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。     

连续搅拌釜式反应器的鲁棒最优控制

针对一类带不确定性的连续搅拌釜式反应器,提出基于滑模控制理论的鲁棒最优控制算法。输入输出线性化方法用于线性化对象模型,假设系统的不确定因素有界,滑模面采用积分型滑模面以确保系统稳态误差为零,将线性二次型理论用于等效控制律的设计中,保证了系统的性能指标最优,自适应滑模切换控制增益的选取在降低系统抖振的前提下补偿了系统的不确定因素及外部扰动,实现了控制器的鲁棒最优。通过仿真实验表明,提出的控制器对匹配的不确定性因素及外部扰动具有鲁棒性,且闭环系统的性能指标最优。     

发泡成型机蒸汽压力的神经网络自适应逆控制研究

针对发泡成型机蒸汽压力的非线性系统存在控制精度差和动态性能不佳的问题,设计了基于神经网络的自适应逆控制方法。建立了发泡成型机蒸汽压力的参考模型,利用参考模型的输入输出对训练单层神经网络逆模型作为逆控制器,并采用LMS自适应算法在线调节神经网络权向量。通过与PID控制器进行对比实验,结果表明,所设计的神经网络自适应逆控制方法提高了蒸汽压力的稳定性以及快速响应能力,几乎不存在系统超调。     

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。     

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。     

多故障并发不确定系统的鲁棒完整性容错控制

针对传统容错控制方法难以保证非线性系统在执行器和传感器多故障并发情形下的稳定性问题,研究了一类时滞不确定模糊系统的鲁棒完整性容错控制方法。建立了基于T-S模糊逻辑的不确定非线性模型,定义执行器和传感器故障阵的标准归一化形式,在利用Newton-Leibniz公式变换系统结构的基础上,根据线性矩阵不等式技术给出了鲁棒容错控制器存在的时滞相关性充分条件,以保证整个闭环系统在执行器和(或)传感器发生故障时的稳定性,同时满足给定的广义鲁棒性能约束,联合抑制扰动、初始状态和时滞状态对系统性能的影响。最后仿真结果验证了方法的必要性和可行性。     

不确定线性饱和系统的非脆弱控制

考虑带有饱和执行器的不确定系统,研究了该系统的非脆弱控制和吸引域估计问题。与传统的二次Lyapunov函数不同,引入了分段二次Lyapunov函数,给出了保守性更小的稳定性条件,所设计的控制器不再是单一的线性控制器,而是在多个不同的线性控制器之间切换的非线性控制器;同时给出了闭环系统最大吸引域的估计方法,状得比传统方法更人的吸引域。仿真结果也表明本文方法的有效性。     

陶瓷取坯机械手H∞鲁棒自适应跟踪控制

针对具有不确定和干扰输入的二自由度电动陶瓷取坯机械手系统，基于李雅普诺夫函数递推设计方法设计了H∞鲁棒自适应跟踪控制器，该控制器不仅可保证跟踪误差闭环系统的一致有界稳定性，而且使得由干扰力短到跟踪误差评价信号的L2增益小于给定的值，同时本文也提出了不同求解HJI不等式设计陶瓷取坯机械手H∞鲁棒控制器的方法。仿真结果表明，所设计的控制器具有良好的跟踪性能和较强的鲁棒自适应性。     

化工过程多回路PID控制系统模式切换参数自整定

化工过程一般为多变量系统,但其主要控制方案为分散多回路PID常规控制。由于多变量系统内部存在不同程度的耦合作用,各控制回路之间存在相互影响,当其他回路进行手动/自动模式切换时,本回路等效被控对象将会发生突变,导致本回路的原有控制参数不能适应等效被控对象的变化,造成控制性能下降,甚至闭环系统不稳定。为避免这种情况的发生,从整个系统的角度研究控制回路模式切换时的稳定性,采用多变量频域Nyquist阵列设计法。基于对角优势下正Nyquist稳定性判据,从Gershgorin圆边界点的角度定量分析各个控制回路在模式切换前后的稳定性变化程度,从而确定各回路控制器增益的调整方向及程度,实现各回路的控制器参数在控制回路模式切换瞬间的自动整定,尽可能抵消控制回路模式切换对整个系统的扰动,保证整个系统的闭环稳定性。以Shell公司重油分馏塔的多回路PID控制系统为例,将3个PID控制回路依次投用时,根据Gershgorin圆边界点进行控制参数的自整定,闭环系统仍能保持一定的控制性能,否则闭环系统将不稳定。     

基于ANFIS的一类免疫聚类建模方法的研究

针对复杂的非线性系统较难建立精确的数学模型这一难题,提出一种基于模糊推理系统的(ANFIS)自适应方法方法对系统进行建模。建模过程中为了给ANFIS赋合适初始值,选用人工免疫聚类算法对输入数据进行处理。最后利用MATLAB离线对一种非线性系统的进行实验仿真,仿真结果表明了该方法的有效性。     

Nonlinear adaptive switching control for a class of non-affine nonlinear systems

An improved nonlinear adaptive switching control method is presented to relax the assumption on the higher order nonlinear terms of a class of discrete-time non-affine nonlinear systems. The proposed control strategy is composed of a linear adaptive controller, a neural network (NN) based nonlinear adaptive controller and a switching mechanism. An incremental model is derived to represent the considered system and an improved robust adaptive law is chosen to update the parameters of the linear adaptive controller. A new performance criterion of the switching mechanism is designed to select the proper controller. Using this control scheme, all the signals in the system are proved to be bounded. Numerical examples verify the effectiveness of the proposed algorithm.     

基于自适应模糊神经网络的非线性系统模型预测控制

针对非线性动态系统的控制问题,提出了一种基于自适应模糊神经网络(adaptive fuzzy neural network, AFNN)的模型预测控制(model predictive control, MPC)方法。首先,在离线建模阶段,AFNN采用规则自分裂技术产生初始模糊规则,采用改进的自适应LM学习算法优化网络参数;然后,在实时控制过程,AFNN根据系统输出和预测输出之间的误差调整网络参数,从而为MPC提供一个精确的预测模型;进一步,AFNN-MPC利用带有自适应学习率的梯度下降寻优算法求解优化问题,在线获取非线性控制量,并将其作用到动态系统实施控制。此外,给出了AFNN-MPC的收敛性和稳定性证明,以保证其在实际工程中的成功应用。最后,利用数值仿真和双CSTR过程进行实验验证。结果表明,AFNN-MPC能够取得优越的控制性能。     

Neural network‐based optimal iterative controller for nonlinear processes

A new optimal iterative neural network‐based control (OINNC) strategy with simple computation and fast convergence is proposed for the control of processes with nonlinear dynamics. The process dynamics is captured by a forward neural network, and the control is determined by a simple iterative optimization during each sampling interval based on a linearized neural network model. In addition, a feedback control is incorporated into the system to compensate for any model mismatches and to reject disturbances. With the proposed system, the tracking error is shown to be confined to the origin. An application of the proposed OINNC scheme to a nonlinear process results in superior performance when compared with a well‐tuned conventional PID controller.     

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.     

Online machine learning modeling and predictive control of nonlinear systems with scheduled mode transitions

This work develops a model predictive control (MPC) scheme using online learning of recurrent neural network (RNN) models for nonlinear systems switched between multiple operating regions following a prescribed switching schedule. Specifically, an RNN model is initially developed offline to model process dynamics using the historical operational data collected in a small region around a certain steady-state. After the system is switched to another operating region under a Lyapunov-based MPC with suitable constraints to ensure satisfaction of the prescribed switching schedule policy, RNN models are updated using real-time process data to improve closed-loop performance. A generalization error bound is derived for the updated RNN models using the notion of regret, and closed-loop stability results are established for the switched nonlinear system under RNN-based MPC. Finally, a chemical process example with the operation schedule that requires switching between two steady-states is used to demonstrate the effectiveness of the proposed RNN-MPC scheme.     

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。     

Multi-objective nonlinear model predictive control through switching cost functions and its applications to chemical processes

This paper proposes a switching multi-objective model predictive control (MOMPC) algorithm for constrained nonlinear continuous-time process systems. Different cost functions to be minimized inMPC are switched to satisfy different performance criteria imposed at different sampling times. In order to ensure recursive feasibility of the switching MOMPC and stability of the resulted closed-loop system, the dual-mode control method is used to design the switching MOMPC controller. In this method, a local control law with some free-parameters is constructed using the control Lyapunov function technique to enlarge the terminal state set of MOMPC. The correction termis computed if the states are out of the terminal set and the free-parameters of the local control laware computed if the states are in the terminal set. The recursive feasibility of the MOMPC and stability of the resulted closed-loop system are established in the presence of constraints and arbitrary switches between cost functions. Finally, implementation of the switching MOMPC controller is demonstrated with a chemical process example for the continuous stirred tank reactor.     

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.