Parametric optimal control for uncertain linear quadratic models

In recent few decades, linear quadratic optimal control problems have achieved great improvements in theoretical and practical perspectives. For a linear quadratic optimal control problem, it is well known that the optimal feedback control is characterized by the solution of a Riccati differential equation, which cannot be solved exactly in many cases, and sometimes the optimal feedback control will be a complex time-oriented function. In this paper, we introduce a parametric optimal control problem of uncertain linear quadratic model and propose an approximation method to solve it for simplifying the expression of optimal control. A theorem is given to ensure the solvability of optimal parameter. Besides, the analytical expressions of optimal control and optimal value are derived by using the proposed approximation method. Finally, an inventory-promotion problem is dealt with to illustrate the efficiency of the results and the practicability of the model.     

Stochastic linear quadratic regulation for discrete-time linear systems with input delay

This paper considers the stochastic LQR problem for systems with input delay and stochastic parameter uncertainties in the state and input matrices. The problem is known to be difficult due to the presence of interactions among the delayed input channels and the stochastic parameter uncertainties in the channels. The key to our approach is to convert the LQR control problem into an optimization one in a Hilbert space for an associated backward stochastic model and then obtain the optimal solution to the stochastic LQR problem by exploiting the dynamic programming approach. Our solution is given in terms of two generalized Riccati difference equations (RDEs) of the same dimension as that of the plant.     

On the discrete and continuous time infinite-dimensional algebraic Riccati equations

The standard state space solution of the finite-dimensional continuous time quadratic cost minimization problem has a straightforward extension to infinite-dimensional problems with bounded or moderately unbounded control and observation operators. However, if these operators are allowed to be sufficiently unbounded, then a strange change takes place in one of the coefficients of the algebraic Riccati equation, and the continuous time Riccati equation begins to resemble the discrete time Riccati equation. To explain why this phenomenon must occur we discuss a particular hyperbolic PDE in one space dimension with boundary control and observation (a transmission line) that can be formulated both as a discrete time system and as a continuous time system, and show that in this example the continuous time Riccati equation can be recovered from the discrete time Riccati equation. A particular feature of this example is that the Riccati operator does not map the domain of the generator into the domain of the adjoint generator, as it does in the standard case.     

二次型最优控制问题中的权矩阵与最优控制律

二次型最优的权矩阵选择是一个包含大量经验与技巧的问题．对此，研究权矩阵与最优控制律的关系：一个最优控制律，其对应的性能指标（权矩阵）是否唯一的问题．不论是单输入还是多输入情形，在系统能控性指数大于2，对应权矩阵的对角线元素有2个不为零的元素，且满足一定条件时．该性能指标对应的最优控制律，必有另一性能指标与之对应．     

Solvability of indefinite stochastic Riccati equations and linear quadratic optimal control problems

A new approach to study the indefinite stochastic linear quadratic (LQ) optimal control problems, which we called the “equivalent cost functional method”, is introduced by Yu (2013) in the setup of Hamiltonian system. On the other hand, another important issue along this research direction, is the possible state feedback representation of optimal control and the solvability of associated indefinite stochastic Riccati equations. As the response, this paper continues to develop the equivalent cost functional method by extending it to the Riccati equation setup. Our analysis is featured by its introduction of some equivalent cost functionals which enable us to have the bridge between the indefinite and positive-definite stochastic LQ problems. With such bridge, some solvability relation between the indefinite and positive-definite Riccati equations is further characterized. It is remarkable the solvability of the former is rather complicated than the latter, hence our relation provides some alternative but useful viewpoint. Consequently, the corresponding indefinite linear quadratic problem is discussed for which the unique optimal control is derived in terms of state feedback via the solution of the Riccati equation. In addition, some example is studied using our theoretical results.     

A Riccati approach for constrained linear quadratic optimal control

An active-set method is proposed for solving linear quadratic optimal control problems subject to general linear inequality path constraints including mixed state-control and state-only constraints. A Riccati-based approach is developed for efficiently solving the equality constrained optimal control subproblems generated during the procedure. The solution of each subproblem requires computations that scale linearly with the horizon length. The algorithm is illustrated with numerical examples.     

Self-triggered linear quadratic control

Self-triggered control is a recently proposed paradigm that abandons the more traditional periodic time-triggered execution of control tasks with the objective of reducing the utilization of communication resources, while still guaranteeing desirable closed-loop behavior. In this paper, we introduce a self-triggered strategy based on performance levels described by a quadratic discounted cost. The classical LQR problem can be recovered as an important special case of the proposed self-triggered strategy. The self-triggered strategy proposed in this paper possesses three important features. Firstly, the control laws and triggering mechanisms are synthesized so that a priori chosen performance levels are guaranteed by design. Secondly, they realize significant reductions in the usage of communication resources. Thirdly, we address the co-design problem of jointly designing the feedback law and the triggering condition. By means of a numerical example, we show the effectiveness of the presented strategy. In particular, for the self-triggered LQR strategy, we show quantitatively that the proposed scheme can outperform conventional periodic time-triggered solutions.     

Robust stabilization and guaranteed cost control for discrete-time linear systems by static output feedback

This paper proposes a systematic approach for the static output feedback control design for discrete-time uncertain linear systems. It is shown that if the open-loop system satisfies some particular structural conditions and the uncertainty has a specific structure, a static output feedback gain can be calculated easily, using a formula only involving the original system matrices. Among the conditions the system has to satisfy, the strongest one relies on a minimum phase argument. Square and nonsquare systems are considered. The performance problem through a quadratic criterion is also discussed (guaranteed cost control).     

Infinite horizon linear quadratic optimal control for discrete‐time stochastic systems

This paper is concerned with the infinite horizon linear quadratic optimal control for discrete‐time stochastic systems with both state and control‐dependent noise. Under assumptions of stabilization and exact observability, it is shown that the optimal control law and optimal value exist, and the properties of the associated discrete generalized algebraic Riccati equation (GARE) are also discussed. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Indefinite linear quadratic optimal control problem for singular discrete-time system with multiple input delays

An indefinite linear quadratic (ILQ) optimal control problem is discussed for singular discrete-time-varying linear systems with multiple input delays. The problem is transformed to the one for standard systems by normalizability decomposition. An explicit controller is obtained by computing the gain of the smoothing estimation of dual systems. Necessary and sufficient conditions guaranteeing the existence of unique solution are given simultaneously. A numerical example illustrates the presented method.     

鲁棒稳定性对最优二次型控制设计的约束

高品质反馈系统中,对象的模型不确定性给最优控制设计带来许多困难.本文针对车载"动中通"天线伺服系统,研究了一种内模扩展的线性二次调节器(LQR)最优控制设计方法.根据卡尔曼等式和小增益定理,给出了系统的鲁棒稳定性对控制器设计参数的约束条件,以及鲁棒稳定裕量与二次最优性能指标参数的定量关系.最后通过MATLAB仿真和实际系统实验,验证了控制器的有效性.     

有限时间信息融合线性二次型最优控制

针对有限时间线性二次型最优控制问题, 提出了一种新的求解方法—–信息融合估计方法. 基于线性最小方差估计准则下的融合估计理论, 通过融合期望状态轨迹、理想控制策略等软约束信息, 分别采用集中式融合和序贯式融合两种信息处理方法, 求得最优状态调节器问题的最优融合控制序列. 进一步从理论上论证了序贯式融合控制方法与传统最优控制方法的一致性, 并通过直流电机系统的数值仿真也验证了集中式和序贯式融合控制方法 与传统最优控制方法的等效性, 从而统一了最优估计与最优控制问题, 并为最优控制问题提供了一种新的求解方法.     

An algorithm for optimal decentralized regulation of linear quadratic interconnected systems

This paper provides a method for designing ‘optimal’ decentralized controls for linear time-invariant interconnected systems. The optimality is with respect to a quadratic cost criterion. An algorithm is proposed which uses the method of feasible directions and provides a local minimum while ensuring, at each iteration, the stability of the overall system.     

Stochastic linear quadratic adaptive control for continuous-time first-order systems

In this paper, we discuss the linear quadratic (LQ) adaptive control problem for the following continuous-time first-order scalar stochastic system: dx_t = ax_t dt + bu_t dt + c dw_t, with cost function min lim sup J_t(u), where

相似文献   

Linear-quadratic discrete-time control and constant directions

The general linear-quadratic discrete-time minimization problem is studied, in which no restrictions are placed on the singularity or otherwise of certain matrices, or the appearance of cross-product terms in the performance indices. Constant directions are characterized in a number of ways, and an algorithm is presented for replacing a prescribed problem with constant directions by one of lower state and/or control dimension, lacking constant directions. The replacement is achieved using a series of state and input co-ordinate basis changes, and allows simplification of the calculations obtaining the optimal control law and performance index of the original problem.     

Error estimates for the logarithmic barrier method in linear quadratic stochastic optimal control problems

We consider a linear quadratic stochastic optimal control problem with non-negativity control constraints. The latter are penalized with the classical logarithmic barrier. Using a duality argument and the stochastic minimum principle, we provide error estimates for the central path which are the natural extensions of the well known estimates in the deterministic framework.     

Discrete-time decentralized linear quadratic control for linear time-varying systems

This article addresses the problem of designing a decentralized control solution for a network of agents modeled by linear time-varying (LTV) dynamics, in a discrete-time framework. A general scheme is proposed, in which the problem is formulated as a classical linear quadratic regulator problem, for the global system, subject to a given sparsity constraint on the gain, which reflects the decentralized nature of the network. A method able to compute a sequence of well-performing stabilizing regulator gains is presented and validated resorting to simulations of two randomly generated LTV systems, one stable and the other unstable. Moreover, a tracking solution is developed, building on the solution to the regulator problem. Both methods rely on a closed-form solution, thus they can be computed very rapidly. Similarly to the centralized solution, both the presented methods require that a window of the future system dynamics is known. Both methods are validated resorting to simulations of: (i) a nonlinear network of four interconnected tanks; and (ii) a large-scale nonlinear network of interconnected tanks. When implemented to a nonlinear network, approximated by an LTV system, the proposed methods are able to compute well-performing gains that track the desired output. Finally, both algorithms are scalable, being adequate for implementation in large-scale networks.