Neural network solution for finite-horizon H-infinity constrained optimal control of nonlinear systems

In this paper, neural networks are used to approximately solve the finite-horizon constrained input H-infinity state feedback control problem. The method is based on solving a related Hamilton-Jacobi-Isaacs equation of the corresponding finite-horizon zero-sum game. The game value function is approximated by a neural network with time- varying weights. It is shown that the neural network approximation converges uniformly to the game-value function and the resulting almost optimal constrained feedback controller provides closed-loop stability and bounded L2 gain. The result is an almost optimal H-infinity feedback controller with time-varying coefficients that is solved a priori off-line. The effectiveness of the method is shown on the Rotational/Translational Actuator benchmark nonlinear control problem.     

基于二次型性能指标的混合动力汽车实时优化控制

为解决混合动力系统实时优化控制问题,本文提出了一种基于二次型性能指标最优的混合动力汽车功率分配优化方案.通过合理的假设和近似,建立了混合动力系统的线性模型,并利用二次型最优控制理论将混合动力最优控制问题转化为二次型最优调节问题进行求解,得到了一个结构简单的实时优化控制算法.5种道路工况下的仿真结果表明,本文提出的控制方法在未来道路工况未知的情况下能够实现混合动力系统的实时优化控制,且节油率与离线计算以燃油消耗最小为性能指标的全局最优控制的节油率相近.     

Robust model predictive control with time-varying tubes

This paper focuses on the problem of robustly stabilizing uncertain discrete-time systems subject to bounded disturbances. The proposed tube-based model predictive controller ensures that all possible realizations of the state trajectory lie in the time-varying tubes so robust stability and satisfaction of the state and input constraints are guaranteed. The time-varying tubes are computed off-line so the on-line computational time is tractable. At each sampling time, the precomputed time-varying tubes are included in the optimal control problem as the constraints in the prediction horizon and only a quadratic programming problem is solved. In comparison to the algorithm that calculates the time-varying tubes on-line, the proposed algorithm can achieve the same level of control performance while the on-line computational time is greatly reduced.     

Observer-based output feedback control of networked control systems with non-uniform sampling and time-varying delay

This paper investigates the problem of observer-based output feedback control for networked control systems with non-uniform sampling and time-varying transmission delay. The sampling intervals are assumed to vary within a given interval. The transmission delay belongs to a known interval. A discrete-time model is first established, which contains time-varying delay and norm-bounded uncertainties coming from non-uniform sampling intervals. It is then converted to an interconnection of two subsystems in which the forward channel is delay-free. The scaled small gain theorem is used to derive the stability condition for the closed-loop system. Moreover, the observer-based output feedback controller design method is proposed by utilising a modified cone complementary linearisation algorithm. Finally, numerical examples illustrate the validity and superiority of the proposed method.     

Global optimal feedback control for general nonlinear systems with nonquadratic performance criteria

Optimal control of general nonlinear nonaffine controlled systems with nonquadratic performance criteria (that permit state- and control-dependent time-varying weighting parameters), is solved classically using a sequence of linear- quadratic and time-varying problems. The proposed method introduces an “approximating sequence of Riccati equations” (ASRE) to explicitly construct nonlinear time-varying optimal state-feedback controllers for such nonlinear systems. Under very mild conditions of local Lipschitz continuity, the sequences converge (globally) to nonlinear optimal stabilizing feedback controls. The computational simplicity and effectiveness of the ASRE algorithm is an appealing alternative to the tedious and laborious task of solving the Hamilton–Jacobi–Bellman partial differential equation. So the optimality of the ASRE control is studied by considering the original nonlinear-nonquadratic optimization problem and the corresponding necessary conditions for optimality, derived from Pontryagin's maximum principle. Global optimal stabilizing state-feedback control laws are then constructed. This is compared with the optimality of the ASRE control by considering a nonlinear fighter aircraft control system, which is nonaffine in the control. Numerical simulations are used to illustrate the application of the ASRE methodology, which demonstrate its superior performance and optimality.     

Part-by-part dimensional error compensation in compliant sheet metal assembly processes

Dimensional variation in assembly processes is one of the most important issues that affect quality. Although robust design and statistical process quality control help to reduce this problem, they cannot be used for instant variation reduction during assembly operations, especially during process ramp-up. This paper introduces a complete methodology for dimensional-related error compensation in compliant sheet metal assembly processes. The proposed methodology is divided into two steps: (1) an off-line error control-learning module using virtual assembly models to determine necessary adjustments; and (2) an in-line control implementation using a feed-forward control strategy based on the learned adjustments. The off-line learning step focuses on determining control actions or corrections to compensate for the negative effects incoming part errors have on Key Product Characteristics. Specifically, it utilizes a newly developed iterative sampling method based on Kriging fitting to efficiently determine optimal control actions. The in-line feed-forward control identifies appropriate part-by-part adjustments using these learned control actions and incoming assembly component measurements. In this paper, two case studies are presented. First, a mathematical case study presents an empirical proof for the feasibility of the Iterative Sampling and Fitting Algorithm. Second, a simulation-based case study illustrates the effectiveness of the proposed methodology to improve dimensional quality in assembly operations for compliant sheet metal parts.     

Optimal control design for time-varying catalytic reactors: a Riccati equation-based approach

The linear quadratic (LQ) optimal control problem is studied for a partial differential equation model of a time-varying catalytic reactor. First, the dynamical properties of the linearised model are studied. Next, an LQ-control feedback is computed by using the corresponding operator Riccati differential equation, whose solution can be obtained via a related matrix Riccati partial differential equation. Finally, the designed controller is applied to the non-linear reactor system and tested numerically.     

基于无线传感器网络的控制系统采样频率优化算法

针对基于无线传感网的网络化控制系统,讨论了采样频率的优化问题．建立了以数字和模拟控制系统性能差距指数最低为目标,以无线节点的通信容量为约束条件的非线性优化模型,并以障碍函数法进行求解,提出了基于节点缓冲区信息的分布武迭代算法．该算法在传感节点的计算量小,易于实现．仿真表明该算法能有效收敛到系统的最优目标点,并能适应于系统的负载变化．     

基于模糊模型的CMP过程智能R2R预测控制方法

针对化学机械研磨(CMP)过程非线性、时变和产品质量不易在线测量的特性,提出了一种基于T-S模糊模型的CMP过程智能run-to-run(R2R)预测控制器FIPR2R;通过G-K聚类算法和最小二乘法对CMP过程的T-S模糊预测模型离线辨识,解决了复杂CMP过程难以建立精确数学模型的难题和提高了模型预测精度;通过双指数加权移动平均(dEWMA)中对过程扰动及漂移进行估计的方法实现反馈校正和基于克隆选择算法的滚动优化求取最优控制律,提高了控制精度;性能分析结果表明,FIPR2R控制器的控制性能优于dEWMA方法,有效抑制了过程扰动和漂移的影响。     

带有通讯约束的网络化控制系统容错控制技术研究

文中研究了针对具有通讯约束的网络化控制系统存在执行器增益故障的容错控制问题. 提出一种静态调度策略, 利用周期通讯序列的方法调度网络资源及网络可用通道. 该研究的创新处在于首先将具有周期通讯序列及执行器故障的网络化控制系统建模为一个周期的切换系统, 并提出了基于调度依赖的利亚普诺夫函数方法对系统容错控制器进行设计. 由于调度策略使得在每个采样时刻存在数据丢失, 文中分别利用0值和上一时刻采样值替代所丢失的数据. 更进一步的, 当考虑控制对象存在外界能量有界干扰时, 分别就上述两种情况讨论了其鲁棒容错控制器的设计问题. 最后, 通过仿真实例证明了所提方法的有效性.     

ProOpter: An advanced platform for production analysis and optimization

This paper presents the prototype of an advanced platform for production analysis and optimization, referred to as ProOpter. The platform was developed to support the recently derived concept of holistic production control (HPC), which relies on model-based control. The prototype is comprised of a set of off-line and on-line modules. The off-line modules support the definition of key performance indicators (KPIs), the selection of the most influential input (manipulative) variables, and the identification of a simple production model from historical data. The on-line modules enable KPI prediction and suggest actions to the production manager, employing model-based production control and/or optimization techniques. In this way, a new decision-support reasoning based on historical production data can be introduced. ProOpter has a modular design and can be used as an add-on to existing production IT systems since it relies on established industrial communication standards. The use of the platform is validated on the well-known Tennessee Eastman benchmark simulation process and on two industrial case studies.     

Optimization-based tuning of nonlinear model predictive control with state estimation

Nonlinear model predictive controllers determine appropriate control actions by solving an on-line optimization problem. A nonlinear process model is utilized for on-line prediction, making such algorithms particularly appropriate for the control of chemical reactors. The algorithms presented in this paper incorporates an extended Kalman filter, which allows operations around unstable steady-state points. The paper proposes a formalization of the procedure for tuning the several parameters of the control algorithm. This is accomplished by specifying time-domain performance criteria and using an interactive multi-objective optimization package off-line to determine parameters values that satisfy these criteria. Three reactor examples are used to demonstrate the effectiveness of the proposed on-line algorithm and off-line tuning procedure.     

Solving Fixed Final Time Fractional Optimal Control Problems Using the Parametric Optimization Method

In this paper, the parametric optimization method is used to find optimal control laws for fractional systems. The proposed approach is based on the use for the fractional variational iteration method to convert the original optimal control problem into a nonlinear optimization one. The control variable is parameterized by unknown parameters to be determined, then its expression is substituted into the system state‐space model. The resulting fractional ordinary differential equations are solved by the fractional variational iteration method, which provides an approximate analytical expression of the closed‐form solution of the state equations. This solution is a function of time and the unknown parameters of the control law. By substituting this solution into the performance index, the original fractional optimal control problem reduces to a nonlinear optimization problem where the unknown parameters, introduced in the parameterization procedure, are the optimization variables. To solve the nonlinear optimization problem and find the optimal values of the control parameters, the Alienor global optimization method is used to achieve the global optimal values of the control law parameters. The proposed approach is illustrated by two application examples taken from the literature.     

Structural Properties,LQG Control and Scheduling of a Networked Control System with Bandwidth Limitations and Transmission Delays

The problem of simultaneous LQG control and scheduling of a Networked Control System (NCS) with constant network induced delays at input and output and bandwidth limitations is investigated. Delays are considered at plant as well as controller side. Sufficient conditions for controllability, stabilizability, reconstructibility and detectability of the underlying networked control system are drawn. The proposed conditions extend previous works on structural properties of NCS by capturing both plant and controller side delays together with bandwidth limitations. A framework for computing the optimal LQG controller for the NCS with a fixed scheduling is provided. The proposed modeling approach facilitates use of LQG as well as other control methods for NCSs with delays and bandwidth limitations. In order to optimize performance, a semi-online scheduling procedure is proposed based on an offline look up table. The look up table assigns an optimal schedule with associated optimal LQG controller to initial conditions. The proposed scheme improves previous results by online deployment of schedule and LQG control with stability guarantees and very low computational overhead. A simulation example with communication delays, packet losses and bandwidth limitations in both sensor and actuator sides is included. Static optimal periodic communication sequence, Optimal Pointer Placement (OPP) approach proposed in previous works, a random access scheduling method representing contention based access policies and the proposed method are simulated and compared.      

基于连续Hopfield网络的多变量时变系统最优控制

针对多变量时变系统提出了一种基于连续Hop fie ld网络的最优控制系统设计方法.该方法不仅从理论上建立了移动时域上的LQ性能指标与连续Hop fie ld网络能量函数间的等价关系,并在此基础上设计出可求解LQ最优控制问题的连续Hop fie ld网络,而且将滚动优化控制策略引入控制系统,形成了包括连续Hop fie ld网络在内的闭环控制结构,实现了多变量时变系统无限域上的动态最优控制.仿真结果验证了该设计方法的有效性.     

Non-uniform sampled-data control of MIMO systems

In this paper, the problem of controlling MIMO plants where the pattern of measurements sampling and control actions delivering is not regular is tackled. Multirate control, time delay systems, missing data environments as well as limited computing and communication resources are on the grounds of these problems. There is an extensive literature on these topics. Different models are reviewed and, from the control viewpoint, model-based control design techniques are reported and new algorithms are proposed. Among the main challenges are the achievement of good performance and the avoidance of intersampling ripple. Both issues are considered and some results are discussed.     

一类多包传输网络控制系统的H∞鲁棒控制

研究了一类不确定时延多包传输网络控制系统的最优H∞控制问题. 针对传感器时钟驱动、控制器和执行器事件驱动的多包传输网络控制系统, 考虑不大于一个采样周期的不确定时延和有限能量的外部扰动, 提出一种基于模型的反馈控制策略, 利用Lyapunov理论推导了H∞控制律存在的充分条件, 通过求解矩阵不等式约束的优化问题得到最优H∞控制律. 数值仿真表明了所用方法的有效性.     

Nonlinear Q-Design for Convex Stochastic Control

In this note we describe a version of the Q-design method that can be used to design nonlinear dynamic controllers for a discrete-time linear time-varying plant, with convex cost and constraint functions and arbitrary disturbance distribution. Choosing a basis for the nonlinear Q-parameter yields a convex stochastic optimization problem, which can be solved by standard methods such as sampling. In principle (for a large enough basis, and enough sampling) this method can solve the controller design problem to any degree of accuracy; in any case it can be used to find a suboptimal controller, using convex optimization methods. We illustrate the method with a numerical example, comparing a nonlinear controller found using our method with the optimal linear controller, the certainty-equivalent model predictive controller, and a lower bound on achievable performance obtained by ignoring the causality constraint.     

基于数据决策的人力资源服务绩效模型

当前大部分电力企业人力资源服务绩效考核模式较为陈旧,绩效考核的指标较为单一.为了提高电力部门人力资源服务绩效量化管理能力,提出基于数据决策的人力资源服务绩效模型,由此加快电力部门人力资源评价机制的转变,实现电力系统人力资源管理优化配置.通过信息聚类和特征优化采样方法,构建电力系统人力资源服务绩效大数据信息分析模型;采用...     

Sampling schedule design towards optimal drug monitoring for individualizing therapy

We study the individualization of therapy by simultaneously taking into account the design of sampling schedule and optimal therapeutic drug monitoring. The sampling schedule design in this work is to determine the number of samples, the sampling times, the switching time from the loading to the maintenance period, and the drug dosages. A closed-loop control policy is employed to determine the sampling schedule, and an advanced stochastic global optimization algorithm, which integrates the stochastic approximation and simulated annealing techniques, is implemented to search the optimal sampling schedule. A simulated one-compartment model of intravenous theophylline therapy is used to illustrate our method. This method can be readily extended to multiple compartment systems and allow incorporating other criteria of drug control. While currently the method is mainly of theoretical interest, it offers a starting point for practical applications and thus is hopefully of great value for the clinically individualizing therapy in the future.