带有常数干扰的正则线性系统的镇定

本文考虑带有常数干扰的抽象正则线性系统的状态反馈镇定问题.本文控制设计采用线性系统的动态补偿方法,将传统的PID控制推广到无穷维正则线性系统.通过引入积分作用,控制器可以有效地补偿常数干扰.论文给出了具体的状态反馈法则,并证明了对应闭环系统的指数稳定性.理论结果被应用于带有常数干扰的不稳定热方程,给出了控制器及其闭环系统的指数稳定性,数值仿真验证了本文理论结果的有效性.     

连续时间无穷维正则状态信号系统的最优控制

本文研究连续时间线性无穷维正则状态信号(s/s)系统的最优问题–—线性二次调节器(LQR)最优控制问题和卡 尔曼滤波问题. 正则s/s系统的最优问题可解与正则s/s系统的某个正则i/s/o表示的最优问题可解是等价的. 在正则s/s系 统有一个预解集非空的正则i/s/o表示的前提下, 建立了系统本身的未来最优花费与系统表示的未来最优花费之间的联 系, 并给出了相应的例子.     

连续时间无穷维正则状态信号系统的最优控制（英文）

本文研究连续时间线性无穷维正则状态信号(s/s)系统的最优问题–—线性二次调节器(LQR)最优控制问题和卡尔曼滤波问题.正则s/s系统的最优问题可解与正则s/s系统的某个正则i/s/o表示的最优问题可解是等价的.在正则s/s系统有一个预解集非空的正则i/s/o表示的前提下,建立了系统本身的未来最优花费与系统表示的未来最优花费之间的联系,并给出了相应的例子.     

参数不确定性奇异系统的鲁棒H∞控制

利用线性矩阵不等式,通过引入广义二次可镇定且具有H∞性能指标的概念,得到 了在状态反馈作用下,参数不确定性奇异系统鲁棒H∞控制律的存在条件.所得的状态反馈 控制律保证闭环系统正则、无脉冲、稳定且满足给定的H∞性能指标.     

具有多图拓扑结构的同类智能体LQR控制

本文考虑具有一般线性时不变动态特性的多智能体系统优化控制问题. 将智能体之间的通讯拓扑结构建模成具有自环的无向多图, 每个子系统就是一个节点, 每个节点的控制行为只与本身及邻居节点有关. 由于反馈矩阵具有块对角结构约束, 本文研究的LQR控制问题本质上是一类结构优化问题. 最小化系统LQR性能指标等价于最小化单个智能体性能指标和. 基于线性矩阵不等式得到系统的次优性能指标, 指出LQR性能域是凸集. 由此多智能体系统的LQR控制转化为若干个子系统的LQR控制, 可以通过求解系数是Laplacian矩阵最小最大特征值的两个矩阵不等式得到反馈增益. 数值例子验证了方法的有效性.     

控制能量约束时不稳定过程最优控制与仿真

在最优控制问题的研究中,工业过程控制领域中不稳定系统的控制难度比较大,且在许多控制系统设计中控制能量可能成为重要的性能指标。对控制能量存在约束时不稳定过程的最优控制问题进行了研究与仿真,优化问题的难点在于性能指标的表示与最小化。首先基于不稳定过程的互质分解,由敏感度函数和控制敏感度函数定义了一个包含跟踪误差和控制能量在内的性能指标,采用谱分解最小化该性能指标,从而为不稳定系统导出了一种最优的控制器设计方法,可使系统获得最优的控制性能。仿真结果进一步说明了方法的有效性。     

区间离散广义系统状态反馈鲁棒H∞控制

讨论了一类区间离散广义系统的状态反馈鲁棒H_∞控制问题.在给出区间离散广义系统的等价描述之后,基于系统参数矩阵不等式,得到了问题可解的充分条件,并给出了状态反馈控制器显式表示.所得的控制器保证闭环系统正则,具有因果关系,稳定并且满足给定的H_∞性能指标.数值例子说明了该方法的正确性.     

一类不稳定时滞过程的最优控制

本文对控制能量存在约束条件下一类不稳定时滞过程的最优控制问题进行了探讨. 首先基于不稳定过程的互质分解, 由敏感度函数和控制敏感度函数定义了一个包含跟踪误差和控制能量在内的性能指标, 然后应用谱分解最小化该性能指标, 从而为一类不稳定时滞过程导出了一种最优的控制器设计方法, 可使系统在控制能量存在约束时获得最优的控制性能. 仿真研究进一步说明了该方法的有效性.     

不确定性机械系统的最优鲁棒控制设计: 模糊法

针对不确定性机械系统,提出了一种新的最优鲁棒控制方法.本文用模糊法去描述机械系统中的不确定性.机械系统的性能要求是确定的(保证最低要求),同时也是模糊的(成本控制里用到).所提出的控制方法是确定的,而不是基于假设的规则.经过严格的理论证明,控制系统最终可达到理想的性能指标.基于模糊信息,本文设计了一个性能指标(综合成本,包括系统的平均模糊性能和控制成本).通过最小化此性能指标,可解决控制的最优设计问题.这种最优设计方法可得到唯一的解析形式的最优解.总的来说,这种最优鲁棒控制方法较为系统,能够保证确定的系统性能得以实现,同时控制成本最小.最后,本文选了一个机械系统作为例子.     

广义离散系统的状态反馈鲁棒H∞控制及仿真

针对一类范数有界参数不确定性的广义离散线性系统,研究了该系统的状态反馈鲁棒H∞控制问题.利用线性矩阵不等式(LMI)的方法,得到了问题可解的条件,并给出了相应的状态反馈控制律.在一定条件下,所得的状态反馈鲁棒H∞控制律使广义离散线性系统对所有容许的不确定性参数,能够保证闭环系统正则、具有因果关系并且渐进稳定,同时其传递函数矩阵能够满足给定的H∞性能指标.正常离散线性系统的相对应结果可作为论文结果的特殊形式.仿真例子验证了该方法的正确性.     

Singular ergodic control for multidimensional Gaussian processes

A multidimensional Wiener process is controlled by an additive process of bounded variation. A convex nonnegative function measures the cost associated with the position of the state process, and the cost of controlling is proportional to the displacement induced. We minimize a limiting time-average expected (ergodic) criterion. Under reasonable assumptions, we prove that the optimal discounted cost converges to the optimal ergodic cost. Moreover, under some additional conditions there exists a convex Lipschitz continuous function solution to the corresponding Hamilton-Jacobi-Bellman equation which provides an optimal stationary feedback control.Research supported in part by NSF Grant DMS-8702236.Research supported in part by Grant AFOSR-88-D183.     

Optimal infinite-horizon control and the stabilization of linear discrete-time systems: State-control constraints and nonquadratic cost functions

Stability results are given for a class of feedback systems arising from the regulation of time-invariant, discrete-time linear systems using optimal infinite-horizon control laws. The class is characterized by joint constraints on the state and the control and a general nonlinear cost function. It is shown that weak conditions on the cost function and the constraints are sufficient to guarantee asymptotic stability of the optimal feedback systems. Prior results, which concern the linear quadratic regulator problem, are included as a special case. The proofs make no use of discrete-time Riccati equations and linearity of the feedback law, hence, they are intrinsically different from past proofs.     

Quadratic control of stochastic hybrid systems with renewal transitions

We study the quadratic control of a class of stochastic hybrid systems with linear continuous dynamics for which the lengths of time that the system stays in each mode are independent random variables with given probability distribution functions. We derive a condition for finding the optimal feedback policy that minimizes a discounted infinite horizon cost. We show that the optimal cost is the solution to a set of differential equations with unknown boundary conditions. Furthermore, we provide a recursive algorithm for computing the optimal cost and the optimal feedback policy. The applicability of our result is illustrated through a numerical example, motivated by stochastic gene regulation in biology.     

An algorithm for simultaneous stabilization using decentralizedconstant gain output feedback

An algorithm which solves, numerically, the simultaneous stabilization problem using a constant gain decentralized control law is presented. The algorithm is determined from the necessary conditions for minimizing an optimal control problem. The optimal control problem consists of n plant models each with its own cost function. The costs are summed to create an average cost function and equality constraints are added to yield the decentralized control structure. The algorithm can start with any stabilizing full state feedback gain for each model and will converge to the optimal constant feedback gain for all models assuming a solution exists. Examples of the algorithm are given for Kharitonov synthesis and optimal gain scheduled control law synthesis using output feedback     

Necessary and sufficient conditions for distributed constrained optimal consensus under bounded input

In this paper, we study a distributed constrained optimal consensus problem for discrete‐time first‐order integrator systems under bounded input. Each agent is assigned with a local convex cost function, and all agents are required to achieve consensus at the minimum of the aggregate cost over a common convex constraint set, which is only accessible by part of the agents. A 2‐step control protocol is designed to solve the problem under bounded input. Firstly, at each time step, each agent moves a bounded step of the subgradient descent from an individual cost and another one along the projection direction if it can access the constraint. The second movement is then given by a bounded average of the relative positions to neighbors. Specifically, to coordinate the subgradient step within the network without using global information, we introduce an estimate of the upper bound of all agents' subgradients, which is updated by a unilateral consensus mechanism. Under the given control protocol, we obtain the necessary and sufficient conditions to achieve the constrained optimal consensus for a fixed topology. Under similar conditions, we also solve the problem for switching topologies and conduct a convergence rate analysis for strongly convex costs.     

Asymptotic optimal control of uncertain nonlinear Euler–Lagrange systems

A sufficient condition to solve an optimal control problem is to solve the Hamilton–Jacobi–Bellman (HJB) equation. However, finding a value function that satisfies the HJB equation for a nonlinear system is challenging. For an optimal control problem when a cost function is provided a priori, previous efforts have utilized feedback linearization methods which assume exact model knowledge, or have developed neural network (NN) approximations of the HJB value function. The result in this paper uses the implicit learning capabilities of the RISE control structure to learn the dynamics asymptotically. Specifically, a Lyapunov stability analysis is performed to show that the RISE feedback term asymptotically identifies the unknown dynamics, yielding semi-global asymptotic tracking. In addition, it is shown that the system converges to a state space system that has a quadratic performance index which has been optimized by an additional control element. An extension is included to illustrate how a NN can be combined with the previous results. Experimental results are given to demonstrate the proposed controllers.     

Optimal and suboptimal control for sets of trajectories of deterministic continuous-discrete systems

The optimal control problem for deterministic continuous-discrete systems whose continuous part is described by differential equations and the discrete part, by recurrence equations simulating the control device operation, is considered. Based on sufficient optimality conditions for complete feedback control, algorithms for construction of optimal control with limited set of exact measurements and suboptimal guaranteeing and suboptimal on average controls under uncertainty conditions are proposed. It is shown that for linear continuous-discrete systems with quadratic cost function the optimal open-loop control for the set coincides with the optimal control for one set trajectory, i.e., the suboptimal control is optimal. The application of optimality and suboptimality conditions is demonstrated using examples.     

Wiener-Hopf design of servo-regulator-type multivariable control systems including feedforward compensation

Frequency-domain design in a quadratic cost setting is treated for a multivariable control system which includes disturbance feedforward, output feedback, and reference-input compensation (i.e. a three-degrees-of-freedom control system). The cost function is taken to account for tracking accuracy, plant saturation and plant sensitivity. The class of all controllers is determined for which the given system is internally asymptotically stable and the quadratic cost function is finite. This controller class is parametrized in terms of arbitrary real rational matrices Z_z Z_u and Z_w which are strictly proper and analytic in Re s ≥ 0. The optimal solution is obtained by setting the Z matrices to zero.     

Stabilizing a linear system with Saturation Through optimal control

We construct a continuous feedback for a saturated system x(t)=Ax(t)+B/spl sigma/(u(t)). The feedback renders the system asymptotically stable on the whole set of states that can be driven to 0 with an open-loop control. The trajectories of the resulting closed-loop system are optimal for an auxiliary optimal control problem with a convex cost and linear dynamics. The value function for the auxiliary problem, which we show to be differentiable, serves as a Lyapunov function for the saturated system. Relating the saturated system, which is nonlinear, to an optimal control problem with linear dynamics is possible thanks to the monotone structure of saturation.