An approximate minimal time closed-loop controller for processes with bounded control amplitudes and rates

This paper presents a method for designing an approximate minimal time closed-loop controller for linear systems with bounded control amplitudes and rates. The method is based on obtaining an approximate functional expression (explicitly in terms of the state variables) that describes the minimal time isochrones of the system. This expression is obtained by a series of least-squares fits to the computed system states on the various isochrones. The computation of the system states on the isochrones and the determination of the approximate expressions are achieved off-line. For on-line operation, it is only required to store a limited number of coefficients of the approximate expressions, and to compute the closed-loop control function by some algebraic manipulation. Consequently, the on-line computer storage requirement as well as the on-line computation requirement is relatively small. Thus, the method is feasible for high-order practical systems. To evaluate its usefulness in applications, the scheme is used to design a fourth-order Ranger Block III Attitude Control System. The results are compared to those obtained by applying the minimal time open-loop control.     

一类不确定非线性系统的自适应输出反馈镇定

研究了一类不确定非线性系统的全局自适应输出反馈镇定问题. 由于不确定控制系数和未知线性增长率的存在, 这个问题比较复杂且很难解决. 本文引入一个新的在线调节的动态增益, 并基于此设计了高增益K-滤波器用于重构系统的状态. 然后, 受广义控制方法的启发, 发展了反推法并设计了自适应输出反馈镇定控制器. 结果表明, 通过选择恰当的设计参数可以保证闭环系统的全局稳定性. 给出的仿真算例验证了本文理论结果的正确性.     

MPC of constrained discrete-time linear periodic systems — A framework for asynchronous control: Strong feasibility, stability and optimality via periodic invariance

State-feedback model predictive control (MPC) of discrete-time linear periodic systems with time-dependent state and input dimensions is considered. The states and inputs are subject to periodically time-dependent, hard, convex, polyhedral constraints. First, periodic controlled and positively invariant sets are characterized, and a method to determine the maximum periodic controlled and positively invariant sets is derived. The proposed periodic controlled invariant sets are then employed in the design of least-restrictive strongly feasible reference-tracking MPC problems. The proposed periodic positively invariant sets are employed in combination with well-known results on optimal unconstrained periodic linear-quadratic regulation (LQR) to yield constrained periodic LQR control laws that are stabilizing and optimal. One motivation for systems with time-dependent dimensions is efficient control law synthesis for discrete-time systems with asynchronous inputs, for which a novel modeling framework resulting in low dimensional models is proposed. The presented methods are applied to a multirate nano-positioning system.     

The explicit linear quadratic regulator for constrained systems

For discrete-time linear time invariant systems with constraints on inputs and states, we develop an algorithm to determine explicitly, the state feedback control law which minimizes a quadratic performance criterion. We show that the control law is piece-wise linear and continuous for both the finite horizon problem (model predictive control) and the usual infinite time measure (constrained linear quadratic regulation). Thus, the on-line control computation reduces to the simple evaluation of an explicitly defined piecewise linear function. By computing the inherent underlying controller structure, we also solve the equivalent of the Hamilton-Jacobi-Bellman equation for discrete-time linear constrained systems. Control based on on-line optimization has long been recognized as a superior alternative for constrained systems. The technique proposed in this paper is attractive for a wide range of practical problems where the computational complexity of on-line optimization is prohibitive. It also provides an insight into the structure underlying optimization-based controllers.     

Design of receding horizon controls for constrained time-varying systems

In this note, we propose a generalized stabilizing receding horizon control scheme for input/state constrained linear discrete time-varying systems that improves feasibility and on-line computation on the constrained finite-horizon optimization problem, compared with existing schemes. The control scheme is based on a time-varying horizon cost function with time-varying terminal weighting matrices, which can easily be implemented via linear matrix inequality technique. We discuss modifications of the proposed scheme that improve feasibility or on-line computation time. Through simulation examples, we illustrate the results of these schemes.     

Lower order control for systems with fast and slow modes

Given a stabilizing control for a linear system with fast and slow subsystems, a lower order control as a function of slow states alone is designed to give a first-order approximation of the original quadratic performance index, which is not necessarily minimized by the given stabilizing full-order control.     

A robust model predictive control algorithm for incrementally conic uncertain/nonlinear systems

This paper presents a robustly stabilizing model predictive control algorithm for systems with incrementally conic uncertain/nonlinear terms and bounded disturbances. The resulting control input consists of feedforward and feedback components. The feedforward control generates a nominal trajectory from online solution of a finite‐horizon constrained optimal control problem for a nominal system model. The feedback control policy is designed off‐line by utilizing a model of the uncertainty/nonlinearity and establishes invariant ‘state tubes’ around the nominal system trajectories. The entire controller is shown to be robustly stabilizing with a region of attraction composed of the initial states for which the finite‐horizon constrained optimal control problem is feasible for the nominal system. Synthesis of the feedback control policy involves solution of linear matrix inequalities. An illustrative numerical example is provided to demonstrate the control design and the resulting closed‐loop system performance. Copyright © 2010 John Wiley & Sons, Ltd.     

Stabilizable regions of receding horizon predictive control with input constraints

Stabilizable regions of receding horizon predictive control (RHPC) with input constraints are examined. A feasible region of states, which is spanned by eigenvectors of the closed-loop system with a stabilizing feedback gain, is derived in conjunction with input constraints. For states in this region, the feasibility of state feedback is guaranteed with the corresponding feedback gain. It is shown that an RHPC scheme with adequate finite terminal weights can guarantee stability for any initial state which can be steered into this region using finite number of control moves in the presence of input saturation. This methodology results in feasible regions which are infinite (in certain directions) even in the case of open-loop unstable systems. It is shown that the proposed feasible regions are larger than the ellipsoidal regions which were suggested in earlier works. We formulated the optimization problem in LMI so that it can be solved by semidefinite programming.     

Adaptive stabilization of a class of nonlinear systems using high-gain feedback

This note presents a high-gain feedback stabilizing control algorithm in which the high-gain parameter is adapted on-line. The algorithm is developed for a class of nonlinear systems which can be viewed as the nonlinear counterpart of uniform rank systems. The system can be unknown except for a number of vital pieces of information. For single-input single-output linear systems such information is usually required in the traditional adaptive control literature.     

Immersion and invariance: a new tool for stabilization and adaptive control of nonlinear systems

A new method to design asymptotically stabilizing and adaptive control laws for nonlinear systems is presented. The method relies upon the notions of system immersion and manifold invariance and, in principle, does not require the knowledge of a (control) Lyapunov function. The construction of the stabilizing control laws resembles the procedure used in nonlinear regulator theory to derive the (invariant) output zeroing manifold and its friend. The method is well suited in situations where we know a stabilizing controller of a nominal reduced order model, which we would like to robustify with respect to higher order dynamics. This is achieved by designing a control law that asymptotically immerses the full system dynamics into the reduced order one. We also show that in adaptive control problems the method yields stabilizing schemes that counter the effect of the uncertain parameters adopting a robustness perspective. Our construction does not invoke certainty equivalence, nor requires a linear parameterization, furthermore, viewed from a Lyapunov perspective, it provides a procedure to add cross terms between the parameter estimates and the plant states. Finally, it is shown that the proposed approach is directly applicable to systems in feedback and feedforward form, yielding new stabilizing control laws. We illustrate the method with several academic and practical examples, including a mechanical system with flexibility modes, an electromechanical system with parasitic actuator dynamics and an adaptive nonlinearly parameterized visual servoing application.     

An LMI based scheduling algorithm for constrained stabilization problems

We propose a gain-scheduling control algorithm for locally stabilizing a discrete-time linear system with input saturation. The proposed control law has a structure that a high-gain control law and a low-gain control law are interpolated by using a single scheduling parameter. The scheduling parameter is computed on-line by solving a convex optimization problem with LMI constraints.     

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.     

Stabilizing a class of nonlinear parameter-varying systems using interval observer based on a piecewise framework

This article addresses an interval observer-based control for stabilizing a class of nonlinear parameter-varying systems with noisy output by designing a switching surface. An input-dependent interval observer is firstly developed to estimate the lower and upper bounds of the states. Next, a switching-based controller is designed to stabilize the interval observer which implies the stability of the main parameter-varying system. The developed stabilizing switching surfaces are designed based on the outputs of the main system and the bounds of the states of the observer. By choosing an appropriate piecewise Lyapunov function, the closed-loop stability analysis of the interval observer system leads to a set of linear matrix inequalities including stability and Metzler constraints, simultaneously. The effectiveness of the proposed method is verified using the simulation results.     

A NEURAL NETWORK APPROACH TO ON-LINE IDENTIFICATION OF NONLINEAR SYSTEMS

Research investigating neural identification of dynamic systems has concentrated on off-line techniques. To be suitable for adaptive process control, on-line algorithms must be developed. This study investigates a modified back-propagation technique to achieve practical on-line capability. A technique denoted history-stack enhancement greatly improves the identification performance of the neural network. As a demonstration, a composite system of formidable but realistic nonlinear components was constructed and used to compare identification techniques including a recursive linear estimator and die new neural method. The results show that on-line neural identification is feasible for a wide range of formidable nonlinear characteristics individually found in industrial processes. Although performance is slower than with linear identification, the asymptotic accuracy of the neural technique is better for the nonlinear system being identified.     

Stabilization of a class of nonlinear systems by adaptive output feedback

In recent years, several results have been proposed on global stabilization of a class of nonlinear systems that are dominated by a triangular system satisfying the linear growth condition. However, in these works, at least the linear growth bound is assumed to be known in designing stabilizing controllers. In our work, we propose an adaptive output feedback control scheme in which the high-gain parameter is tuned on-line. Thus, a priori knowledge on the linear growth bound of system nonlinearities is not required in our scheme.     

时变时滞不确定系统的鲁棒输出反馈控制

研究了时变时滞不确定系统基于状态观测器的动态输出反馈实现鲁棒镇定的分 析和综合问题.所研究的系统不仅同时包含时变状态时滞和时变控制时滞,而且包含时变未 知且有界不确定参数.提出了确保该系统可通过输出反馈鲁棒镇定的充分条件,并将该充分 条件转化为线性矩阵不等式(LMI)问题,最终通过求解两个LMI来构造输出反馈控制律.     

基于高阶观测器和干扰补偿控制的模型预测控制方法

针对状态不可测、外部干扰未知, 并且状态和输入受限的离散时间线性系统, 将高阶观测器、干扰补偿控制与标准模型预测控制(Model predictive control, MPC)相结合, 提出了一种新的MPC方法. 首先利用高阶观测器同步观测未知状态和干扰, 使得观测误差一致有界收敛;然后基于该干扰估计值设计新的干扰补偿控制方法, 并将该方法与基于状态估计的标准MPC相结合, 实现上述系统的优化控制. 所提出的MPC方法克服了利用现有MPC方法求解具有外部干扰和状态约束的优化控制问题时存在无可行解的局限, 能够保证系统状态在每一时刻都满足约束条件, 并且使系统的输出响应接近采用标准MPC方法控制线性标称系统时得到的输出响应. 最后, 将所提控制方法应用到船舶航向控制系统中, 仿真结果表明了所提方法的有效性和优越性.     

Decentralized stabilization of large electric power systems

This paper considers the problem of stabilizing the dynamics of a large electric power system, represented by a linear time invariant system of equations, by using several decentralized (or local) state feedback control laws. The stabilization problem is formulated into a functional minimization problem which implicitly controls the closed-loop eigenvalues of the controlled system. The constraint of decentralization is tackled in the minimization algorithm by using the method of feasible directions. To illustrate the application of the algorithm, it is used to stabilize a three machine electric power system.     

On linear programming and robust model-predictive control using impulse-responses

Campo and Morari have derived a linear programming problem, with a potentially large number of constraints, which is equivalent to a min-max formulation for robust model-predictive control of linear systems. That formulation involves minimization, with respect to the controls, of the maximum, with respect to the system's impulse response (from a set of possible impulse responses), of the infinity norm of the error between the predicted and required system output sequences. Here an alternative linear programming problem is derived which has a smaller number of constraints and is therefore potentially more convenient for on-line control.     

约束多传感器线性系统的RMPC容错控制

针对一类约束多传感器线性故障系统,提出了一种基于鲁棒预测控制策略的容错控制方案．首先为多传感器线性系统设计了观测器,然后离线设计不变集列,使得时变的状态估计误差存在于相应的不变集列中,利用不变集的理论提出了一种新的故障检测的方法,最后基于鲁棒预测控制策略为故障系统设计了容错控制器,给出了闭环系统鲁棒稳定性的证明．仿真结果证明了方法的可行性。