基于GA 的SVM R 预测控制研究

研究高精度、有效、简单的信息预测模型是目前非线性预测控制需要解决的重要问题．SVMR建模方法简单、理论基础完备,所反映的是系统的非线性特征,在建立非线性模型中与神经网络等非线性回归方法相比具有许多独特的优点．为此,提出一种SVMR预测控制结构,利用SVMR建立非线性系统模型,利用GA进行滚动优化．实验证明,这种预测控制具有良好的非线性控制效果．     

Multivariable predictive control of a pressurized tank using neural networks

The behavior of a multivariable predictive control scheme based on neural networks applied to a model of a nonlinear multivariable real process, consisting of a pressurized tank is investigated in this paper. The neural scheme consists of three neural networks; the first is meant for the identification of plant parameters (identifier), the second one is for the prediction of future control errors (predictor) and the third one, based on the two previous, compute the control input to be applied to the plant (controller). The weights of the neural networks are updated on-line, using standard and dynamic backpropagation. The model of the nonlinear process is driven to an operation point and it is then controlled with the proposed neural control scheme, analyzing the maximum range over the neural control works properly.     

Comparative performance study of QN and LM algorithms in predictive control for NNARX-identified model of hydro-power plant

In this paper input-constrained predictive control strategy for NNARX (neural network non-linear auto-regression with exogenous signal) model of hydro-turbine is presented. The input (gate position) and output (turbine power) data are generated by means of dynamic plant model. The collected data are utilized to develop the NNARX model of the plant. Then NN-based predictive control (NNPC) scheme is applied to control the turbine power. The control cost function (CCF) includes the squared difference between the model predicted output and desired response and a weighted squared change in the control signal. The CCF is minimized with both Quasi-Newton and Levenberg–Marquardt iterative algorithms. To demonstrate the suitability of the strategy, the plant has been simulated on two different reference signals. An erratum to this article can be found at     

基于ANN模型的非线性自校正预测控制器

采用局部建模和线性化方法,提出了一种基于人工神经网络(Artificial Neural Network --ANN)模型的非线性自校正预测控制算法.仿真实例表明,所提控制策略可有效地 控制某些未知多变量非线性动态系统.     

Adaptive neural model-based decentralized predictive control

This paper considers the problem of developing an adaptive neural model-based decentralized predictive controller for general multivariable non-linear processes, where the equations governing the system are unknown. It derives a method for implementing a neural network model for unknown non-linear process dynamics for adaptive control. The performance of this controller is demonstrated and evaluated using a simulated chemical process: multivariable non-linear control of distillation column. The simulation results indicate that the proposed control strategies have good practical potential for adaptive control of multivariable non-linear processes.     

Neural network model-based automotive engine air/fuel ratio control and robustness evaluation

Automotive engines are multivariable system with severe non-linear dynamics, and their modelling and control are challenging tasks for control engineers. Current control of engine used look-up table combined with proportional and integral (PI) control and is not robust to system uncertainty and time varying effects. In this paper the model predictive control strategy is applied to engine air/fuel ratio control using neural network model. The neural network model uses information from multivariables and considers engine dynamics to do multi-step ahead prediction. The model is adapted in on-line mode to cope with system uncertainty and time varying effects. Thus, the control performance is more accurate and robust compared with non-adaptive model based methods. To speed up algorithm calculation, different optimisation algorithms are investigated and performance compared. Finally, the developed method is evaluated on a well-known engine benchmark, a simulated mean value engine model (MVEM). The simulation results demonstrate the effectiveness of the developed method.     

基于LS-SVM预报的动态矩阵预测控制

针对实际工业过程中的非线性及时变特性,传统预测控制算法就难于建立精确的数学模型,从而提出了一种基于最小二乘支持向量机预报的动态矩阵预测控制模型.在整个过程中,首先建立基于最小二乘支持向量机的非线性动态矩阵预测控制结构,通过利用最小二乘支持向量机辨识被控对象模型,同时预测对象的未来输出,然后用动态矩阵控制算法进行滚动优化和反馈校正.仿真实例表明该模型对预测结果有很好的控制作用,有效消除输入干扰的影响,从而提高了预测精度.     

Tracking performance control of a cable communicated underwater vehicle using adaptive neural network controllers

In this paper, design, dynamic modeling and control of the fabricated underwater remotely operated vehicle have been considered. Dynamic model of the vehicle is presented for four degrees of freedom and an accurate representation of the dynamic effects of the towed cable is used for dynamic simulation and control design. A nonlinear adaptive neural network controller is developed and simulated. Multi-layer and radial basis function neural networks are used for designing the adaptive controllers. Finally, the performance of the vehicle with neural network controllers is compared with a PD controller. The significant improvement is observed for tracking performance of the vehicle in all controllable degrees of freedom. Also, the simulation illustrated the robustness of controllers for the relative large distributions of the communication cable.     

Neural Network Structures for Optimal Control of LPCVD Reactors

In this paper, a new approach of LPCVD reactor modelling and control is presented, based on the use of neural networks. We first present the development of a hybrid networks model of the reactor. The objective is to provide a simulation model which can be used to compute online the film thickness on each wafer. In the second section, the thermal control of a LPCVD reactor is studied. The objective is to develop a multivariable controller to control a space- and time-varying temperature profile inside the reactor. A neural network is designed using a methodology based on process inverse dynamics modelling. Good control results have been obtained when tracking space-time temperature profiles inside the LPCVD reactor pilot plant. Finally, global software is elaborated to achieve film thickness control in an experimental LPCVD reactor pilot plant, in order to get a defined and uniform deposition thickness on the wafers all along the reactor. Experimental results are presented which confirm the efficiency of the optimal control strategy.     

A Wavelet Neural Network Based Non-linear Model Predictive Controller for a Multi-variable Coupled Tank System

In this paper, a novel real time non-linear model predictive controller(NMPC) for a multi-variable coupled tank system(CTS) is designed. CTSs are highly non-linear and can be found in many industrial process applications. The involvement of multi-input multi-output(MIMO) system makes the design of an effective controller a challenging task. MIMO systems have inherent couplings,interactions in-between the process input-output variables and generally have an complex internal structure. The aim of this paper is to design, simulate, and implement a novel real time constrained NMPC for a multi-variable CTS with the aid of intelligent system techniques. There are two major formidable challenges hindering the success of the implementation of a NMPC strategy in the MIMO case. The first is the difficulty of obtaining a good non-linear model by training a non-convex complex network to avoid being trapped in a local minimum solution. The second is the online real time optimisation(RTO) of the manipulated variable at every sampling time.A novel wavelet neural network(WNN) with high predicting precision and time-frequency localisation characteristic was selected for an MIMO model and a fast stochastic wavelet gradient algorithm was used for initial training of the network. Furthermore, a genetic algorithm was used to obtain the optimised parameters of the WNN as well as the RTO during the NMPC strategy. The proposed strategy performed well in both simulation and real time on an MIMO CTS. The results indicated that WNN provided better trajectory regulation with less mean-squared-error and average control energy compared to an artificial neural network. It is also shown that the WNN is more robust during abnormal operating conditions.     

Model predictive control of continuous drum granulation

This paper details a methodology for the design of a model predictive controller for a continuous granulation plant. The work is based on a non-linear one-dimensional population balance model (1D-PBM), which was parameterized using experimental step test data generated at a continuous granulation pilot plant installed at the University of Queensland, Australia. The main objective was to operate the granulator under optimal conditions while off-specification material was fed back into the granulator to increase the economy of the process. The final algorithm design combines elements of model predictive control (MPC) with gain scheduling to cancel non-linearities in the recycle flow. A model directly identified from the step test data was the basis for testing a model predictive controller. Simulations show that the efficiency and robustness of this granulation process can be improved by applying the proposed control strategy. Ongoing work focuses on the implementation of the proposed control strategy on a full scale industrial plant.     

Comparative analysis of artificial neural networks and dynamic models as virtual sensors

This paper presents a comparison of predictive models for the estimation of engine power and tailpipe emissions for a 4 kW gasoline scooter. This study forms a benchmark toward establishing an online emissions control and monitoring system to bring the emissions to within specific limits. Three emissions predictive models were investigated in this study; direct and series artificial neural network (ANN) models and a MATLAB dynamic model. The direct models takes variables lambda, throttle position, engine and vehicle speed to predict the engine power and the emissions CO, CO₂ and HC. The series model first takes the mentioned input to predict the engine power and consequently using the engine power as the fifth input to predict the emissions. For the ANN models, two multilayered networks were compared and analyzed; the backpropagation (BP) and optimization layer-by-layer (OLL) algorithms. The predictive accuracy for each algorithm were compared and it was found that the OLL network is the most accurate with a maximum mean relative error (MRE) of 1.78% and 1.38% for the direct and series predictive model respectively. Comparative results showed that the series neural network model gives the most accurate predictions, with MRE of 0.63% and 0.47% for the engine power and emissions respectively. The series neural network model can be seen as generic virtual power and emissions sensors, substituting costly and cumbersome hardware. Simple obtainable process parameters together with the series neural network will contribute immensely in control and tuning of emissions for real-time vehicular applications.     

Identification and predictive control of a melter unit used in the sugar industry

This paper presents the real identification and non-linear predictive control of a melter unit; the unit is used in a sugar factory placed in Benavente (Spain). The proposed approach uses a specific recurrent neural network that allows us to identify a non-linear model of the process, providing a mathematical representation in the state space form. Output and state variables can be obtained from the inputs and measured disturbances acting on the system. The neural based predictive control is carried out through the optimization of a cost function that takes into account the output prediction errors from a reference trajectory and the future control efforts, by using the identified model as a prediction model for the system outputs. The solution to this problem provides the optimal set of future control actions, but only the first one is applied to the real process, and the optimization problem is solved again at time t + 1.The results show the good performance of neural predictive control and its suitability for applications in real systems, particularly in the process industry.     

Nonlinear control structures based on embedded neural system models

This paper investigates in detail the possible application of neural networks to the modeling and adaptive control of nonlinear systems. Nonlinear neural-network-based plant modeling is first discussed, based on the approximation capabilities of the multilayer perceptron. A structure is then proposed to utilize feedforward networks within a direct model reference adaptive control strategy. The difficulties involved in training this network, embedded within the closed-loop are discussed and a novel neural-network-based sensitivity modeling approach proposed to allow for the backpropagation of errors through the plant to the neural controller. Finally, a novel nonlinear internal model control (IMC) strategy is suggested, that utilizes a nonlinear neural model of the plant to generate parameter estimates over the nonlinear operating region for an adaptive linear internal model, without the problems associated with recursive parameter identification algorithms. Unlike other neural IMC approaches the linear control law can then be readily designed. A continuous stirred tank reactor was chosen as a realistic nonlinear case study for the techniques discussed in the paper.     

Fault tolerance in the framework of support vector machines based model predictive control

Model based predictive control (MBPC) has been extensively investigated and is widely used in industry. Besides this, interest in non-linear systems has motivated the development of MBPC formulations for non-linear systems. Moreover, the importance of security and reliability in industrial processes is in the origin of the fault tolerant strategies developed in the last two decades. In this paper a MBPC based on support vector machines (SVM) able to cope with faults in the plant itself is presented. The fault tolerant capability is achieved by means of the accurate on-line support vector regression (AOSVR) which is capable of training an SVM in an incremental way. Thanks to AOSVR is possible to train a plant model when a fault is detected and to change the nominal model by the new one, that models the faulty plant. Results obtained under simulation are presented.     

循环神经网络建模在非线性预测控制中的应用

基于动态Ｌｅｖｅｎｂｅｒｇ－Ｍａｒｑｕａｒｄｔ（ＬＭ）算法,提出两步ＬＭ方法建立非线性过程的循环神经网络模型。该模型以足够的精度并行于过程运行,并能从过程的输入信息模拟过程未来的响应。研究了基于该模型的扩展ＤＭＣ预测控制策略,仿真结果表明该控制器的性能得到了很大提高。     

基于预测函数模型的APF补偿电流控制研究

为了有效地改善电网电流中因接入非线性负载所引入的谐波分量和削弱控制系统的延时特点,提出了一种基于预测函数模型的有源电力滤波器(APF)补偿电流控制方法,由当前时刻采样数据和最近历史时刻的数据进行构建预测函数模型,实现了有源滤波器谐波补偿电流的预测控制.仿真结果表明:该控制方法不仅对负载电流有精确的预测能力,且对系统电流中谐波电流具有较好的抑制效果和补偿精度.     

Identification of non-linear processes using reciprocal multiquadric functions

In this paper radial basis function (RBF) networks are used to model general non-linear discrete-time systems. In particular, reciprocal multiquadric functions are used as activation functions for the RBF networks. A stepwise regression algorithm based on orthogonalization and a series of statistical tests is employed for designing and training of the network. The identification method yields non-linear models, which are stable and linear in the model parameters. The advantages of the proposed method compared to other radial basis function methods and backpropagation neural networks are described. Finally, the effectiveness of the identification method is demonstrated by the identification of two non-linear chemical processes, a simulated continuous stirred tank reactor and an experimental pH neutralization process.     

Multivariable Control of a Pulsed Liquid-Liquid Extraction Column by Neural Networks

Modelling and control of chemical process systems are usual applications of artificial neural networks that have been explored so far with success. This paper deals with the potential application of neural networks to the multivariable control of a solvent extraction pilot plant. The pilot plant to be controlled is a pulsed liquid-liquid extraction column, which presents a non-linear behaviour and time-varying dynamics. Previous works have shown that the column could be maintained in its optimal behaviour by means of the control of conductivity by action on the pulse frequency. A given product specification can be obtained by the control of the product concentration in the outlet stream by acting on the solvent feed-flow rate. Owing to interactions between one variable and the other, a two input– two output control scheme has been developed and implemented. Promising experimental results have been obtained by using neural networks as an alternative tool for online control of chemical plant with dynamic changes.     

Neural network control of multivariable processes with a fast optimisation algorithm

A radial basis function (RBF) neural network model based predictive control scheme is developed for multivariable nonlinear systems in this paper. A fast convergence algorithm is proposed and employed in multidimensional optimisation in the control scheme to reduce the computing time and save required computer memory. The scheme is applied to a simulated two-input two-output nonlinear process for set-point tracking control. Simulation results demonstrate the effectiveness of the control strategy and the fast learning algorithm for multivariable non-linear processes. Comparison of the performance with PID control is included.