specifically, the paper considers a continuous‐time linear plant in feedback with a linear sampled‐data controller with an unknown time varying sampling rate, the possibility of data packet dropout, and an uncertain time varying delay. The main contribution of this paper is the derivation of new sufficient stability conditions for linear networked control systems taking into account all of these factors. The stability conditions are based on a modified Lyapunov–Krasovskii functional. The stability results are also applied to the case where limited information on the delay bounds is available. The case of linear sampled‐data systems is studied as a corollary of the networked control case. Furthermore, the paper also formulates the problem of finding a lower bound on the maximum network‐induced delay that preserves exponential stability as a convex optimization program in terms of linear matrix inequalities. This problem can be solved efficiently from both practical and theoretical points of view. Finally, as a comparison, we show that the stability conditions proposed in this paper compare favorably with the ones available in the open literature for different benchmark problems. Copyright © 2012 John Wiley & Sons, Ltd.     

Optimal control of saturable linear plants for minimum integral-square-error and similar performance criteria†

The paper deals with the problem of minimizing integral–square–error and similar performance indices for linear single–variable plants with saturating control input. It is shown that the optimal control can be obtained by treating it as the limit as υ→∞ of the Sequence of controls which minimize the performance index on the class of all bang–bang controls containing not more than υ switches. These optimal υ switch controls are shown to satisfy a modified maximum principle.The method enables the computation of optimal controls from any given initial plant state. The particular problems of minimizing integral–modulus–error for a two–integrator plant and integral–square–error for a three–integrator plant are worked out in detail so as to illu3trata the techniques involved.     

An integrated optimal control algorithm for discrete-time nonlinear stochastic system

Consider a discrete-time nonlinear system with random disturbances appearing in the real plant and the output channel where the randomly perturbed output is measurable. An iterative procedure based on the linear quadratic Gaussian optimal control model is developed for solving the optimal control of this stochastic system. The optimal state estimate provided by Kalman filtering theory and the optimal control law obtained from the linear quadratic regulator problem are then integrated into the dynamic integrated system optimisation and parameter estimation algorithm. The iterative solutions of the optimal control problem for the model obtained converge to the solution of the original optimal control problem of the discrete-time nonlinear system, despite model-reality differences, when the convergence is achieved. An illustrative example is solved using the method proposed. The results obtained show the effectiveness of the algorithm proposed.     

Real-time guaranteed cost control of MIMO networked control systems with packet disordering

This paper discusses guaranteed cost control for multi-input and multi-output (MIMO) networked control systems (NCSs) with multi-channel packet disordering. Considering the time-varying and bounded network transmission delay, packet dropout and packet disordering, a novel model of NCSs is proposed by introducing the concept of packet displacement. It is worthwhile mentioned that this model can fully describe the dynamic characteristic of network and always guarantee the newest control input executed by the plant, which makes that the plant can be controlled in real time. The resulting closed-loop systems are jump linear systems due to the newest signals executed subject to Markovian chains. A real-time controller is designed for uncertain and certain NCSs based on Markovian theory combined with linear matrix inequality (LMI) techniques such that the closed-loop cost function value is not more than a specified upper bound that varies according to Quality of Services (QOS). Finally, numerical examples are given to illustrate the effectiveness of the proposed method.     

基于动态规划的约束优化问题多参数规划求解方法及应用

结合动态规划和单步多参数二次规划, 提出一种新的约束优化控制问题多参数规划求解方法. 一方面能得到约束线性二次优化控制问题最优控制序列与状态之间的显式函数关系, 减少多参数规划问题求解的工作量; 另一方面能够同时求解得到状态反馈最优控制律. 应用本文提出的多参数二次规划求解方法, 建立无限时间约束优化问题状态反馈显式最优控制律. 针对电梯机械系统振动控制模型做了数值仿真计算.     

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.     

Time-optimal control for discrete-time hybrid automata

In this paper, the problem of time-optimal control for hybrid systems with discrete-time dynamics is considered. The hybrid controller steers all trajectories starting from a maximal set to a given target set in minimum time. We derive an algorithm that computes this maximal winning set. Also, algorithms for the computation of level sets associated with the value function rather than the value function itself are presented. We show that by solving the reachability problem for the discrete time hybrid automata we obtain the time optimal solution as well. The control synthesis is subject to hard constraints on both control inputs and states. For linear discrete-time dynamics, linear programming and quantifier elimination techniques are employed for the backward reachability analysis. Emphasis is given on the computation of operators for non-convex sets using an extended convex hull approach. A two-tank example is considered in order to demonstrate the techniques of the paper.     

A method of realizing quasi-time optimal control by means of an approximate switching surface†

The optimal switching surface, required for the time optimal control of a linear plant with real poles, is approximated by a polynomial expansion of the state variables. The expansion is obtained by least error squared fitting. Experimental results, obtained by using the approximate switching surface to control a third-order plant, are presented.     

Discretized partial differential equations: Examples of control systems defined on modules

The purpose of this paper is to show how the important problems of linear system theory can be solved concisely for a particular class of linear systems, namely block circulant systems, by exploiting the algebraic structure. This type of system arises in lumped approximations to linear partial differential equations. The computation of the transition matrix, the variation of constants formula, observability, controllability, pole allocation, realization theory, stability and quadratic optimal control are discussed. In principle, all questions which are solved here could also be solved by standard methods; the present paper clearly exposes the structure of the solution, and thus permits various savings in computational effort.     

Worst‐case optimal control for an electrical drive system with time‐delay

This paper considers an optimal control developed for an electrical drive system with a DC motor. Since it is a linear control system with input time‐delay subject to unknown but bounded disturbances, we construct a worst‐case feedback control policy, which can guarantee that, for all admissible uncertain disturbances, the real system state should be in a prescribed neighborhood of a desired value at the given final time, and the cost functional takes the best guarantee value. The worst‐case feedback control policy is allowed to be corrected at a given set of correction points between the initial and the final time, which is equivalent to solving a (m‐1)‐level min‐max problem. Since the min‐max problem at each stage does not yield a simple analytical solution, construction of the optimal policy is computationally prohibitive. This is why we consider an approximate control policy which is more convenient for computation. The simulation results illustrate that this proposed approach is feasible. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

A constructive approach for an optimal control applied to a class of nonlinear time delay systems

In this contribution, we obtain a nonlinear controller for a class of nonlinear time delay systems, by using the inverse optimality approach. We avoid the solution of the Hamilton Jacobi Bellman type equation and the determination of the Bellman's functional by extending the inverse optimality approach for delay free nonlinear systems to time delay nonlinear systems. This is achieved by combining the Control Lyapunov Function framework and Lyapunov-Krasovskii functionals of complete type. Explicit formulas for an optimal control are obtained. The efficiency of the proposed method is illustrated via experimental results applied to a dehydration process whose model includes a delayed state linear part and a delayed nonlinear part. To give evidence of the good performance of the proposed control law, experimental comparison against an industrial Proportional Integral Derivative controller and optimal linear controller. Additionally experimental robustness tests are presented.     

Optimal control of discrete-time linear fractional-order systems with multiplicative noise

A finite horizon linear quadratic (LQ) optimal control problem is studied for a class of discrete-time linear fractional systems (LFSs) affected by multiplicative, independent random perturbations. Based on the dynamic programming technique, two methods are proposed for solving this problem. The first one seems to be new and uses a linear, expanded-state model of the LFS. The LQ optimal control problem reduces to a similar one for stochastic linear systems and the solution is obtained by solving Riccati equations. The second method appeals to the principle of optimality and provides an algorithm for the computation of the optimal control and cost by using directly the fractional system. As expected, in both cases, the optimal control is a linear function in the state and can be computed by a computer program. A numerical example and comparative simulations of the optimal trajectory prove the effectiveness of the two methods. Some other simulations are obtained for different values of the fractional order.     

Compensation of plant variations in optimal control systems

Control of a linear plant, with bounded control input, may be implemented by constructing a control law generator which produces the optimal control as a function of the state variables. If the plant parameters differ from their nominal values, then maintaining optimal control by changing the control law generator is inconvenient since the control law is usually nonlinear. It is shown that in certain cases optimal control can be maintained without changing the control law generator. This is accomplished by using a linear transformation of state variables as the input to the control law generator. The variations of the plant are compensated for by changing the linear transformation. The conditions under which this is possible are established in this paper. The advantage of this system is that a change in a linear function is easier to implement than a change in a nonlinear function. It is shown how this system can be incorporated into an adaptive system which compensates for plant variations.     

一类时延网络控制系统的骱容错保成本控制

针对具有时延和参数不确定性的网络控制系统的研究。考虑系统的时延小于一个采样周期,传感器是时钟驱动,控制器和执行器是事件驱动。当执行器发生故障时,研究网络控制系统的Hoo容错保成本控制。依据所描述情况建立系统模型,基于Lyapunov稳定性理论、容错控制理论和线性矩阵不等式（LMIs）处理方法,推导出网络控制系统是渐近稳定的,并且得出系统的Hoo容错保成本控制的充分条件和系统的保成本上界。实例仿真证明结论的有效性。     

Synthesis of optimal control using neural network with mixed structure

This paper proposes firstly to use a neural network with a mixed structure for learning the system dynamics of a nonlinear plant, which consists of multilayer and recurrent structure. Since a neural network with a mixed structure can learn time series, it can learn the dynamics of a plant without knowing the plant order. Secondly, a novel method of synthesizing the optimal control is developed using the proposed neural network. Procedures are as follows: (1) Let a neural network with a mixed structure learn the unknown dynamics of a nonlinear plant with arbitrary order, (2) after the learning is completed, the network is expanded into an equivalent feedforward multilayer network, (3) it is shown that the gradient of a criterion functional to be optimized can be easily obtained from this multilayer network, and then (4) the optimal control is generated by applying any of the existing non-linear programming algorithm based on this gradient information. The proposed method is successfully applied to the optimal control synthesis problem of a nonlinear coupled vibratory plant with a linear quadratic criterion functional.