基于神经网络和多模型的非线性自适应PID控制及应用

针对一类未知的单输入单输出离散非线性系统,提出了基于神经网络和多模型的非线性自适应PID控制方法。该方法由线性自适应PID控制器、神经网络非线性自适应PID控制器以及切换机构组成。采用线性自适应PID控制器可保证闭环系统所有信号有界;采用神经网络非线性自适应PID控制器可改善系统性能;通过引入合理的切换机制,能够在保证闭环系统稳定的同时,提高系统性能。理论分析表明,该方法能够保证闭环系统所有信号有界,如果适当地选择神经网络的结构和参数,系统的跟踪误差将收敛于任意给定的紧集。将所提出的方法应用于连续搅拌反应釜,仿真结果验证了所提出方法的有效性。由于该方法基于增量式数字PID控制器,在工业过程中有着广阔的应用前景。     

基于ANFIS与多模型的一类非线性系统的自适应控制方法

针对一类不确定的离散时间零动态不稳定非线性系统,提出了一种基于自适应神经模糊推理系统(ANFIS)与多模型的非线性自适应控制方法。该方法由线性鲁棒自适应控制器,基于ANFIS的非线性自适应控制器以及切换机制组成。线性控制器用来保证闭环系统输入输出信号有界,非线性控制器用来改善系统性能。切换机制通过对上述两种控制器的切换,保证闭环系统输入输出有界的同时,改善系统性能。在采用ANFIS作为系统未建模动态补偿器时,首先用一个连续、单调、可逆的一一映射把可能无界的未建模动态的定义域转化成一个有界闭集,保证了ANFIS的万能逼近特性成立的前提条件。而且,ANFIS能减小BP神经网络收敛速度慢和容易陷入局部极小的问题,改善了控制效果。建立了保证系统稳定性的引理,并给出了闭环系统的稳定性和收敛性分析。通过仿真比较,说明了所提方法的有效性。     

Generic model controller tuning for chemical processes with input saturation

Control in the face of process input constraints is very common and of great practical importance in the processing industries. Generic Model Control (GMC) is a model‐based control framework for both linear and nonlinear systems. In this paper, a constrained GMC controller tuning approach using a nonlinear least squares technique is proposed. This tuning approach is simple to apply. For a SISO GMC control system with input saturation, the tracking performance is significantly improved by adding a simple heuristic switching strategy. The effectiveness of the proposed controller tuning approach is demonstrated using dynamic simulations and MIMO real‐time experiments.     

基于复合神经网络的离散非线性系统建模与控制

An adaptive inverse controller for nonliear discrete-time system is proposed in this paper. A compound neural network is constructed to identify the nonlinear system, which includes a linear part to approximate the nonlinear system and a recurrent neural network to minimize the difference between the linear model and the real nonlinear system. Because the current control input is not included in the input vector of recurrent neural network (RNN), the inverse control law can be calculated directly. This scheme can be used in real-time nonlinear single-input single-output (SISO) and multi-input multi-output (MIMO) system control with less computation work. Simulation studies have shown that this scheme is simple and affects good control accuracy and robustness.     

参数不确定非线性系统切换控制

针对一类含有不确定参数的非线性系统,基于其参数的不确定范围,设计多个滑模变结构控制器。在此基础上,基于一给定的指标切换函数构造切换控制器。在确保系统Lyapunov稳定性的前提下,被控对象的控制器按照预先设定好的切换条件,在多个控制器之间相互切换,从而极大地改善系统的瞬态响应。以工业机器人手臂为研究对象,针对机器人手臂运动方程构造滑模变结构控制器,并设计以输出误差为自变量的指标切换函数,基于此切换函数构造切换控制器,使机械手的控制器在多个控制器之间进行切换。针对不同的参数变化范围,研究切换控制的有效性。多个仿真实例表明切换控制能极大地改善控制品质。     

A MODEL-BASED DIRECT ADAPTIVE PI CONTROLLER FOR NONLINEAR PROCESS CONTROL

This article proposes a model-based direct adaptive proportional-integral (PI) controller for a class of nonlinear processes whose nominal model is input-output linearizable but may not be accurate enough to represent the actual process. The proposed direct adaptive PI controller is composed of two parts: the first is a linearizing feedback control law that is synthesized directly based on the process's nominal model and the second is an adaptive PI controller used to compensate for the model errors. An effective parameter-tuning algorithm is devised such that the proposed direct adaptive PI controller is able to achieve stable and robust control performance under uncertainties. To show the robust stability and performance of the direct adaptive PI control system, a rigorous analysis involving the use of a Lyapunov-based approach is presented. The effectiveness and applicability of the proposed PI control strategy are demonstrated by considering the time-dependent temperature trajectory tracking control of a batch reactor in the presence of plant/model mismatch, unanticipated periodic disturbances, and measurement noises. Furthermore, for use in an environment that lacks full-state measurements, the integration of a sliding observer with the proposed control scheme is suggested and investigated. Extensive simulation results reveal that the proposed model-based direct adaptive PI control strategy enables a highly nonlinear process to achieve robust control performance despite the existence of plant/model mismatch and diversified process uncertainties.     

单级倒立摆的自适应模糊控制方法

倒立摆系统是一个复杂的、不稳定的非线性系统,为了使其具有更好的适应性和鲁棒稳定性,我们采用模糊控制器与监督控制器相结合的方法来对其进行控制。通过MATLAB环境下的仿真并对仿真结果进行分析,验证了此方法按照预定的要求精确、稳定、快速地控制倒立摆系统,实现既定目标的性能。     

化学反应器的神经元自适应观测和非线性控制

提出了一种基于神经网络的自适应观测和非线性控制策略,证明了自适应观测器的收敛件和非线性控制系统的稳定性,将其用于连续搅拌釜式放热反应器的浓度控制。根据可在线测量的反应温度,在线估计不可在线测量的反应物浓度和辨识Arrhenius指前因子,并利用重构的状态信息设计出带约束的非线性控制策略。仿真结果表明,观测器/控制器的组合提供了满意的闭环特性,证实了本文方法的有效性。     

非线性多变量系统的多模型广义预测解耦控制

针对实际工业过程中多变量系统存在着非线性、工况范围广、耦合强的特点,提出基于设定值观测器的非线性多模型广义预测解耦控制算法。该方法由线性广义预测控制器、一种新的设定值观测器和切换机构组成。理论分析和仿真结果表明,该控制策略不但可以保证闭环系统B IBO稳定和渐近收敛,而且能够得到很好的控制效果。     

A simple adaptive control strategy for temperature trajectory tracking in batch processes

In this paper, a simple adaptive control strategy is suggested for temperature tracking control of batch processes. A nonlinear controller, which is in structure very simple and consists of a single parameter, is proposed. To enable this controller to control a batch process adaptively, a simple parameter tuning algorithm is derived based on the Lyapunov stability theorem. The proposed adaptive control scheme is directly operational, which does not depend on process model and the only a priori process information required is the system response direction. To demonstrate the effectiveness and applicability of the proposed scheme, illustrative examples are provided. Extensive simulation results reveal that the proposed adaptive control strategy appears to be a simple and effective approach to batch process control, which provides robust control despite the wide range of operating conditions and nonlinear dynamics of the system.     

APPLICATION OF FUZZY ADAPTIVE CONTROLLER IN NONLINEAR PROCESS CONTROL

In general, physical processes are usually nonlinear and control system design based on the linearization technique cannot control the process well for a wide range of operation. Use of the variable transformation method may not always solve the problem. In this paper, a fuzzy adaptive controller is proposed to control the nonlinear process. The CSTR control problem has also been considered. The results are compared with the method of nonlinear model predictive control (NMPC) with constrained and unconstrained control variables. A fuzzy model-following control system scheme is also proposed. The results show that the proposed controller is a feasible control structure for a nonlinear or parameter-variations process control.     

Feedback linearizing control of a fluidized bed reactor

The design of an adaptive nonlinear controller for the control of a fluidized bed reactor is derived by using exact linearization techniques. Reset action and parameter adaptation are used to make more robust the precise compensation of nonlinear terms, which is called for in the linearization technique. A nonlinear antiwindup mechanism is introduced to handle reset windup problem and to provide fast response without large overshoot. Simulation results show that the proposed adaptive controller guarantees good setpoint tracking. The developed estimation algorithm allows accurate estimation of the parameters for which the regressor component is not zero.     

一种基于多模型切换的阶梯式广义预测控制算法

针对一类模型参数突变的系统,提出一种基于多模型切换的阶梯式广义预测控制算法。采用多个固定模型、一个常规自适应模型和一个可重新赋初值的自适应模型并行辨识系统的动态特性。多个固定模型可以提高系统的暂态性能,常规自适应模型可以保证系统的稳定性,可重新赋初值的自适应模型可以进一步提高系统的暂态性能。在每个采样时刻基于性能指标切换到最优的局部模型作为当前模型,设计阶梯式广义预测控制器,从而实现系统全局的控制。最后的仿真结果表明,其控制效果明显优于单一模型的控制器。     

基于自适应模糊PID的水泥窑分解炉温控系统研究

对非线性大滞后等特殊的系统,存在常规PID控制器控制效果不甚理想的问题,为此针对水泥窑分解炉温度控制系统,提出一种参数自适应模糊PID控制策略,并进行了仿真研究。结果表明:该控制系统响应速度快,调节时间短,控制精度高,控制效果优于传统的PID控制器。     

DYNAMIC FUZZY ADAPTIVE CONTROLLER FOR pH

Control of pH processes is very difficult due to nonlinear dynamics, high sensitivity at the neutral point, and changes in the concentrations of known or unknown chemical species. In this study, a dynamic fuzzy adaptive controller (DFAC) with a new inference mechanism is proposed and applied for the control of pH processes. The DFAC consists of a low-level basic control phase with a minimum rule base and a high-level dynamic learining phase with an updating mechanism to interact and modify the control rule base. The DFAC can self-adjust its fuzzy control rules using information from the process during on-line control and create new fuzzy control rules or modify the present control rules using its learning capability from past control trends. The controller is evaluated by applying it to a weak acid-strong base pH process with input disturbances and to another pH process that involve that has changes in acidic/buffering streams. The results of the DFAC with the new inference mechanism are compared with the known inference mechanisms, the fuzzy controller, the conventional PI controller, and also with an adaptive PID controller. The proposed DFAC provides better performance for set point tracking of the pH and rejection of load disturbances and buffering affects.     

Nonlinear adaptive control algorithm for a multicomponent batch distillation column

In this article, a nonlinear adaptive control strategy is proposed for a multicomponent batch distillation column. The hybrid control scheme consists of a generic model controller (GMC) and a nonlinear adaptive state estimator (ASE). In the first part of the study, an adaptive observer is designed aiming to estimate the partially known parameters based on the measured compositions in the presence of process/predictor mismatch. The open-loop dynamic behavior of the developed ASE estimator is investigated under initialization error, disturbance, and uncertain parameters. In the subsequent part, the adaptive GMC-ASE controller (GMC control structure in conjunction with ASE estimator) has been synthesized for the example distillation column. A simulation-based comparative study has been conducted between the derived nonlinear GMC-ASE control algorithm and a gain-scheduled proportional integral (GSPI) law in terms of constant composition control. The proposed adaptive control scheme is shown to be quite promising due to the exponential error convergence capability of the ASE estimator in addition to the high-quality performance of the GMC controller.     

A tuning of the nonlinear PI controller and its experimental application

Many industrial chemical process control systems consist of conventional PID and nonlinear controllers, even though many advanced control strategies have been proposed. In addition, nonlinear control methods are widely used even for linear processes to achieve better control performance compared with linear PID controllers. However, there are few tuning methods for these nonlinear controllers. In this work, we suggest new controller tuning methods for the error square type of nonlinear PI controller. These control methods can be applied to a large number of linear and nonlinear processes without changing control structures. We also propose new tuning rules for integrating processes. In addition, we suggest application guidelines for performing the proposed tuning rules at the pilot scale multistage level control system. Finally, in this work we confirmed good control performances of the proposed tuning methods through both simulation studies and experimental studies.     

复合控制器及其在连消中的应用

由于常规PID控制方式对非线性、大滞后对象难以进行有效的控制,模糊控制具有很好的动态特性,所以结合常规PID和模糊控制的优势设计了参数自调整Fuzzy-PID复合控制器。通过模糊推理实现参数自调整,以使控制器能够适应不同对象和对象的不同状态。采用模糊推理的方法完成两种控制方式的平稳过渡。对某制药厂连消温度的控制表明,该控制器可以大幅度提高控制精度和缩短系统响应时间,从而避免了染菌事故的发生,提高了发酵单位。     

基于永磁同步电动机模型的自适应混沌控制

基于永磁同步电动机的系统模型,运用简单的线性状态反馈方法,在系统参数给定和未给定的情况下针对系统的混沌现象提出了一种简单快速的混沌控制方法。当系统参数给定时使混沌系统的指数趋于稳定并给出反馈增益的范围,在系统参数未给定的时候设计一种自适应控制器,该控制器克服了以往一般自适应控制器中空置率不连续的缺点。最后通过Matlab进行仿真证明了所设计方法的可行性。