Stability analysis of randomly sampled digital control systems

Random sampling in digital control systems has characteristically been avoided by development of precise timing and computational cycles even though examples are known in which the randomness aids system stability. Generally, attempts to determine the effects of relaxing these rigid timing requirements have been hampered by the lack of a convenient analysis technique for this class of problems. In order to address these problems, a method for the analysis of expected system response is developed and applied to a typical spacecraft digital control system design.     

Optimum filtering and control of randomly sampled systems

Kalman's concept of optimum filtering is interpreted in such a way that it can be applied to both nonlinear and linear systems with Gaussian or non-Gaussian statistics. The essential idea is to synthesize a generalized Kalman filter in two stages, a) propagation and b) measurement and correction. The analysis of each stage is independent of the other, and the generalized results are quite simple. In the present paper, the application of the above result is confined to linear systems: 1) Optimum filtering and interpolation of randomly sampled signals; for the interpolation problem it is assumed that the signals are measured exactly at the sampling instant. 2) Optimum filtering of related continuous and randomly sampled signals. 3) Optimum control of randomly sampled linear systems with quadratic cost criterion.     

A discrete optimal control problem

A certain class of discrete optimization problems is investigated using the framework of nonlinear programming. It is shown that a discrete maximum principle similar to the Pontryagin maximum principle is valid for a subclass of these problems, specifically systems with linear dynamics, convex inequality constraints and convex performance criteria. This result extends the applicability of the discrete maximum principle to a class of problems not covered by the Rozonoer-Halkin formulation.     

Synthesis of digital optimal reduced-order compensators for asynchronously sampled systems

The synthesis of finite-horizon digital optimal reduced-order compensators is presented, for asynchronous and aperiodically sampled continuous-time systems. The dimensions of the compensator state are a priori fixed and may be time varying. Asynchronous and aperiodic sampling refers to a deterministic sampling scheme where an arbitrary, but a priori known, number of control variables is updated, and/or an arbitrary, but a priori known, number of outputs is sampled, at arbitrary, but a priori known, time instants. This sampling scheme generalizes most deterministic sampling schemes considered in the control literature. Through the use of an integral criterion the intersample behaviour is explicitly considered in the design. As a result, frequent, synchronous and periodic sampling is no longer necessary, which can be highly relevant in practice. Also the synthesis enables comparison of the optimal performance of reduced-order compensators as a function of their dimensions and the sampling scheme. The synthesis is illustrated with a numerical example.     

Application of discrete Chebyshev polynomials to the optimal control of digital systems

The shift transformation matrix for discrete Chebyshev polynomials is introduced in this study. The discrete variational principle combined with the idea of penalty function is taken to construct the modified discrete Euler-Lagrange equations. Then, the discrete Chebyshev series are applied to simplify the modified equations into a set of linear algebraic ones for the approximations of state and control variables of digital systems. It is seen that this technique is quite straightforward and simple, and computing time can be saved considerably.     

Singular linear-quadratic optimal control problem for a class of discrete singular systems with multiple time-delays

The singular linear-quadratic optimal control problem for a class of discrete-time linear singular systems with multiple time-delays is investigated. The equivalent relationship between this problem and the non-singular linear-quadratic problem for standard state-space discrete-time linear systems without time-delay is derived, and the necessary and sufficient condition guaranteeing this equivalent relationship is also given. The optimal control is synthesized as state feedback, and the closed-loop system is regular, impulse-free and without time-delay. An example is given to show the validity of the proposed method.     

Asymptotic solution of a discrete optimal control problem for one class of weakly controllable systems

An asymptotic expansion in integer nonnegative powers of small parameter for a solution of a discrete optimal control problem for one class of weakly controllable systems is constructed by substituting an assumed asymptotic expansion into the problem conditions and obtaining a series of problems in the coefficients of the asymptotics. Conditions of existence of a solution to the perturbed problem for sufficiently small values of the parameter are found. Estimates of closeness of the approximate and exact solutions in terms of trajectory, control, and functional are obtained. The values of the minimized functional are proven not to increase when higher-order asymptotic approximations of the optimal control are used. The discussion is illustrated by examples.     

A graph-theoretic optimal control problem for terminating discrete event processes

Most of the results to date in discrete event supervisory control assume a zero-or-infinity structure for the cost of controlling a discrete event system, in the sense that it costs nothing to disable controllable events while uncontrollable events cannot be disabled (i.e., their disablement entails infinite cost). In several applications however, a more refined structure of the control cost becomes necessary in order to quantify the tradeoffs between candidate supervisors. In this paper, we formulate and solve a new optimal control problem for a class of discrete event systems. We assume that the system can be modeled as a finite acylic directed graph, i.e., the system process has a finite set of event trajectories and thus is terminating. The optimal control problem explicitly considers the cost of control in the objective function. In general terms, this problem involves a tradeoff between the cost of system evolution, which is quantified in terms of a path cost on the event trajectories generated by the system, and the cost of impacting on the external environment, which is quantified as a dynamic cost on control. We also seek a least restrictive solution. An algorithm based on dynamic programming is developed for the solution of this problem. This algorithm is based on a graph-theoretic formulation of the problem. The use of dynamic programming allows for the efficient construction of an optimal subgraph (i.e., optimal supervisor) of the given graph (i.e., discrete event system) with respect to the cost structure imposed. We show that this algorithm is of polynomial complexity in the number of vertices of the graph of the system.Research supported in part by the National Science Foundation under grant ECS-9057967 with additional support from GE and DEC.     

Optimization of non-uniformly sampled discrete systems

This paper deals with a general method for the optimization of multivariable systems with non-uniform sampling chosen in an optimal way. The algorithm is based on Dynamic Programming and is applied to extend the Kalman-Koepcke optimization method for a linear dynamic system, with a quadratic performance index. In the general multivariable case, the method implies the solution of a two-point boundary value problem. In the scalar case, which has only one input and one output, the sampling periods may be found separately from the control law. The computational difficulties connected with the implementation of the method are discussed and simplifications, possible for discretized continuous systems, are derived. Numerical examples are given.     

The design of stochastic sampled data control systems containing a discrete actuator

A numerical method is outlined for synthesizing optimum impulse compensators in the mean-square error sense for feedback systems containing a coarse quantization actuator element where thesigma/qratio must be specified not only to ensure a valid estimate of the quantization error but also to limit the delay errors caused by the finite rate of actuator operation.     

受扰线性离散系统的前馈2反馈最优控制

研究具有已知动态特性但未知初始条件的持续外界扰动的线性离散系统最优控制问题。给出了前馈一反馈最优控制律的存在唯一性条件，并提出了最优控制律的设计算法．通过降维扰动观测器解决了前馈一反馈最优控制律的物理不可实现问题．对近海结构物振动控制的实例仿真表明，该设计算法易于实现，在抑制外部持续扰动和鲁棒性方面优于经典的状态反馈最优控制。     

Dissipative control for state-saturated discrete time-varying systems with randomly occurring nonlinearities and missing measurements

In this paper, the dissipative control problem is investigated for a class of discrete time-varying systems with simultaneous presence of state saturations, randomly occurring nonlinearities as well as multiple missing measurements. In order to render more practical significance of the system model, some Bernoulli distributed white sequences with known conditional probabilities are adopted to describe the phenomena of the randomly occurring nonlinearities and the multiple missing measurements. The purpose of the addressed problem is to design a time-varying output-feedback controller such that the dissipativity performance index is guaranteed over a given finite-horizon. By introducing a free matrix with its infinity norm less than or equal to 1, the system state is bounded by a convex hull so that some sufficient conditions can be obtained in the form of recursive nonlinear matrix inequalities. A novel controller design algorithm is then developed to deal with the recursive nonlinear matrix inequalities. Furthermore, the obtained results are extended to the case when the state saturation is partial. Two numerical simulation examples are provided to demonstrate the effectiveness and applicability of the proposed controller design approach.     

Synthesis of digital control systems using discrete Walsh polynomials

The use of discrete Walsh polynomials to synthesize a digital control system and determine suitable internal system structure from its prescribed external (input/output) behaviour is studied in this paper. Algorithms for the synthesis of discrete system transfer functions and state equations are presented. The discrete Walsh approach method transforms the original model difference equations into computationally convenient algebraic forms. Two examples in which a transfer function and a state equation are synthesized illustrate the discrete Walsh technique.     

Equivalent optimal compensation problem in the delta domain for systems with white stochastic parameters

The equivalent representation in the delta domain of the digital optimal compensation problem is provided and computed in this article. This problem concerns finding digital optimal full and reduced-order output feedback controllers for linear time-varying and time-invariant systems with white stochastic parameters. It can subsequently be solved in the delta domain using the strengthened optimal projection equations that we recently formulated in this domain as well. If the sampling rate becomes high, stating and solving the problem in the delta domain becomes necessary because the conventional discrete-time problem formulation and solution become ill-conditioned. In this article, by means of several numerical examples and compensator implementations, we demonstrate this phenomenon. To compute and quantify the improved performance when the sampling rate becomes high, a new delta-domain algorithm is developed. This algorithm computes the performance of arbitrary digital compensators for linear systems with white stochastic parameters. The principle application concerns nonconservative robust digital perturbation feedback control of nonlinear systems with high sampling rates.     

A benchmark for optimal control problem solvers for hybrid nonlinear systems

In this paper, a benchmark problem is proposed in order to assess comparisons between different optimal control problem solvers for hybrid nonlinear systems. The model is nonlinear with 20 states, 4 continuous controls, 1 discrete binary control and 4 configurations. Transitions between configurations lead to state jumps. The system is inspired by the simulated moving bed, a counter-current separation process.     

参数未知的离散系统Q-学习优化状态估计与控制

控制系统的应用中存在状态不能直接测量或测量成本高的实际问题,给模型参数未知的系统完全利用状态数据学习最优控制器带来挑战性难题.为解决这一问题,首先构建具有状态观测器且系统矩阵中存在未知参数的离散线性增广系统,定义性能优化指标;然后基于分离定理、动态规划以及Q-学习方法,给出一种具有未知模型参数的非策略Q-学习算法,并设计近似最优观测器,得到完全利用可测量的系统输出和控制输入数据的非策略Q-学习算法,实现基于观测器状态反馈的系统优化控制策略,该算法的优点在于不要求系统模型参数全部已知,不要求系统状态直接可测,利用可测量数据实现指定性能指标的优化;最后,通过仿真实验验证所提出方法的有效性.     

离散约束系统最优控制中的内点法

对于带约束的力学系统的最优控制,约束系统离散力学最优控制（Discrete Mechanics and Optimal Control for Constrained Systems,DMOCC）采用了“先离散,后变分”的方法,结合离散零空间法,能很好地保持系统的物理特性,其模型方程可表示为非线性等式约束的优化问题,通常采用标准序列二次规划（Sequence Quadratic Program,SQP）算法求解。由于约束条件的规模大,SQP算法的计算效率不高。相对于SQP,内点法具有收敛性好、稳定性强的特点。在对DMOCC约束条件的特点进行分析之后,将内点法用于DMOCC的数学模型进行数值计算,能有效提高计算效率。曲柄滑块的数值仿真证明了在数值精度一致的情况下,内点法具有效率上的优势。