Simplex sliding mode control for nonlinear uncertain systems via chaos optimization

As an emerging effective approach to nonlinear robust control, simplex sliding mode control demonstrates some attractive features not possessed by the conventional sliding mode control method, from both theoretical and practical points of view. However, no systematic approach is currently available for computing the simplex control vectors in nonlinear sliding mode control. In this paper, chaos-based optimization is exploited so as to develop a systematic approach to seeking the simplex control vectors; particularly, the flexibility of simplex control is enhanced by making the simplex control vectors dependent on the Euclidean norm of the sliding vector rather than being constant, which result in both reduction of the chattering and speedup of the convergence. Computer simulation on a nonlinear uncertain system is given to illustrate the effectiveness of the proposed control method.     

基于样条插值的模糊控制算法

利用三次样条插值函数,直接由控制输入输出数据对建立了控制输入与控制输出之间的映射关系,得到了一元三次样条插值控制算法和二元双三次样条插值控制算法,并将二者分别用于单输入单输出系统和双输入单输出系统的仿真控制.仿真结果表明,上述方法是可行的,并且基于三次样条函数的模糊插值控制,具有响应快,无超调,稳态误差极小等很好的控制效果.其设计简单,不需要过多规则,对稀疏规则库条件下的控制器设计尤为适用.     

On ergodic control problems for singularly perturbed Markov processes

In this paper ergodic control problems (optimal stopping, impulsive control, and stochastic control for singularly perturbed Feller Markov processes) are studied. As the main result the so-called limit control principle is shown to hold in each case. The results obtained depend on the averaging properties of the perturbed system which follow from the fact that the perturbing process does not depend on either the perturbed process or on the control.     

基于测量质量损失函数的控制图控制界限的优化

控制界限和抽样间隔是控制图的两个基本参数。常规控制图是基于3σ原理确定的控制界限,该控制界限是在大量试验基础上依据经验确定的,并没有精确的公式推导.对于抽样间隔,常规控制图也没有明确的规定。田口博士的质量损失函数可以很好的解决质量经济性方面的一些问题.利用田口博士的理论,通过确定适宜的二次测量质量损失函数,可以确定控制图的最佳控制界限和最佳抽样间隔.文章简要介绍了常规控制图原理和田口博士的质量损失函数,重点叙述了田口博士反馈控制系统的测量质量损失函数,在此基础上,研究了控制图最佳控制界限和最佳抽样间隔,并且通过具体实例验证了该控制图良好的经济性.     

混沌记忆系统的自适应反馈控制和轨迹跟踪控制

研究了混沌记忆系统的自适应反馈控制和基于反馈线性化的轨迹跟踪控制问题.首先,通过绘制系统的时域波形图和混沌吸引子图验证系统的复杂的动力学行为;然后,分别应用自适应反馈控制方法和基于反馈线性化的轨迹跟踪控制方法设计控制器,对系统施加控制;最后,通过数值仿真验证控制器的有效性.     

Development of stability research on Takagi-Sugeno fuzzy control systems and approximation of the necessary and sufficient conditions

The controller’s design and analysis based on Takagi-Sugeno (T-S) models play important roles in the research of fuzzy control. When fuzzy control is being widely applied, the stability research on T-S fuzzy control supports the applications on the theoretical standpoint. The development of the stability research on T-S fuzzy control is surveyed in this paper. For the Lyapunov functions, this paper considers common quadratic Lyapunov functions, piecewise quadratic Lyapunov functions, fuzzy Lyapunov functions, nonquadratic Lyapunov functions and homogenously polynomially parameterized Lyapunov functions. For the control laws, this paper considers parallelly distributed compensation, non-parallel distributed compensation laws and homogenously polynomially parameterized control laws. By extensively applying the Pólya’s theorem and the techniques for homogenous polynomials, the stability conditions are gradually developed towards necessary and sufficient. It is very important to recognize and master this development in order to further study fuzzy control and the related control theories.     

基于主成分原理的多元质量控制图的构造

控制图是从事统计质量管理经常使用的重要工具,经过多年的发展和实践,一元质量控制图业已得到普遍推广,可是如何开展多元质量控制仍有许多问题值得探讨。在这篇文章中,我们着重讨论了基于主成分分析的多元质量控制图的构造,并结合从企业现场采集到的数据给出了具体的应用说明。     

Multivariable fuzzy control applied to the physical–chemical treatment facility of a Cellulose factory

Feedback linearization is a well-known technique in nonlinear control in which known system nonlinearities are canceled by the control input leaving a linear control problem. Feedback linearization requires an exact model for the system. Fundamental and advanced developments in neuro-fuzzy synergy for modeling and control are used to apply the feedback linearization control law on second-order plants. In the models that are used, the nonlinear plant is decomposed on six fuzzy systems necessary to apply the control signal to allow the following of a reference value. A practical application is also presented using a waste water plant. This method can be extended to multiple input–multiple output (MIMO) plants based on input–output data pairs collected directly from the plant.     

Optimal control with a cost on changing control

In this paper, we consider a class of optimal control problems in which the cost functional is the sum of the terminal cost, the integral cost, and the full variation of control. The term involving the full variation of control is to measure the changes on the control action. A computational method based on the control parametrization technique is developed for solving this class of optimal control problems. This computational method is supported by a convergence analysis. For illustration, two numerical examples are solved using the proposed method.This project was partially supported by an Australian Research Grant.This paper is dedicated to Professor L. Cesari on the occasion of his 80th birthday.     

On bi-pointwise control of a wave equation and algorithm

We are concerned with mathematical analysis related to the bi-pointwise control for a mixed type of wave equation. In particular, we are interested in the systematic build-up of the bi-pointwise control actuators; one at the boundary and the other at the interior point simultaneously. The main purpose is to examine Hilbert Uniqueness Method for the setting of bi-pointwise control actuators and to establish relevant algorithm based on our analysis. After discussing the weak solution for the state equation, we investigate bi-pointwise control mechanism and relevant mathematical analysis based on HUM. We then proceed to set up an algorithm based on the conjugate gradient method to establish bi-pointwise control actuators to halt the system.     

Optimization in microbial pest control: An integrated approach

The paper deals with optimal management of agricultural pest population under integrated control arising out of viral infection and spraying of pesticide. The costs of the control measures and the profits or projected profits of the biomass of species give rise to a control theoretic optimization problem. We take a four dimensional mathematical model of pest control under viral infection and pesticide, and apply Pontryagin’s maximum principle (PMP) to find out the necessary conditions on economic as well as on ecological parameters to make the control process maximum profitable.     

Controlling chaos for the dynamical system of coupled dynamos

This work is a tutorial on the different methods to control chaotic behaviour of the coupled dynamos system. Feedback and nonfeedback control techniques are proposed to suppress chaos to unstable equilibrium or unstable periodic solution. The stabilization of unstable fixed point of the chaotic behaviours is achieved also by bounded feedback method. Stability of the controlled systems are studied by Routh–Hurwitz criterion. Nonfeedback method and a derived method based on the delay feedback control are used to control chaos to periodic orbits. Numerical simulation results are included to show the control process of the different methods.     

Sequentially switched fuzzy-model-based control for wheeled mobile robot with visual odometry

This study focused on the Takagi–Sugeno (T–S) fuzzy-model-based control design for the differentially-driven wheeled mobile robot with visual odometry. The position and posture of the mobile robot are estimated by visual odometry. The polar kinematic model of the mobile robot is exactly converted to the T–S fuzzy model and then the fuzzy control design is synthesized to the fuzzy model. The sequentially switched fuzzy control design includes turning, forward motion as well as position and posture control modes. The stabilization is guaranteed based on the Lyapunov stability criterion. The practical constraints on the visual odometry are also satisfied in the control design. Finally, the experiment results demonstrate the effectiveness of the fuzzy-model-based control design for the mobile robot with visual odometry.     

Robust Control of Robots by Stochastic Optimisation Techniques

In optimal control of robots, engineering practice is to calculate off‐line an optimal open‐loop control, based on nominal values of the model parameters. Due to the deviation between the nominal model parameters and the possible realisations of the model parameters, the performance of the system usually does not correspond to the chosen open‐loop control. Hence, expensive on‐line control corrections are necessary to improve system performance. By incorporating available a priori and statistical information about the unknown model parameters into the control process, these expenses are reduced to a large extent.     

Direct model reference adaptive control (MRAC) design and simulation for the vibration suppression of piezoelectric smart structures

The paper presents control system design based on a non-linear model reference adaptive control law (MRAC) used for the vibration suppression of a smart piezoelectric mechanical structure. Numerical simulation of the proposed control system is performed based on the finite element (FE) model of the structure, modally reduced in order to meet the requirements of the control system design. First the MRAC problem is defined and a direct control algorithm described in the paper is suggested as a solution to the control problem. The basic MRAC algorithm is modified by augmenting the integral term of the control law in order to provide the robustness of the control system with respect to the stability. This approach provides preserving the boundness of the system states and adaptive gains, with small trackings error over large ranges of non-ideal conditions and uncertainties. The efficiency of the suggested control for the vibration suppression is tested and shown through a numerical simulation of the funnel-shaped piezoelectric structure.     

A Continuous Implementation of a Second-Variation Optimal Control Method for Space Trajectory Problems

The paper describes a continuous second-variation method to solve optimal control problems with terminal constraints where the control is defined on a closed set. The integration of matrix differential equations based on a second-order expansion of a Lagrangian provides linear updates of the control and a locally optimal feedback controller. The process involves a backward and a forward integration stage, which require storing trajectories. A method has been devised to store continuous solutions of ordinary differential equations and compute accurately the continuous expansion of the Lagrangian around a nominal trajectory. Thanks to the continuous approach, the method adapts implicitly the numerical time mesh and provides precise gradient iterates to find an optimal control. The method represents an evolution to the continuous case of discrete second-order techniques of optimal control. The novel method is demonstrated on bang–bang optimal control problems, showing its suitability to identify automatically optimal switching points in the control without insight into the switching structure or a choice of the time mesh. A complex space trajectory problem is tackled to demonstrate the numerical robustness of the method to problems with different time scales.     

Three Controls are Better than One: A Computational Model of Complex Control Systems

This paper investigates theories that integrate and extend currently accepted agency- and transaction-based approaches to organizational control. We use a computational model to build three forms of control systems (market, bureaucratic, clan) and three forms of control targets (input, behavior, output). Using these models, we examine relationships between control systems and both singular and multiple control targets. Results of this study support the emerging broader perspective on organizational control research and suggest that managers can improve organizational performance by focusing attention on multiple control targets. In addition, findings partially support posited relationships between control systems and singular control targets. The authors suggest that results of this study should direct scholars to refocus control research from examinations of singular forms of control to evaluations of more complex control systems.     

Adaptive Constrained Control of Unknown Strict Feedback Nonlinear Systems With Dead Zone Input北大核心CSCD

研究了具有死区输入的预设约束未知高阶严格反馈非线性系统的控制问题,提出了一种基于免疫函数的自抗扰预设漏斗约束自适应控制策略。首先,针对系统内部的未知问题,采用免疫函数与扩张状态观测器结合对系统内部未知项进行观测;其次,通过Lyapunov方法与漏斗控制相结合设计控制器,使得跟踪误差能够维持在预先设定的漏斗约束范围内;同时,利用双曲正切函数速率变化快这一特性设计自适应控制律,引入指令滤波器避免反步法中重复求导问题,分析证明了闭环系统所有信号的有界性。仿真实例表明了控制方法的有效性。     

Step-guided strategies for control problems with uncertain dynamics

We study here a rather general class of nonlinear control systems with uncertain dynamics involving perturbating variables, without assuming any statistical information on them. Contrary to majority of publications on uncertain control problems, we study here the value function and seek for optimal (-optimal) control policies. Our four theorems, covering all possible situations that may happen in the context of relationships between the influence of the control parameters on the system versus the perturbating parameters, give both upper and lower bounds for the value function in terms of solutions of simplified unperturbed control problems. They also provide constructive procedures for finding strategies that, under additional assumptions, appear to be -optimal control policies in the class of so-called step guided strategies introduced by us in a very recent paper.     

A semi-analytical direct optimal control solution for strongly excited and dissipative Hamiltonian systems

A semi-analytical direct optimal control solution for strongly excited and dissipative Hamiltonian systems is proposed based on the extended Hamiltonian principle, the Hamilton-Jacobi-Bellman (HJB) equation and its variational integral equation, and the finite time element approximation. The differential extended Hamiltonian equations for structural vibration systems are replaced by the variational integral equation, which can preserve intrinsic system structure. The optimal control law dependent on the value function is determined by the HJB equation so as to satisfy the overall optimality principle. The partial differential equation for the value function is converted into the integral equation with variational weighting. Then the successive solution of optimal control with system state is designed. The two variational integral equations are applied to sequential time elements and transformed into the algebraic equations by using the finite time element approximation. The direct optimal control on each time element is obtained respectively by solving the algebraic equations, which is unconstrained by the system state observed. The proposed control algorithm is applicable to linear and nonlinear systems with the quadratic performance index, and takes into account the effects of external excitations measured on control. Numerical examples are given to illustrate the optimal control effectiveness.