鲁棒自适应控制系统的设计

本文提出了鲁棒自适应控制系统的两种设计方案。第一方案为具有逻辑决策环节的模型参考自适应控制系统,即使在被控对象结构不确知的情况下此方案仍能保证全局稳定。在第二种方案中采用混合式自适应控制,采用一种新的参数估计器,利用参数估计结果在离散时刻调节控制器参数。     

Model adaptive hybrid dynamic control for constrained motionsystems

A new task-level adaptive controller is presented for the hybrid dynamic control of constrained motion systems. Using a hybrid dynamic model of the process, velocity constraints are derived from which satisfactory velocity commands are obtained. Due to modeling errors and parametric uncertainties, the velocity commands may be erroneous and may result in suboptimal performance. A task-level adaptive control scheme, based on the occurrence of discrete events, is used to change the model parameters from which the velocity commands are determined. Automated control of an assembly task is given as an example, and simulations and experiments for this task are presented. These results demonstrate the applicability of the method and also indicate properties for rapid convergence     

控制模态转移型系统的稳定切换

离散事件决策子系统和数值反馈子系统合成的混合控制系统呈现出复杂的稳定动态 特性.研究了在达到模态切换周期的控制规则下,相应的系统连续状态轨迹的平稳性.给出了具 有较少约束的李雅谱诺夫型判据,不要求全局一致的李雅谱诺夫函数,不要求每个模态下的能 量函数单调递减.     

Multiobjective evolution based fuzzy PI controller design for nonlinear systems

This work proposes a gain scheduling adaptive control scheme based on fuzzy systems, neural networks and genetic algorithms for nonlinear plants. A fuzzy PI controller is developed, which is a discrete time version of a conventional one. Its data base as well as the constant PI control gains are optimally designed by using a genetic algorithm for simultaneously satisfying the following specifications: overshoot and settling time minimizations and output response smoothing. A neural gain scheduler is designed, by the backpropagation algorithm, to tune the optimal parameters of the fuzzy PI controller at some operating points. Simulation results are shown to demonstrate the efficiency of the proposed structure for a DC servomotor adaptive speed control system used as an actuator of robotic manipulators.     

一种基于MT-波波器的连续自适应反推控制的数字实现方法

研究了基于MT-滤波器的连续自适应反推控制的数字实现问题.首先用δ-算子将连 续系统离散化.利用MT-滤波器和反推设计方法给出了自适应反推控制器的设计,然后分别 分析了离散和混杂自适应控制系统的稳定性和跟踪性能.同已有文献相比,本文的主要工作在 于:1)基于MT-滤波器的离散自适应反推控制器的设计;2)误差系统状态向量的构造.由于 MT-滤波器的阶次比K-滤波器的阶次低,因此,误差系统的构造更加复杂,这也使得适应系统 的稳定性分析更加困难.     

Control of nonlinear systems with Markovian parameter changes

The control of hybrid systems in which a totally observed state process is generated by a nonlinear stochastic differential equation whose parameters are functions of a random jump process called the plant regime is considered. Regime transitions typically represent target maneuvers in a tracking system. The control task is split between a jump detection and identification (JDI) filter and a discrete adaption, or reconfiguration. A design that takes into account the errors of the JDI device and the plant nonlinearities to produce a near-optimal passively adaptive control law with an easily implemented affine structure is presented     

Discrete adaptive control: A sufficient condition for stability and applications

Adaptive control of discrete, deterministic, single-input single-output systems is considered using a model-reference type approach. A sufficient condition upon the adaptation mechanism is obtained. Any algorithm satisfying it solves the adaptive control problem. In particular, constant gain algorithms are shown to verify it without requiring auxiliary signals or anticipative knowledge of the desired output, as generally requied.     

基于偏格式线性化的MIMO系统无模型自适应控制

针对常规PID控制器不能很好兼顾抗干扰性与鲁棒性以及基于模型的控制算法过于依赖受控系统数学模型的缺点,提出一种适用于离散时间多输入多输出(MIMO)非线性系统的无模型自适应控制算法.该算法首先通过偏格式线性化方法将非线性系统线性化,再利用一种新型的投影算法在线辨识受控系统参数,根据辨识得到的受控系统参数直接递推计算无模...     

An Adaptive Data-Shipping Architecture for Client Caching Data Management Systems

Data-shipping is an important form of data distribution architecture where data objects are retrieved from the server, and are cached and operated upon at the client nodes. This architecture reduces network latency and increases resource utilization at the client. Object database management systems (ODBMS), file-systems, mobile data management systems, multi-tiered Web-server systems and hybrid query-shipping/data-shipping architectures all use some variant of the data-shipping. Despite a decade of research, there is still a lack of consensus amongst the proponents of ODBMSs as to the type of data shipping architectures and algorithms that should be used. The absence of both robust (with respect to performance) algorithms, and a comprehensive performance study comparing the competing algorithms are the key reasons for this lack of agreement. In this paper we address both of these problems. We first present an adaptive data-shipping architecture which utilizes adaptive data transfer, cache consistency and recovery algorithms to improve the robustness (with respect to performance) of a data-shipping ODBMS. We then present a comprehensive performance study which evaluates the competing client-server architectures and algorithms. The study verifies the robustness of the new adaptive data-shipping architecture, provides new insights into the performance of the different competing algorithms, and helps to overturn some existing notions about some of the algorithms.     

Timed Petri Nets in Hybrid Systems: Stability and Supervisory Control

In this paper, timed Petri nets are used to model and control hybrid systems. Petri nets are used instead of finite automata primarily because of the advantages they offer in dealing with concurrency and complexity issues. A brief overview of existing results on hybrid systems that are based on Petri nets is first presented. A class of timed Petri nets named programmable timed Petri nets (PTPN) is then used to model hybrid systems. Using the PTPN, the stability and supervisory control of hybrid systems are addressed and efficient algorithms are introduced. In particular, we present sufficient conditions for the uniform ultimate boundness of hybrid systems composed of multiple linear time invariant plants which are switched between using a logical rule described by a Petri net. This paper also examines the supervisory control of a hybrid system in which the continuous state is transfered to a region of the state space in a way that respects safety specifications on the plant's discrete and continuous dynamics.     

Hybrid adaptive fuzzy identification and control of nonlinearsystems

We present a combined direct and indirect adaptive control scheme for adjusting an adaptive fuzzy controller, and adaptive fuzzy identification model parameters. First, using adaptive fuzzy building blocks, with a common set of parameters, we design and study an adaptive controller and an adaptive identification model that have been proposed for a general class of uncertain structure nonlinear dynamic systems. We then propose a hybrid adaptive (HA) law for adjusting the parameters. The HA law utilizes two types of errors in the adaptive system, the tracking error and the modeling error. Performance analysis using a Lyapunov synthesis approach proves the superiority of the HA law over the direct adaptive (DA) method in terms of faster and improved tracking and parameter convergence. Furthermore, this is achieved at negligible increased implementation cost or computational complexity. We prove a theorem that shows the properties of this hybrid adaptive fuzzy control system, i.e., bounds for the integral of the squared errors, and the conditions under which these errors converge asymptotically to zero are obtained. Finally, we apply the hybrid adaptive fuzzy controller to control a chaotic system, and the inverted pendulum system     

Unified adaptive control of non-minimum-phase systems Part 1. Weighted control effort and pole placement for stochastic systems

Global convergence of a class of unified adaptive control algorithms for discrete time non-minimum-phase stochastic linear systems is established. It is shown that the algorithms ensure that the system inputs and outputs are sample mean bounded, the minimum variance cost function with weighted control effort achieves its minimum possible values, the weighted factors adjusted on-line eventually converge to expected values, and the closed-loop poles arise at prespecified locations.     

Almost Passivity and Simple Adaptive Control in Discrete‐Time Systems

Successful implementations of simple direct adaptive control techniques in various domains of application have been presented over the last two decades in the technical literature. The theoretical background concerning basic conditions needed for stability of the controller and the open questions relating the convergence of the adaptive gains have been clarified recently, yet only for the continuous‐time algorithms. Apparently, asymptotic tracking in discrete time systems has been possible only with step input commands and the scope of the so called “almost strictly positive real (ASPR)” condition has also remained not clear. This paper expands the feasibility of discrete simple adaptive control methodology to include any desired input commands and almost all real‐world systems. The proofs of stability are also rigorously revised to solve the ultimate adaptive gain values question that has remained open until now. Finally, a complex algebraic loop that seemed to be inherent in discrete ASPR systems and might prevent the use of passivity properties in discrete systems has also been eliminated.     

Automating the simulation of complex discrete-time control systems:a mathematical framework, algorithms, and a software package

With the rapid proliferation of complex control systems, such as adaptive control systems, a need has arisen to automate as much as possible of the time-consuming preparatory work needed to simulate such systems. This burden currently falls on the user. The smaller the user time required and the more effortless the process, the greater, the authors believe, will be the eventual widespread acceptability of these control systems. This paper is aimed at bridging this gap between the theoretical specification of a complex discrete-time control system as described in a typical research paper and the obtaining of a first simulation. It provides a graph theoretical formulation of the issues involved, discrete algorithms to execute these tasks, and finally describes ISIM, an interpretive simulator which incorporates these features. These algorithms automate the simulation process, as seen by the user. The net result is a package which essentially requires only that the user type in a control system more or less verbatim from a technical paper to obtain a simulation output     

Assume–guarantee verification of nonlinear hybrid systems with Ariadne

In many applicative fields, there is the need to model and design complex systems having a mixed discrete and continuous behavior that cannot be characterized faithfully using either discrete or continuous models only. Such systems consist of a discrete control part that operates in a continuous environment and are named hybrid systems because of their mixed nature. Unfortunately, most of the verification problems for hybrid systems, like reachability analysis, turn out to be undecidable. Because of this, many approximation techniques and tools to estimate the reachable set have been proposed in the literature. However, most of the tools are unable to handle nonlinear dynamics and constraints and have restrictive licenses. To overcome these limitations, we recently proposed an open‐source framework for hybrid system verification, called Ariadne , which exploits approximation techniques based on the theory of computable analysis for implementing formal verification algorithms. In this paper, we will show how the approximation capabilities of Ariadne can be used to verify complex hybrid systems, adopting an assume–guarantee reasoning approach. Copyright © 2012 John Wiley & Sons, Ltd.     

Fuzzy-enhanced Adaptive Control for Flexible Drive System with Friction Using Genetic Algorithms

When a mechatronic system is in slow speed motion, serious effect of nonlinear friction plays a key role in its control design. In this paper, a stable adaptive control for drive systems including transmission flexibility and friction, based on the Lyapunov stability theory, is first proposed. For ease of design, the friction is fictitiously assumed as an unknown disturbance in the derivation of the adaptive control law. Genetic algorithms are then suggested for learning the structure and parameters of the fuzzy-enhancing strategy for the adaptive control to improve system's transient performance and robustness with respect to uncertainty. The integrated fuzzy-enhanced adaptive control is well tested via computer simulations using the new complete dynamic friction model recently suggested by Canudas de Wit et al. for modeling the real friction phenomena. Much lower critical velocity of a flexible drive system that determines system's low-speed performance bound can be obtained using the proposed hybrid control strategy.     

不确定参数下离散混沌系统的开闭环控制

在很多实际环境中,系统模型参数经常是不确定的,使离散混沌系统的控制策略可能不再有效。当参数不能直接观测时,开闭环控制方案将由于不精确的系统模型而失效。针对这个问题,提出了参数自适应开闲环控制策略来控制不确定参数的离散混沌系统。对开闲环控制在不确定参数条件下,参数变化满足控制需要的条件进行了讨论。同时,提出了参数自适应开闲环控制,将原有的开闭环控制推广到更大的范围。从Henon系统的仿真中,参数自适应开闲环控制策略显示了良好的抗扰动特性。     

死区非线性系统的全局收敛自适应控制

本文通过引入一类新的目标函数，对死区非线性系统建立了全局收敛的自适应控制算法，该算法可用于最小相应和非最小相位系统，并具有渐近最优的控制效果。仿真实验表明本文算法具有良好的动态性能。     

A parameter estimation perspective of continuous time model reference adaptive control

The problem of adaptive control of continuous time deterministic dynamic systems is re-examined. It is shown that the convergence proofs for these algorithms may be decomposed into “modules” dealing with estimation and control, yielding a “key technical lemma” analogous to that used successfully in the study of discrete time systems. The extra freedom provided by the modular structure is used to formulate existing algorithms in a common framework and to derive several new algorithms. It is also shown how least squares, as opposed to gradient, estimation can be used in continuous time adaptive control.