无模型控制器理论与应用的进展

本文介绍了无模型控制器又称非建模自适应控制器的基本理论和近年来在理论和应用方面研究的进展，无模型控制器的设计途径是一种非经典的途径。该途径的特点是：建模和反馈控制一体化，冲破经典PID的概念和控制器设计的线性框架，采用了功能组合设计方法。建模与控制一体化的手续，使设计出来的控制器具有一定的结构自适应性，本文还介绍了无模型腔制器大量成功应用的实例。     

从PID到无模型控制器

分析了各种新型PID控制方法控制原理及其存在的问题,介绍了无模型控制器(非建模自适应控制器)的基本理论和基本设计方法,比较了PID与无模型控制器的控制效果,说明无模型控制器冲破了经典PID的概念和控制器设计的线性框架,是一种结构自适应、建模与控制一体化途径的全新的控制器。实践表明,无模型器克服了PID无法对非线性强耦合系统实现稳定控制的弱点,取得了良好的控制效果。     

无模型控制律的基本形式收敛性分析

在实时建模与反馈控制一体化的思想下,利用"泛模型的基本形式",对无模型控制律的收敛性进行了定量和定性的分析,给出了控制律收敛的必要条件、特征参量选择方法以及影响收敛特性的因素.从理论上,进一步证明了无模型控制器设计方法的合理性,并为控制器的设计提供理论依据.分析结果表明了在考虑控制系统设计的建模问题时,可以脱离开"先建模后设计"的经典途径,只要从所谓的泛模型出发,所设计的无模型控制律的收敛性将得到保证.     

典型人工神经网络的结构、功能及其智能系统中的应用

人工神经网络已在各个领域得到广泛的应用，尤其是在智能系统中的非线性建模及其控制器的设计、模式分类与模式识别、联想记忆和优化计算等方面更是得到人们的极大关注；本文从网络在智能系统中建模及控制器设计的具体训练结构入手，详细介绍了BP网络在系统控制中的典型应用方式，并根据不同网络所具遥功能，从性能对比的角度对人工神经网络在上述各方方面的应用给予综述。     

结构未建模系统的变结构自适应控制器设计

本文对一类结构未建模的动态不确定系统，采用变结构控制与自适应控制相结合的方法，给出了一种鲁棒控制器设计方案。所提方案适用于系统已建模部分为非最小相位的系统，并能保证系统输入输出全局有界稳定。     

无拖曳卫星的推进器不确定下鲁棒控制器设计

以低轨环境下无拖曳卫星控制器设计为研究对象,对于给定结构的无拖曳卫星,考虑推进器增益存在不确定时的鲁棒控制器设计。首先,简要介绍无拖曳卫星的概念及原理;之后,对带有不确定性的无拖曳卫星控制系统进行分析与建模,并对其进行LFT不确定性建模,最后利用μ分析和D-K迭代设计出满足鲁棒性能的鲁棒控制器。仿真结果说明鲁棒控制器的有效性。     

PID控制的应用与理论依据

PID控制是自动控制中产生最早，应用最广的一种控制方法，从PID控制的结构形式，实际控制工程需求和实现条件分析了PID控制的独特优点，同时又介绍了二阶线性定常系统PID控制器的设计方法，叙述了高阶线性定常系统的特征建模原理，重点分析和推导了基于特征模型的带消除静差的二次型最优控制设计方法，证明了高阶线性定常系统和一大批非线性系统能用PID控制器实现位置恒值控制的基本原理，为随输出状态不同而选择不同P，I，D参数等各种人工调节方法提供了合理性解释，最后说明了PID控制器结构是智能控制的一种最基础单元。     

典型人工神经网络的结构、功能及其在智能系统中的应用

人工神经网络已在各个领域得到广泛的应用, 尤其是在智能系统中的非线性建模及其控制器的设计、模式分类与模式识别、联想记忆和优 化计算等方面更是得到人们的极大关注．本文从网络在智能系统中建模及控制器设计的具体 训练结构入手,详细介绍了BP网络在系统控制中的典型应用方式,并根据不同网络所具有的 功能,从性能对比的角度对人工神经网络在上述各方面的应用给予综述．     

无模型控制器的应用

无模型控制器在炼油、化工、电力、轻工等领域应用获得了良好的效果。介绍了无模型控制器在各领域的一系列应用实例，并对在应用中取得良好效果的原因进行了分析。指出无模型控制器是一种结构自适应控制器，它是“建模与控制一体化”，冲破P、I、D和线性框架的束缚的产物。     

全数字晶闸管功率控制器的设计

本文介绍了利用RISC结构的单片机作控制核心，设计多功能晶闸管功率控制器的基本思路。描述了功率控制器的硬件模块设计，控制器的各种控制特性分析，同时也介绍了软件的设计要点。实践表明，所介绍的设计方案和使用效果达到了国外同等产品的性能，具有极大的推广和使用价值。     

A new adaptive backstepping method for nonlinear control of turbine main steam valve

A new approach for nonlinear adaptive control of turbine main steam valve is developed. In comparison with the existing controller based on ＂classical＂ adaptive backstepping, this method does not follow the classical certaintyequivalence principle in the design of adaptive control law. We introduce this approach, for the first time, to power systems and present a novel parameter estimator and dynamic feedback controller for a single machine infinite bus （SMIB） system with steam valve control. This system contains unknown parameters such as reactance of transmission lines. Besides preserving useful nonlinearities and the real-time estimation of uncertain parameters, the proposed approach possesses better performances with respect to the response of the system and the speed of adaptation. The simulation results demonstrate that the proposed approach is better than the design based on ＂classical＂ adaptive backstepping in terms of properties of stability and parameter estimation, and recovers the performance of the ＂full-information＂ controller. Hence, the proposed method provides an alternative for engineers in applications.     

模拟电路实现的神经元控制器的仿真研究

该文研究一种适用于电动机控制的模拟电路实现的神经元自适应控制器。根据神经元的特性,将数字神经元控制器模拟化,获得模拟神经元控制器。该文还研究用MATLAB中的动态仿真工具SIMULINK对其进行仿真的方法。仿真结果表明模拟电路实现的神经元控制器比模拟PID具有更良好控制特性,并实现神经元权值的自动调节。该仿真方法为用电路仿真软件进行电路设计、仿真以及实际电路的实现打下良好的基础,提高设计效率。     

一类不确定线性系统的鲁棒自适应控制

针对一类同时具有匹配不确定性和不匹配政治面目 确定性的线性系统,首先采用李雅普诺夫稳定性定理,结合基于矩阵不等式的鲁棒控制器设计方法和变结构控制方法,设计鲁棒控制器,使得闭环系统是二次渐近稳定的,然后,利用自适应参数估计方法,设计具有匹配不确定性范数界估计能力的鲁棒自适应控制器,保证闭环系统的一致终结有界,在此基础上,进一步考虑系统中可能的未知不确定性,分析闭环系统的鲁棒性,并得到了相应的控制器设计方法。     

On the robust PID adaptive controller for exoskeletons: A particle swarm optimization based approach

This article proposes a robust PID adaptive controller for nonlinear systems with one or more degrees of freedom (DoF). The adaptive controller aims at minimizing the errors in trajectory tracking without requiring a prior modeling of the targeted nonlinear system. Furthermore, the proposed controller requires only the inputs and outputs of the system. And it is based on modified particle swarm optimization algorithm whose goal is to find the best PID parameters that optimize the execution of desired task by minimizing an objective function. The adaptation by the controller addresses two critical problems: The first problem is the instability of the control signal provided by the convergence phase of the classical PSO algorithm. This behavior adversely affects the lifetime of any actuator and, therefore, is undesirable. The second problem is the stagnation of the classical PSO algorithm after convergence at the immediately found optimal solution. The proposed adaptive PID controller is initially tested in simulation on a dynamical model of a robot manipulator evolving in the vertical plan. Which is followed by experimental tests performed on an actuated joint orthosis worn by human subjects having different morphologies. A comparative study with two other algorithms has been also conducted. Based on the obtained results, we conclude the efficiency of the proposed approach.     

磁悬浮球系统的非线性自适应控制方法

针对磁悬浮球系统被控对象变化时控制器自适应问题,提出了一种反馈线性化和在线参数辨识相结合的非线性自适应控制方法。基于状态反馈精确线性化方法建立磁悬浮球系统的数学模型,通过状态反馈设计了一种非线性控制器,并给出了控制器参数的在线辨识方法。MATLAB平台上在线实验结果表明,与反演滑模自适应控制方法相比,提出的方法无须在平衡位置近似线性化,可以在平衡位置实现对不同对象的自适应控制,且具有理想的稳态调节性能。     

Adaptive event‐triggered global fast finite‐time control for a class of uncertain nonlinear systems

This article designs an adaptive event‐triggered controller to solve the problem of global finite‐time stabilization for a class of uncertain nonlinear systems. By using the symbol function technique, the event‐triggered error is completely compensated, the adaptive technique and the back‐stepping method are simultaneously applied to the controller design, and the new way of designing controller is completed on the basis of fast finite‐time stability theory. Subsequently, taking Lyapunov stability theorem into account, the system stability is proved, and the system is demonstrated by contradiction to be non‐zeno. Finally, giving a simulation example to display the feasibility of this method.     

Adaptive Controller Design for Unknown Systems Using Measured Data

This paper presents a measurement‐based adaptive control design approach for unknown systems working over a wide range of operating conditions. Traditional control design approaches usually require the availability of a mathematical model. However, it has been shown in many practical situations that, due to complex dynamics of physical systems, some simplifying assumptions are made for the derivation of mathematical models. Hence, controller design based on simplified models may result in degradation of the desired closed‐loop performance. Data‐based control design approaches can be viewed as an alternative approach to model‐based methods. Most data‐based control methods available in the literature aim to design controllers for unknown systems that operate only at a given operating point. However, the dynamical behavior of plants may change for different operating conditions, which makes the task of designing a controller that works over the entire range of operating conditions more challenging. In this paper, we address such a problem and propose to design adaptive controllers based on measured data. Such a proposed method is based on designing a set of measurement‐based controllers validated at a finite set of pre‐specified operating points. Then, the parameters of the adaptive controller are obtained by interpolating between the set of pre‐designed controller parameters to derive a gain‐scheduling controller. Moreover, low‐order adaptive controllers can be designed by simply selecting the desired controller structure. The efficacy of the proposed approach is experimentally validated through a practical application to control a heating system operated over a large range of flow rate.     

Sliding Mode Control for Flexible-link Manipulators Based on Adaptive Neural Networks

This paper mainly focuses on designing a sliding mode boundary controller for a single flexible-link manipulator based on adaptive radial basis function (RBF) neural network. The flexible manipulator in this paper is considered to be an Euler-Bernoulli beam. We first obtain a partial differential equation (PDE) model of single-link flexible manipulator by using Hamiltons approach. To improve the control robustness, the system uncertainties including modeling uncertainties and external disturbances are compensated by an adaptive neural approximator. Then, a sliding mode control method is designed to drive the joint to a desired position and rapidly suppress vibration on the beam. The stability of the closed-loop system is validated by using Lyapunov’s method based on infinite dimensional model, avoiding problems such as control spillovers caused by traditional finite dimensional truncated models. This novel controller only requires measuring the boundary information, which facilitates implementation in engineering practice. Favorable performance of the closed-loop system is demonstrated by numerical simulations.