Performance bounds for nonlinear systems with a nonlinear ℒ2-gain property

Nonlinear ?₂-gain is a finite gain concept that generalises the notion of conventional (linear) finite ?₂-gain to admit the application of ?₂-gain analysis tools of a broader class of nonlinear systems. The computation of tight comparison function bounds for this nonlinear ?₂-gain property is important in applications such as small gain design. This article presents an approximation framework for these comparison function bounds through the formulation and solution of an optimal control problem. Key to the solution of this problem is the lifting of an ?₂-norm input constraint, which is facilitated via the introduction of an energy saturation operator. This admits the solution of the optimal control problem of interest via dynamic programming and associated numerical methods, leading to the computation of the proposed bounds. Two examples are presented to demonstrate this approach.     

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.     

具有最优时域指标的有界控制器设计

针对SISO线性离散系统,运用线性规划的方法,在控制器输出幅值受约束的条件 下,设计最优控制器,使闭环系统获得最优动态性能.为方便计算,将求取最优解的线性规划 问题转化为一个关于有界变量的BVLP(有界变量线性规划)问题.     

A study of the gap between the structured singular value and itsconvex upper bound for low-rank matrices

The size of the smallest structured destabilizing perturbation for a linear time-invariant system can be calculated via the structured singular value (μ). The function μ can be bounded above by the solution of a convex optimization problem, and in general there is a gap between μ and the convex bound. This paper gives an alternative characterization of μ which is used to study this gap for low-rank matrices. The low-rank characterization provides an easily computed bound which can potentially be significantly better than the standard convex bound. This is used to find new examples with larger gaps than previously known     

Robust performance against time-varying structured perturbations

In this paper, we consider the problem of robust performance analysis for some nominal system M(z) against bounded, linear, time-varying, structured feedback perturbations. We introduce a natural input-output notion of rate-of-variation for a linear time-varying operator. We then exhibit upper and lower bounds on the maximum rate-of-variation of these perturbations against which robust performance is achievable. Using these bounds, we show that the existence of frequency dependent D-scales that render the norm of M(z) less than one is necessary and sufficient for robust performance against arbitrarily slowly varying structured linear perturbations of norm less than one. This result suggests that it is natural and well justified to deal with the frequency-dependent upper bound for the μ “norm,” rather than μ itself. This is reassuring given that the upper bound is easily and reliably computable, while computation of complex μ appears difficult     

BIBO stability robustness in the presence of coprime factorperturbations

A necessary and sufficient condition for robustly stabilizing a family of plants described by perturbations of fixed coprime factors of a plant is given. The computation of the largest stability margin is discussed via solving a nonsquare l¹ optimal control problem. An algorithm for obtaining lower approximations of the minimum value of the optimization problem μ₀ is proposed. This, together with the standard algorithm which provides upper approximations, allows μ₀ to be computed within any degree of accuracy     

Lexicographic perturbation for multiparametric linear programming with applications to control

Optimal control problems for constrained linear systems with a linear cost can be posed as multiparametric linear programs (mpLPs) with a parameter in the cost, or equivalently the right-hand side of the constraints, and solved explicitly offline. Degeneracy occurs when the control input, or optimiser, is non-unique, which can cause chattering of the control input and overlap of the polyhedral regions of the explicit solution. This paper introduces a new and efficient approach to the computation of the solution to a degenerate mpLP with the parameter in the cost. Rather than solve the degenerate problem directly, we solve a lexicographically (symbolically) perturbed version of it that is guaranteed to be non-degenerate. We show that every optimal solution of the perturbed problem is an optimal solution to the original and that the perturbed solution is continuous, unique and defined over a set of non-overlapping polyhedral regions. Furthermore, we introduce a new method for computing the optimal solution in an adjacent region, which is very efficient in all cases and reduces to a single simplex pivot for non-degenerate regions. The proposed algorithm is particularly suited for the calculation of the explicit solution to a class of constrained optimal control problems, since it ensures that the control input is everywhere continuous and unique, thereby removing the danger of chattering in problems with linear costs. The algorithm is compared through example to existing proposals and a significant complexity improvement is demonstrated.     

非线性相似组合大系统最优控制的逐次逼近过程

研究一类仿射非线性相似组合大系统关于二次型性能指标的最优控制问题．首先通过模型简化,将非线性相似组合大系统化为若干个准解耦的子系统;然后利用非线性系统最优控制的逐次逼近设计方法,将求解高阶强耦合的非线性两点边值问题简化为求解一族解耦的线性两点边值问题序列．该线性两点边值问题序列的解一致收敛于非线性相似组合大系统的最优控制,得到的最优控制律由线性最优控制的解析项与非线性补偿序列的极限项组成．通过截取最优控制非线性补偿序列的有限次逼近值．得到了非线性组合大系统的次优控制律．     

The bounded energy optimal control for a class of distributed parameter systems†

The bounded energy optimal control for one-dimensional linear stationary distributed parameter system is solved here. The criterion function is a quadratic functional of the output.

Obtaining the optimal control involves the computation of the solution of a certain non-linear integral equation. The method of solving this integral equation is approximating the kernel of the integral operator by a sequence of degenerate kernels. It is shown that the sequence of approximate solutions of the approximate integral equations converges to the optimal solution; and that the sequence of approximate values of the criterion, converges to the optimal value of the criterion.     

Optimal control of linear distributed-parameter systems via polynomial series

A method for finding the optimal control of linear distributed-parameter systems using polynomial series is discussed. It is known that any polynomial series basis vector can be transformed into a Taylor polynomial by the use of a suitable transformation. In this paper, the optimal control of a distributed-parameter system is simplified into the solution of a linear two-point boundary value problem, and, as a result, the optimal control is obtained via a Taylor series. It is shown that the implementation of Taylor series for this problem involves the use of an ill-conditioned matrix commonly known as the Hilbert matrix. The optimal control of linear distributed-parameter systems using other polynomial series is then calculated by transforming the properties of the Taylor series into other polynomial series. The formulation is straightforward and convenient for digital computation. An illustrative example is given.     

A method for decomposing mixed-integer linear programs with staircase structure

An algorithm is presented for solving mixed-integer linear programs with a staircase structure. The basic idea of the algorithm is to decompose the original problem into a series form of small-scale mixed-integer problems. For each problem decomposed, the solution is obtained by the conventional branch-and-bound method. In this algorithm the feasibility of the solution is always assured, but in order to save computation time the optimality condition is checked restrictedly for the solution obtained. The difference between the optimal objective value and the objective value obtained can be estimated. By examining numerical results, it is observed that the algorithm is efficient, requiring less computation time than other methods.     

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.     

Parallel,iterative solution of sparse linear systems: Models and architectures

Solving large, sparse, linear systems of equations is a fundamental problems in large scale scientific and engineering computation. A model of a general class of asynchronous, iterative solution methods for linear systems is developed. In the model, the system is solved by creating several cooperating tasks that each compute a portion of the solution vector. A data transfer model predicting both the probability that data must be transferred between two tasks and the amount of data to be transferred is presented. This model is used to derive an execution time model for predicting parallel execution time and an optimal number of tasks given the dimension and sparsity of the coefficient matrix and the costs of computation, synchronization, and communication.The suitability of different parallel architectures for solving randomly sparse linear systems is discussed. Based on the complexity of task scheduling, one parallel architecture, based on a broadcast bus, is presented and analyzed.     

输出概率密度函数鲁棒弹性最优跟踪控制

研究了一类随机动态系统的鲁棒弹性最优跟踪控制问题。在采用B样条神经网络模型逼近随机动态系统的输出概率密度函数(PDF)的基础上,同时考虑系统模型和控制器增益不确定性,结合Lyapunov稳定性理论和线性矩阵不等式(LMI)技术,引入增广控制作用,设计基于广义状态反馈的鲁棒弹性最优跟踪控制器,目的是使系统的输出PDF跟踪给定PDF。通过求解LMI,所得控制器不仅能实现跟踪目的,而且能确保该随机动态系统全局稳定并满足一定的线性二次型性能指标上界。仿真结果表明该方法简单易行,且无需任何设计参数调整。     

正定二次最优控制问题的最优值的估计

众多实际应用中,人们常用正定二次规划方法对正定二次最优控制问题的最优值进行逼近估计.为简化计算和加快计算速度,本文设计了一种新的算法,利用线性规划代替二次规划.先用最优控制方法构造一个线性规划,使其最优值与给定二次规划相差一个容易确定的常数.由于线性规划具有形式简单和算法成熟等特点,本文的方法就简化了正定二次最优控制问题的最优值的估计,并加快了计算速度.文中给出一个例子说明这一计算过程,并讨论了计算速度.同时,本文的最后部分,利用参数规划,给出了一个供实际应用的具体算法.     

The Nehari problem in systems with distributed input delays is inherently finite dimensional

The Nehari problem is considered in the context of linear systems with distributed input (or, equivalently, output) lags. It is shown that both the computation of the optimal value and the parametrization of suboptimal solutions reduce to matrix computations in an associated undelayed Nehari problem. The solution is then given in terms of a system of Neutral type.     

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.     

Properties of the mixed μ problem and its bounds

Upper and lower bounds for the mixed μ problem have recently been developed, and here we examine the relationship of these bounds to each other and to μ. A number of interesting properties are developed and the implications of these properties for the robustness analysis of linear systems and the development of practical computation schemes are discussed. In particular we find that current techniques can only guarantee easy computation for large problems when μ equals its upper bound, and computational complexity results prohibit this possibility for general problems. In this context we present some special cases where computation is easy and make some direct comparisons between mixed μ and “Kharitonov-type” analysis methods     

Convergence of relaxation methods for two-point boundary-value linear problems

The solution of linear differential problems, with explicit two-point boundary conditions, can sometimes be obtained by a relaxation method of computation. This paper shows that the convergence of iterations is linked to the spectral radius value of an integral operator. The equivalence between eigenvalues research and critical lengths calculation of a differential system is demonstrated. In this context, we present a case of optimal control law calculation of a linear system. The efficiency of this preliminary convergence calculation is illustrated by a numerical example.