Entropy formulation of optimal and adaptive control

The use of entropy as the common measure to evaluate the different levels of intelligent machines is reported. At the execution level, the design of the desirable control can be expressed by the uncertainty of selecting the optimal control that minimizes a given performance index. By choosing a density function over the set of admissible controls to minimize the differential control entropy, it can be shown that the optimal control problem is equivalent to the problem of minimization of the assigned entropy function with respect to the association control. The adaptive control problem can be analyzed by considering the same entropy over extended space that includes the uncertain parameters. It is shown that the optimal entropy is decomposed into three terms: the optimal control term with given parameters, the parameter identification term, and the equivocation term which accounts for the active transition of dual control. The equivocation when calculated can serve as a measure of optimality of the adaptive control algorithms that involve only distinct identification and optimal control algorithms. An upper bound can be used instead, when the equivocation is hard to calculate. An example illustrates the method     

用连续Hopfield网络实现无限时域上的最优控制

为避免直接采用Riccati方程求解时变系统无限域最优控制问题时的计算困难,本文提出一种基于时间连续状态连续型Hopfield网络(CTCSHNN)实现无限域动态最优控制的方法．该方法通过建立CTCSHNN能量函数与移动域控制指标间的等价关系,可在线构建CTCSHNN．理论分析表明,依据该方法设计的CTCSHNN具有稳定性,而且移动域控制量可由网络稳态输出直接产生．将该方法与滚动优化策略相结合,可实现无限时域上的闭环最优控制．仿真实验验证了理论设计的正确性与采用基于CTCSHNN的移动域控制实现无限域闭环最优控制的可行性．     

不完全柔性制造系统的最优控制

考虑具有随机需求的不完全柔性制造系统的最优控制，系统在各种产品间的切换时间是不可忽略的。运用马尔可夫最优决策过程归纳方法，导出机器服务率的最优控制策略。通过分析最优值函数的性质，证明最优策略具有简单的阈值结构，从而可得到次优生产策略--阈值控制策略。     

基于控制参数化的切换系统最优控制方法研究

针对一类特殊的混杂系统——切换系统的最优控制问题,利用控制函数参数化方法和时间尺度转化法,将切换系统最优控制问题转化为最优参数选择问题,可利用Miser 3.2来求解该问题,这样能有效地简化求解切换系统最优控制的难度,而且算法易于理解,编程易于实现。     

From human to humanoid locomotion—an inverse optimal control approach

The purpose of this paper is to present inverse optimal control as a promising approach to transfer biological motions to robots. Inverse optimal control helps (a) to understand and identify the underlying optimality criteria of biological motions based on measurements, and (b) to establish optimal control models that can be used to control robot motion. The aim of inverse optimal control problems is to determine—for a given dynamic process and an observed solution—the optimization criterion that has produced the solution. Inverse optimal control problems are difficult from a mathematical point of view, since they require to solve a parameter identification problem inside an optimal control problem. We propose a pragmatic new bilevel approach to solve inverse optimal control problems which rests on two pillars: an efficient direct multiple shooting technique to handle optimal control problems, and a state-of-the art derivative free trust region optimization technique to guarantee a match between optimal control problem solution and measurements. In this paper, we apply inverse optimal control to establish a model of human overall locomotion path generation to given target positions and orientations, based on newly collected motion capture data. It is shown how the optimal control model can be implemented on the humanoid robot HRP-2 and thus enable it to autonomously generate natural locomotion paths.     

Optimal prescribed performance tracking control of nonlinear motor driven systems via adaptive dynamic programming

Although optimal regulation problem has been well studied, resolving optimal tracking control via adaptive dynamic programming (ADP) has not been completely resolved, particularly for nonlinear uncertain systems. In this paper, an online adaptive learning method is developed to realize the optimal tracking control design for nonlinear motor driven systems (NMDSs), which adopts the concept of ADP, unknown system dynamic estimator (USDE), and prescribed performance function (PPF). To this end, the USDE in a simple form is first proposed to address the NMDSs with bounded disturbances. Then, based on the estimated unknown dynamics, we define an optimal cost function and derive the optimal tracking control. The derived optimal tracking control is divided into two parts, that is, steady-state control and optimal feedback control. The steady-state control can be obtained with the tracking commands directly. The optimal feedback control can be obtained via the concept of ADP based on the PPF; this contributes to improving the convergence of critic neural network (CNN) weights and tracking accuracy of NMDSs. Simulations are provided to display the feasibility of the designed control method.     

A varying terminal time structure for stochastic optimal control under constrained condition

In this study, we propose a varying terminal time structure for the optimal control problem under state constraints, in which the terminal time follows the varying of the control via the constrained condition. Focusing on this new optimal control problem, we investigate a novel stochastic maximum principle, which differs from the traditional optimal control problem under state constraints. The optimal pair of the optimal control model can be verified via this new stochastic maximum principle.     

Parallel Control for Optimal Tracking via Adaptive Dynamic Programming

This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear systems. Unlike existing optimal state feedback control, the control input of the optimal parallel control is introduced into the feedback system. However, due to the introduction of control input into the feedback system, the optimal state feedback control methods can not be applied directly. To address this problem, an augmented system and an augmented performance index function are proposed firstly. Thus, the general nonlinear system is transformed into an affine nonlinear system. The difference between the optimal parallel control and the optimal state feedback control is analyzed theoretically. It is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state feedback control with the traditional performance index function. Moreover, an adaptive dynamic programming (ADP) technique is utilized to implement the optimal parallel tracking control using a critic neural network (NN) to approximate the value function online. The stability analysis of the closed-loop system is performed using the Lyapunov theory, and the tracking error and NN weights errors are uniformly ultimately bounded (UUB). Also, the optimal parallel controller guarantees the continuity of the control input under the circumstance that there are finite jump discontinuities in the reference signals. Finally, the effectiveness of the developed optimal parallel control method is verified in two cases.      

带有随机跳跃干扰的线性二次随机最优控制问题

给出一类布朗运动和泊松过程混合驱动的正倒向随机微分方程解的存在唯一性结果, 应用这一结果研究带有随机跳跃干扰的线性二次随机最优控制问题,并得到最优控制的显式形 式,可以证明最优控制是唯一的.然后,引入和研究一类推广的黎卡提方程系统,讨论该方程系统 的可解性并由该方程的解得到带有随机跳跃干扰的线性二次随机最优控制问题最优的线性反馈.     

Distributed optimal cooperative tracking control of multiple autonomous robots

This paper presents a unified distributed optimal control approach for multiple autonomous robots’ cooperative tracking as well as obstacle avoidance. An inverse optimal control strategy is employed to design cost functions such that three cooperative control objectives including cooperative tracking, obstacle avoidance, and control effort minimization, can be addressed in one optimal control design process. The optimal control law of each robot can be obtained in a closed-form and only depends on the local information from the neighbors, rather than all robots’ information. Three simulation scenarios, rendezvous to a pre-specified point, tracking a straight line reference with a constant velocity, and tracking a circular trajectory, demonstrate the desired cooperative tracking behaviors as well as obstacle avoidance capability.     

Optimal control problems with multiple characteristic time points in the objective and constraints

In this paper, we develop a computational method for a class of optimal control problems where the objective and constraint functionals depend on two or more discrete time points. These time points can be either fixed or variable. Using the control parametrization technique and a time scaling transformation, this type of optimal control problem is approximated by a sequence of approximate optimal parameter selection problems. Each of these approximate problems can be viewed as a finite dimensional optimization problem. New gradient formulae for the cost and constraint functions are derived. With these gradient formulae, standard gradient-based optimization methods can be applied to solve each approximate optimal parameter selection problem. For illustration, two numerical examples are solved.     

一类带随机跳跃的完全耦合的线性二次随机控制问题

研究一类带随机跳跃的完全耦合的线性二次随机控制问题. 得到了最优控制的显式解, 并可以证明最优控制是唯一的. 引入了一类推广的黎卡提方程并讨论了其可解性. 利用这一类推广的黎卡提方程的解, 得到了上述带随机跳跃的最优控制问题的线性状态反馈调节器.     

基于控制参数化的切换系统最优控制方法研究

针对一类特殊的混杂系统——切换系统的最优控制问题．利用控制函数参数化方法和时间尺度转化法,将切换系统最优控制问题转化为最优参数选择问题,可利用Miser3．2来求解该问题,这样能有效地简化求解切换系统最优控制的难度,而且算法易于理解,编程易于实现。     

Stochastic control of linear sigularly perturbed systems

This paper applies singular perturbation theory to the stochastic control for the Linear-Quadratic-Gaussian (L-Q-G) problem for systems with fast and slow modes. The limiting behavior of the optimal control and the performance index is investigated. It is shown that the optimal control can be approximated by a near optimal control which is obtained as a combination of a slow control and a fast control computed in separate time scales.     

Optimal control for systems with deterministic production cycles

Considers a failure prone production system with deterministic production cycles. The objective is to find the optimal production rate to minimize long-run average cost. The authors first show that the optimal control policy belongs to a type of switching curve policy which has a special structural property and can be characterized by a single parameter. The authors then establish a relationship between the surplus process of the system under the optimal control policy and the workload process of a D/G/1 queue, based on which the existing results in queueing theory which can be applied to obtain the steady-state probability distribution of the surplus process under the optimal control policy     

线性离散奇异系统的不定二次最优控制问题: Krein空间方法

The finite time horizon indefinite linear quadratic(LQ) optimal control problem for singular linear discrete time-varying systems is discussed. Indefinite LQ optimal control problem for singular systems can be transformed to that for standard state-space systems under a reasonable assumption. It is shown that the indefinite LQ optimal control problem is dual to that of projection for backward stochastic systems. Thus, the optimal LQ controller can be obtained by computing the gain matrices of Kalman filter. Necessary and sufficient conditions guaranteeing a unique solution for the indefinite LQ problem are given. An explicit solution for the problem is obtained in terms of the solution of Riccati difference equations.     

Neighbouring extremals for nonlinear systems with control constraints

In this paper, we consider a class of nonlinear optimal control problems subject to control constraints. We assume that an initial condition for the dynamical system is specified. Then, we can easily compute an openloop optimal control using any convenient optimal control software package. Now, suppose the optimal trajectory is perturbed due to a change in initial conditions or uncertainty in the model equations. If the perturbations are not too large, it is known that the neighbouring extremal approach can be used to obtain a feedback law which adjusts the open-loop optimal control accordingly provided there are no bounds on the control variables. In this paper, our aim is to develop a computational method for the more general case of fixed-time problems with control constraints.     

使用模型检测解决概率布尔网络优化控制

系统生物学期望对复杂生物系统建立一个真实的、可计算的模型,以便于以系统的角度去理解生物系统的演变过程。在系统生物学中,一个重要的主题是通过外部的干预控制发展关于基因调控网络的控制理论,以作为未来基因治疗技术。目前,布尔网络及其扩展的概率布尔网络已经被广泛用于对基因调控网络进行建模。在控制问题的研究中,概率布尔控制网络的状态迁移本质上构成一条有限状态空间的离散时间马尔科夫决策过程。依据马尔科夫决策过程的理论,通过概率模型检测方法解决网络中有限范围优化控制问题和无限范围优化控制问题。针对带有随机干扰且上下文相关的概率布尔控制网络,使用概率模型检测器PRISM对其进行形式化建模,然后将两类优化控制问题描述为相应的时序逻辑公式,最后通过模型检测寻找出最优解。实验结果表明,提出的方法可以有效地用于生物网络的分析和优化控制。     

An Inverse Optimal Control Problem for a Class of Controlled Systems

A class of optimal control problems having an optimal solution of a specified property is investigated. Examples from irreversible thermodynamics and microeconomics are given. Such a formulation, as has been shown, can be regarded as an inverse optimal control problem.     

分布式多输入系统的自律鲁棒自适应分散控制

为了实现一类分布式多输入系统的分散鲁棒自适应控制, 基于状态扩张和反演干扰抑制控制, 提出一种自律鲁棒自适应分散控制的新方法. 结合直接反馈线性化和最优控制, 给出了自律最优鲁棒自适应分散控制的设计方法. 仿真结果表明, 所提出方法能够有效实现各子系统的自律鲁棒稳定、全系统整体鲁棒稳定和不确定参数自适应, 同时通过求解LMI, 具备了最优干扰抑制的功能.