Predictive Functional Control Based on Fuzzy Model: Comparison with Linear Predictive Functional Control and PID Control

The implementation of the fuzzy predictive functional control (FPFC) on the magnetic suspension system is presented in the paper. The magnetic suspension system was in our case the pilot plant for magnetic bearing and is an open-loop unstable process, therefore a lead compensator was used to stabilize it. The high quality control requirements were a-periodical step response and zero steady-state error. Adding the integrator to a feedback causes overshoot. The solution to the problem was cascade control with fuzzy predictive functional controller in the outer loop. To cope with the unknown model parameters and the nonlinear nature of the magnetic system, a fuzzy identification based on FNARX model was used. After successful validation the obtained fuzzy model was used for controller design. The FPFC is compared with a cascade linear predictive functional control (PFC) and PID control. The results we obtained with the FPFC are very promising and hardly comparable with conventional control techniques.     

Adaptive fuzzy identification and predictive control for industrial processes

This paper proposes a method for adaptive identification and control for industrial applications. The learning of a T–S fuzzy model is performed from input/output data to approximate unknown nonlinear processes by a hierarchical genetic algorithm (HGA). The HGA approach is composed by five hierarchical levels where the following parameters of the T–S fuzzy system are learned: input variables and their respective time delays, antecedent fuzzy sets, consequent parameters, and fuzzy rules. In order to reduce the computational cost and increase the algorithm’s performance an initialization method is applied on HGA. To deal with nonlinear plants and time-varying processes, the T–S fuzzy model is adapted online to maintain the quality of the identification/control. The identification methodology is proposed for two application problems: (1) the design of data-driven soft sensors, and (2) the learning of a model for the Generalized predictive control (GPC) algorithm. The integration of the proposed adaptive identification method with the GPC results in an effective adaptive predictive fuzzy control methodology. To validate and demonstrate the performance and effectiveness of the proposed methodologies, they are applied on identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment system; and on control of a simulated continuous stirred tank reactor (CSTR) and on a real experimental setup composed of two coupled DC motors. The results are presented, showing that the developed evolving T–S fuzzy model can identify the nonlinear systems satisfactorily and it can be used successfully as a prediction model of the process for the GPC controller.     

Predictive functional control based on fuzzy model: magnetic suspension system case study

Fuzzy model based predictive functional controller (FPFC) is applied to the magnetic suspension system—a pilot plant for magnetic bearing. High quality control requirements are short settle time with a-periodical step response and zero steady-state error. Open loop unstable process was stabilised with linear lead compensator. The FPFC was used as a cascade controller. Due to some model uncertainties, the Takagi–Sugeno fuzzy model of stabilised system was obtained using fuzzy identification. Comparing to PID, it improved quality and robustness performance. With its computational efficiency, it proved to be ideal solution for high sampling frequency systems.     

基于快速算法的模糊神经网络自适应控制

针对过程控制中被控对象常具有非线性、不确定性及参数时变等复杂因素,而难以建立精确的数学模型的情况,提出了一种基于快速学习算法的模糊神经网络自适应预测控制方案。该方案用神经网络作辨识器,模糊神经网络作控制器来实现非线性系统的自适应预测控制。为了克服传统的梯度下降法收敛速度慢、容易陷入局部极小值的缺点,该方案采用递推最小二乘法训练模糊神经网络。仿真结果表明,该方案可以实现模糊控制和神经网络的优势互补,对不确定非线性系统具有很好的控制效果。     

基于T-S 模型的模糊预测控制研究

提出一种基于T—S模型的模糊预测控制策略．利用模糊聚类算法高线辨识T—S模型，采用带遗忘因子的递推最小二乘法进行模型参数的选择性在线学习；对模糊模型在每一采样点进行线性化，将T—S模型表示的非线性系统转化为线性时变状态空间模型，并将约束非线性优化问题转化为线性二次规划问题，解决了非线性预测控制中如何获得非线性模型和非线性优化在线求解的难题．将预测域内的线性模型序列作为预测模型，减小了模型误差，提高了控制性能．pH中和过程的仿真验证了该方法的有效性．     

基于T-S模型的自适应模糊广义预测控制

对一类非线性系统,利用一种基于模糊规则的快速模糊辨识方法建立起系统的T—S模型,并基于该模型应用局部递推最小二乘方法根据采样值对模型参数进行在线修正,根据系统动态线性化模型采取广义预测控制策略,从而实现了基于T—S模糊模型的非线性系统自适应模糊预潮控制。与以往的模糊广义预测控制算法相比,此方法简单,而且较大地减少计算量,适合于在线控制。通过仿真研究验证所提方法的有效性。     

Fuzzy adaptive predictive flow control of ATM network traffic

In order to exploit the nonlinear time-varying property of network traffic, the traffic flow from controlled sources is described by a fuzzy autoregressive moving-average model with auxiliary input (fuzzy ARMAX process), with the traffic flow from uncontrolled sources (i.e., cross traffic) being described as external disturbances. In order to overcome the difficulty of the transmission delay in the design of congestion control, the fuzzy traffic model is translated to an equivalent fuzzy predictive traffic model. A fuzzy adaptive flow control scheme is proposed to avoid congestion at high utilization while maintaining good quality of service. By use of fuzzy adaptive prediction technique, the difficulties in congestion control design due to nonlinearity, time-varying characteristics, and large propagation delay can be overcome by the proposed adaptive traffic control method. A comparative evaluation is also given to show the superiority of the proposed method.     

一种基于T-S模糊模型的自适应预测函数控制

针对离散时间非线性系统,提出了一种基于T-S模糊模型的自适应预测函数控制算法。该算法利用加权递推最小二乘法在线辨识T-S模糊模型后件参数,以克服模型失配对系统性能的影响。根据辨识得到的模型参数直接递推计算模型的预测输出,而不需要求解Diophantine方程,进而直接递推求解预测控制律,而不需要求解矩阵逆。仿真结果表明,该算法具有良好的跟踪性能和较强的鲁棒性。     

核学习自适应预测控制器的在线更新方法

针对非线性过程控制器的设计问题,将基于稀疏核学习的一种具有解析形式的自适应预测控制算法与选择性递推核学习相结合.该在线核学习模型可以通过递推算法进行节点增长和删减的有效更新.因此,所提出的控制器复杂度可控,且能学习过程的时变等特性,从而获得更好的性能.通过一非线性时变过程的仿真研究,验证了所提出的核学习控制器较传统的PID和无在线更新的核学习控制器等具有更好的自适应能力和鲁棒性.     

Adaptation of diagonal recurrent neural network model

An adaptive direct recurrent neural network model is developed for nonlinear dynamic system modelling in this paper. The model adaptation is achieved with the extended Kalman filter (EKF). A novel recursive algorithm is proposed to calculate the Jacobian matrix in the model adaptation so that the algorithm is simple and converges fast. The effectiveness of the developed adaptive model is demonstrated by applying to modelling a simulated continuous stirred tank reactor (CSTR). The model converges to the new process dynamics very quickly after a constant disturbance is added, and therefore can be used as an adaptive model in the adaptive model predictive control or internal model control for time-varying systems or fault tolerant control of nonlinear systems.     

一类非齐次时变线性模型及自适应预测控制策略

通过分析系统闭环预测存在的缺陷，为了改善系统的预测性能、便于系统辨识和实现自适应控制，提出了一类适合于一般非线性系统的非齐次时变线性模型，给出并证明了其存在性定理。而后给出了这类非齐次时变线性模型的渐消记忆递推最小二乘参数估计，推出了基于该类模型的自适应预测控制算法。仿真结果表明基于该类模型的自适应预测控制策略具有优良的控制品质。     

一种非线性模型的在线辨识方法

提出一种用于非线性模型在线辨识的模糊算法。该算法将非线性输入输出系统用时变线性系统模型来拟和。并把此非线性系统模型表示成模糊模型的形式,用在线调节模糊模型的方法来辨识时变线性模型的相关参数。在以往的模糊辨识方法中,均未给出在线调整非线性系统的模糊辨识算法。将递推模糊聚类方法与卡尔曼滤波法用于在线调整模糊模型参数,仿真算例表明了此算法的有效性与良好的实用价值。     

未知多变量非线性系统自适应模糊预测控制

对一类未知多变量非线性系统提出了直接自适应模糊预测控制方法,此方法首先对被控对象提出了线性时变子模型加非线性子模型的预测模型,然后直接用模糊逻辑系统组成的向量来设计预测控制器,并基于时变死区函数对控制器中的未知向量和广义误差估计值中的未知矩阵进行自适应调整．文中证明了此方法可使广义误差向量估计值收敛到原点的一个邻域内．     

基于自适应递归模糊神经网络的污水处理控制

针对污水处理过程中具有的非线性、大时变等特征,提出了一种基于自适应递归模糊神经网络(recurrent fuzzy neural network,RFNN)的污水处理控制方法.该方法利用自适应RFNN识别器建立污水处理过程的非线性动态模型,建立的模型可以为RFNN控制器提供污水处理过程中的状态变量信息,保证了控制器根据系统响应调整操作变量的精确性;并且RFNN辨识器及RFNN控制器基于自适应学习率进行学习,确保了递归模糊神经网络的收敛精度和速度,并通过构造李雅普诺夫函数证明了此算法的收敛性;最后,基于基准仿真模型(benchmark simulation model 1,BSM1)平台进行仿真实验.结果表明,与PID、模型预测控制及前馈神经网络相比,该方法对污水处理中溶解氧浓度和硝态氮浓度的跟踪控制精度具有明显的提升.     

污水处理过程递推双线性子空间建模及无模型自适应控制

污水处理过程中, 生化反应硝态氮浓度和溶解氧浓度是决定出水水质好坏的两个最关键变量, 难以采用常规基于模型的方法进行有效控制. 本文基于数据驱动建模与控制技术, 提出一种污水处理过程递推双线性子空间辨识(Recursive bilinear subspace identification, RBLSI)建模和无模型自适应控制方法. 首先, 针对污水处理过程的非线性时变动态特性, 采用最小二乘递推双线性子空间辨识方法建立污水处理生化反应过程具有参数自适应能力的递推双线性模型; 其次, 基于建立的数据驱动模型, 采用基于多参数灵敏度分析(Multi-parameter sensitivity analysis, MPSA)和遗传粒子群优化(Genetic algorithm-particle swarm optimization, GA-PSO)算法的无模型自适应控制(Model-free adaptive control, MFAC)方法对硝态氮和溶解氧浓度进行直接数据驱动控制; 最后, 数据实验及其比较分析表明了所提方法的有效性和优越性.     

Fuzzy dynamic characteristic modeling and adaptive control of nonlinear systems and its application to hypersonic vehicles

In this paper, a fuzzy dynamic characteristic modeling and adaptive control method is proposed for a class of nonlinear systems. By employing fuzzy dynamic characteristic model, the controlled plant is described as a slowly time-varying fuzzy system, wherein the parameters are estimated online by using recursive Least-Squares algorithm. Under this framework, a fuzzy adaptive controller is constructed, and the stability condition of the closed-loop system is also derived. The main advantage of the proposed m...     

一种制动机气缸压力控制方法

为实现对具有非线性、时变和滞后等特性的机车制动系统的制动气缸的精确控制,提出一种气缸压力控制方法;该方法利用模糊控制领域的T-S模糊建模方法对容积室压力控制进行精确建模,通过BP算法学习得到系统的参数,利用模糊C平均聚类方法初始化模型的前件参数,采用带遗忘因子的递推最小二乘法在线修正模型的后件参数;得到系统精确的模型后再运用预测控制领域中基于模型的广义预测控制算法,实现对制动机气缸压力的精确控制;实际应用结果表明,该方法具有控制响应速度快、超调量小、自适应能力强、控制稳定等优点。     

Adaptive discrete-time sliding-mode control of nonlinear systems described by Wiener models

In this paper, we propose an adaptive control scheme that can be applied to nonlinear systems with unknown parameters. The considered class of nonlinear systems is described by the block-oriented models, specifically, the Wiener models. These models consist of dynamic linear blocks in series with static nonlinear blocks. The proposed adaptive control method is based on the inverse of the nonlinear function block and on the discrete-time sliding-mode controller. The parameters adaptation are performed using a new recursive parametric estimation algorithm. This algorithm is developed using the adjustable model method and the least squares technique. A recursive least squares (RLS) algorithm is used to estimate the inverse nonlinear function. A time-varying gain is proposed, in the discrete-time sliding mode controller, to reduce the chattering problem. The stability of the closed-loop nonlinear system, with the proposed adaptive control scheme, has been proved. An application to a pH neutralisation process has been carried out and the simulation results clearly show the effectiveness of the proposed adaptive control scheme.     

基于模糊辨识方法的机械臂振动控制研究

为了抑制机器人等复杂结构的振动,提高复杂结构的振动控制精度,提出一种用于辨识机械臂连接结构的非线性模型在线辨识的模糊算法,并以此为基础研究了机械臂振动控制方法.该算法将非线性输入输出系统用时变线性系统模型来拟和,并把此模型表示成模糊模型的形式,用在线调节模糊模型的方法来辨识时变线性模型的相关参数.将递推模糊聚类方法与卡尔曼滤波法用于在线调整模糊模型参数,将此算法应用在两自由度柔性杆件的扭转振动的控制上,并设计相应的硬件控制系统,实验结果表明了此算法的有效性.将该算法应用于工程实践中,实际使用效果表明,此算法具有重要的工程应用价值.     

Automated postoperative blood pressure control

It is very important to maintain the level of mean arterial pressure （MAP）. The MAP control is applied in many clinical situations, including limiting bleeding during cardiac surgery and promoting healing for patient＇ s post-surgery. This paper presents a fuzzy controller-based multiple-model adaptive control system for postoperative blood pressure management. Multiple-model adaptive control （MMAC） algorithm is used to identify the patient model, and it is a feasible system identification method even in the presence of large noise. Fuzzy control （FC） method is used to design controller bank. Each fuzzy controller in the controller bank is in fact a nonlinear proportional-integral （PI） controller,whose proportional gain and integral gain are adjusted continuously according to error and rate of change of error of the plant output, resulting in better dynamic and stable control performance than the regular PI controller, especially when a nonlinear process is involved. For demonstration, a nonlinear, pulsatile-flow patient model is used for simulation, and the results show that the adaptive control system can effectively handle the changes in patient＇s dynamics and provide satisfactory performance in regulation of blood pressure of hypertension patients.