Nonlinear Decoupling PID Control Using Neural Networks and Multiple Models

For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential （PID） controller is proposed, which consists of a traditional PID controller, a decoupling compensator and a feedforward compensator for the unmodeled dynamics. The parameters of such controller is selected based on the generalized minimum variance control law. The unmodeled dynamics is estimated and compensated by neural networks, a switching mechanism is introduced to improve tracking performance, then a nonlinear decoupling PID control algorithm is proposed. All signals in such switching system are globally bounded and the tracking error is convergent. Simulations show effectiveness of the algorithm.

Application of PID Controller Based on BP Neural Network in Export Steam’s Temperature Control System

By combining the Back-Propagation （BP） neural network with conventional proportional Integral Derivative （PID） controller, a new temperature control strategy of the export steam in supercritical electric power plant is put forward. This scheme can effectively overcome the large time delay, inertia of the export steam and the influencee of object in varying operational parameters. Thus excellent control quality is obtaitud. The present paper describes the development and application of neural network based controller to control the temperature of the boiler＇s export steam. Through simulation in various situations, it validates that the control quality of this control system is apparently superior to the conventional PID control system.

Non-Minimum Phase Nonlinear System Predictive Control Based on Local Recurrent Neural Networks

After a recursive multi-step-ahead predictor for nonlinear systems based on local recurrent neural networks is introduced, an intelligent FID controller is adopted to correct the errors including identified model errors and accumulated errors produced in the recursive process. Characterized by predictive control, this method can achieve a good control accuracy and has good robustness. A simulation study shows that this control algorithm is very effective.

An Adaptive Sliding Mode Tracking Controller Using BP Neural Networks for a Class of Large-scale Nonlinear Systems

A new type controller, BP neural-networks-based sliding mode controller is developed for a class of large-scale nonlinear systems with unknown bounds of high-order interconnections in this paper. It is shown that decentralized BP neural networks are used to adaptively learn the uncertainty bounds of interconnected subsystems in the Lyapunov sense, and the outputs of the decentralized BP neural networks are then used as the parameters of the sliding mode controller to compensate for the effects of subsystems uncertainties. Using this scheme, not only strong robustness with respect to uncertainty dynamics and nonlinearities can be obtained, but also the output tracking error between the actual output of each subsystem and the corresponding desired reference output can asymptotically converge to zero. A simulation example is presented to support the validity of the proposed BP neural-networks-based sliding mode controller.

Combining the genetic algorithms with artificial neural networks for optimization of board allocating

This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm 1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly in-creasing the probability of finding a global optimum.

BP-PID Control Applied in Evaporator of Organic Rankine Cycle System

According to the problem that the selection of traditional PID control parameters is too complicated in evaporator of Organic Rankine Cycle system(ORC),an evaporator PID controller based on BP neural netw ork optimization is designed. Based on the control theory,the model of ORC evaporator is set up. The BP algorithm is used to control the Kp,Kiand Kdparameters of the evaporator PID controller,so that the evaporator temperature can reach the optimal state quickly and steadily. The M ATLAB softw are is used to simulate the traditional PID controller and the BP neural netw ork PID controller. The experimental results show that the Kp,Kiand Kdparameters of the BP neural netw ork PID controller are 0. 5677,0. 2970,and 0. 1353,respectively.Therefore,the evaporator PID controller based on BP neural netw ork optimization not only satisfies the requirements of the system performance,but also has better control parameters than the traditional PID controller.

NONLINEAR MODELING AND CONTROLLING OF ARTIFICIAL MUSCLE SYSTEM USING NEURAL NETWORKS

The pneumatic artificial muscles are widely used in the fields of medicalrobots, etc. Neural networks are applied to modeling and controlling of artificial muscle system. Asingle-joint artificial muscle test system is designed. The recursive prediction error (RPE)algorithm which yields faster convergence than back propagation (BP) algorithm is applied to trainthe neural networks. The realization of RPE algorithm is given. The difference of modeling ofartificial muscles using neural networks with different input nodes and different hidden layer nodesis discussed. On this basis the nonlinear control scheme using neural networks for artificialmuscle system has been introduced. The experimental results show that the nonlinear control schemeyields faster response and higher control accuracy than the traditional linear control scheme.

Model algorithm control using neural networks for input delayed nonlinear control system

The performance of the model algorithm control method is partially based on the accuracy of the system＇s model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. Neural networks have the ability to ＂learn＂the characteristics of a system through nonlinear mapping to represent nonlinear functions as well as their inverse functions. This paper presents a model algorithm control method using neural networks for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one produces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to illustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems.

Hydrodynamic Performance Analysis of a Submersible Surface Ship and Resistance Forecasting Based on BP Neural Networks

This paper investigated the resistance performance of a submersible surface ship(SSS)in different working cases and scales to analyze the hydrodynamic performance characteristics of an SSS at different speeds and diving depths for engineering applications.First,a hydrostatic resistance performance test of the SSS was carried out in a towing tank.Second,the scale effect of the hydrodynamic pressure coefficient and wave-making resistance was analyzed.The differences between the three-dimensional real-scale ship resistance prediction and numerical methods were explained.Finally,the advantages of genetic algorithm(GA)and neural network were combined to predict the resistance of SSS.Back propagation neural network(BPNN)and GA-BPNN were utilized to predict the SSS resistance.We also studied neural network parameter optimization,including connection weights and thresholds,using K-fold cross-validation.The results showed that when a SSS sails at low and medium speeds,the influence of various underwater cases on resistance is not obvious,while at high speeds,the resistance of water surface cases increases sharply with an increase in speed.After improving the weights and thresholds through K-fold cross-validation and GA,the prediction results of BPNN have high consistency with the actual values.The research results can provide a theoretical reference for the optimal design of the resistance of SSS in practical applications.

Application of Neural network PID Controller in Constant Temper-ature and Constant Liquid-level System

Guided by the principle of neural network, an intelligent PID controller based on neural network is devised and applied to control of constant temperature and constant liquidlevel system. The experiment results show that this controller has high accuracy and strong robustness and good characters.

Congestion control method: improved ERICA algorithm based on available bandwidth prediction using neural networks

The improved ERICA algorithm for ABR flow congestion control is presented based on available bandwidth prediction using the BP neural networks in ATM networks. The switch periodically calculates the available bandwidth, monitors the load of network and updates the ER value according to the PERICA algorithm. The proposed algorithm can keep better performance than ERICA algorithm in simulations.

Particle Swarm Optimization Based Fuzzy-Neural Like PID Controller for TCP/AQM Router

In this paper a PID Fuzzy-Neural controller (FNC) is designed as an Active Queue Management (AQM) in internet routers to improve the performance of Fuzzy Proportional Integral (FPI) controller for congestion avoidance in computer networks. A combination of fuzzy logic and neural network can generate a fuzzy neural controller which in association with a neural network emulator can improve the output response of the controlled system. This combination uses the neural network training ability to adjust the membership functions of a PID like fuzzy neural controller. The goal of the controller is to force the controlled system to follow a reference model with required transient specifications of minimum overshoot, minimum rise time and minimum steady state error. The fuzzy membership functions were tuned using the propagated error between the plant outputs and the desired ones. To propagate the error from the plant outputs to the controller, a neural network is used as a channel to the error. This neural network uses the back propagation algorithm as a learning technique. Firstly the parameters of PID of Fuzzy-Neural controller are selected by trial and error method, but to get the best controller parameters the Particle Swarm Optimization (PSO) is used as an optimization method for tuning the PID parameters. From the obtained results, it is noted that the PID Fuzzy-Neural controller provides good tracking performance under different circumstances for congestion avoidance in computer networks.

Converting GPS height by a new method based on neural networks

The adjusted GPS height is the height above the surface of the WGS-84ellipsoid. It is necessary to convert a GPS height into a normal height in engineering. The conicoidfitting method (CFM) and the neural networks method (NNM) are used for this purpose, but each ofthem has its advantages and disadvantages. After studying these two methods, a new method (abbr.CF&NNM) is conceived. The procedure of the CF&NNM is introduced. A practical engineering example isused to study these three different methods. The results by the three methods are listed. The CF&NNMmethod can produce better results than either the CFM or the NNM in deriving normal height from GPSheight. The theory of the CF&NNM method is analyzed.

Implementation on FPGA of Neuro-Genetic PID Controllers Auto-Tuning

The present paper studies the use of genetic algorithm to optimize the tuning of the Proportional, Integral and Derivative (PID) controller. Two control criteria were considered, the integral of the time multiplied by the absolute error (ITAE), and the integral of the time multiplied by the absolute output (ITAY). The time variant plant tested is a first-order plant with time delay. We aim at a real time implementation inside a digital board, so, the previous continuous approach was discretized and tested;the corresponding control algorithm is presented in this paper. The genetic algorithms and the PID controller are executed using the soft processor NIOS II in the Field Programmable Gate Array (FPGA). The computational results show the robustness and versatility of this technology.

Proton exchange membrane fuel cells modeling based on artificial neural networks

To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.

Hybrid intelligent PID control design for PEMFC anode system

Control design is important for proton exchange membrane fuel cell (PEMFC) generator. This work researched the anode system of a 60-kW PEMFC generator. Both anode pressure and humidity must be maintained at ideal levels during steady operation. In view of characteristics and requirements of the system, a hybrid intelligent PID controller is designed specifically based on dynamic simulation. A single neuron PI controller is used for anode humidity by adjusting the water injection to the hydrogen cell. Another incremental PID controller, based on the diagonal recurrent neural network (DRNN) dynamic identification, is used to control anode pressure to be more stable and exact by adjusting the hydrogen flow rate. This control strategy can avoid the coupling problem of the PEMFC and achieve a more adaptive ability. Simulation results showed that the control strategy can maintain both anode humidity and pressure at ideal levels regardless of variable load, nonlinear dynamic and coupling characteristics of the system. This work will give some guides for further control design and applications of the total PEMFC generator.

A Kind of Second-Order Learning Algorithm Based on Generalized Cost Criteria in Multi-Layer Feed-Forward Neural Networks

A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluctant, which led to the loss of valuable information and affected performance of the algorithm to certain extent. For multi layer feed forward neural networks, the second order back propagation recursive algorithm based generalized cost criteria was proposed. It is proved that it is equivalent to Newton recursive algorithm and has a second order convergent rate. The performance and application prospect are analyzed. Lots of simulation experiments indicate that the calculation of the new algorithm is almost equivalent to the recursive least square multiple algorithm. The algorithm and selection of networks parameters are significant and the performance is more excellent than BP algorithm and the second order learning algorithm that was given by Karayiannis.

A Four-color Matching Method Combining Neural Networks with Genetic Algorithm

A brief review of color matching technology and its application of printing RGB images by CMY or CMYK ink jet printers is presented, followed by an explanation to the conventional approaches that are commonly used in color matching. Then, a four color matching method combining neural network with genetic algorithm is proposed. The initial weights and thresholds of the BP neural network for RGB to CMY color conversion are optimized by the new genetic algorithm based on evolutionarily stable strategy. The fourth component K is generated by using GCR (Gray Component Replacement) concept. Simulation experiments show that it is well behaved in both accuracy and generalization performance.

A Modified Algorithm for Feedforward Neural Networks

As a most popular learning algorithm for the feedforward neural networks, the classic BP algorithm has its many shortages. To overcome some of the shortages, a modified learning algorithm is proposed in the article. And the simulation result illustrate the modified algorithm is more effective and practicable.

Application of quantum neural networks in localization of acoustic emission

Due to defects of time-difference of arrival localization,which influences by speed differences of various model waveforms and waveform distortion in transmitting process,a neural network technique is introduced to calculate localization of the acoustic emission source.However,in back propagation（BP） neural network,the BP algorithm is a stochastic gradient algorithm virtually,the network may get into local minimum and the result of network training is dissatisfactory.It is a kind of genetic algorithms with the form of quantum chromosomes,the random observation which simulates the quantum collapse can bring diverse individuals,and the evolutionary operators characterized by a quantum mechanism are introduced to speed up convergence and avoid prematurity.Simulation results show that the modeling of neural network based on quantum genetic algorithm has fast convergent and higher localization accuracy,so it has a good application prospect and is worth researching further more.