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.

An intelligent control method based on artificial neural network for numerical flight simulation of the basic finner projectile with pitching maneuver

In this paper,an intelligent control method applying on numerical virtual flight is proposed.The proposed algorithm is verified and evaluated by combining with the case of the basic finner projectile model and shows a good application prospect.Firstly,a numerical virtual flight simulation model based on overlapping dynamic mesh technology is constructed.In order to verify the accuracy of the dynamic grid technology and the calculation of unsteady flow,a numerical simulation of the basic finner projectile without control is carried out.The simulation results are in good agreement with the experiment data which shows that the algorithm used in this paper can also be used in the design and evaluation of the intelligent controller in the numerical virtual flight simulation.Secondly,combined with the real-time control requirements of aerodynamic,attitude and displacement parameters of the projectile during the flight process,the numerical simulations of the basic finner projectile’s pitch channel are carried out under the traditional PID(Proportional-Integral-Derivative)control strategy and the intelligent PID control strategy respectively.The intelligent PID controller based on BP(Back Propagation)neural network can realize online learning and self-optimization of control parameters according to the acquired real-time flight parameters.Compared with the traditional PID controller,the concerned control variable overshoot,rise time,transition time and steady state error and other performance indicators have been greatly improved,and the higher the learning efficiency or the inertia coefficient,the faster the system,the larger the overshoot,and the smaller the stability error.The intelligent control method applying on numerical virtual flight is capable of solving the complicated unsteady motion and flow with the intelligent PID control strategy and has a strong promotion to engineering application.

STUDY ON INJECTION AND IGNITION CONTROL OF GASOLINE ENGINE BASED ON BP NEURAL NETWORK

According to advantages of neural network and characteristics of operatingprocedures of engine, a new strategy is represented on the control of fuel injection and ignitiontiming of gasoline engine based on improved BP network algorithm. The optimum ignition advance angleand fuel injection pulse band of engine under different speed and load are tested for the samplestraining network, focusing on the study of the design method and procedure of BP neural network inengine injection and ignition control. The results show that artificial neural network technique canmeet the requirement of engine injection and ignition control. The method is feasible for improvingpower performance, economy and emission performances of gasoline engine.

Parameters Optimization of the Heating Furnace Control Systems Based on BP Neural Network Improved by Genetic Algorithm

The heating technological requirement of the conventional PID control is difficult to guarantee which based on the precise mathematical model,because the heating furnace for heating treatment with the big inertia,the pure time delay and nonlinear time-varying.Proposed one kind optimized variable method of PID controller based on the genetic algorithm with improved BP network that better realized the completely automatic intelligent control of the entire thermal process than the classics critical purporting(Z-N)method.A heating furnace for the object was simulated with MATLAB,simulation results show that the control system has the quicker response characteristic,the better dynamic characteristic and the quite stronger robustness,which has some promotional value for the control of industrial furnace.

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.

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.

Parameter Self - Learning of Generalized Predictive Control Using BP Neural Network

This paper describes the self—adjustment of some tuning-knobs of the generalized predictive controller(GPC).A three feedforward neural network was utilized to on line learn two key tuning-knobs of GPC,and BP algorithm was used for the training of the linking-weights of the neural network.Hence it gets rid of the difficulty of choosing these tuning-knobs manually and provides easier condition for the wide applications of GPC on industrial plants.Simulation results illustrated the effectiveness of the method.

Research on the controller of an arc welding process based on a PID neural network

A controller based on a PID neural network （PIDNN） is proposed for an arc welding power source whose output characteristic in responding to a given value is quickly and intelligently controlled in the welding process. The new method syncretizes the PID control strategy and neural network to control the welding process intelligently, so it has the merit of PID control rules and the trait of better information disposal ability of the neural network. The results of simulation show that the controller has the properties of quick response, low overshoot, quick convergence and good stable accuracy, which meet the requirements for control of the welding process.

Control of Hydraulic Power System by Mixed Neural Network PID in Unmanned Walking Platform

To speedily regulate and precisely control a hydraulic power system in a unmanned walking platform(UWP),based on the brief analysis of digital PID and its shortcomings,dual control parameters in a hydraulic power system are given for the precision requirement,and a control strategy for dual relative control parameters in the dual loop PID is put forward,a load and throttle rotation-speed response model for variable pump and gasoline engine is provided according to a physical process,a simplified neural network structure PID is introduced,and formed mixed neural network PID(MNN PID)to control rotation speed of engine and pressure of variable pump,calculation using the back propagation(BP)algorithm and a self-adapted learning step is made,including a mathematic principle and a calculation flow scheme,the BP algorithm of neural network PID is trained and the control effect of system is simulated in Matlab environment,real control effects of engine rotation speed and variable pump pressure are verified in the experimental bench.Results show that algorithm effect of MNN PID is stable and MNN PID can meet the adjusting requirement of control parameters.

The study of film tension control system based on RBF neural network and PID

In the BOPP （Biaxially Oriented Polypropylene） production line, the tension size and smooth film received change volume has a decisive effect on the rolling quality, casting machine is a complicated electromechanical control system, tension control of casting machine are the main factors that influence the production quality. Analyzed the reason and the tension control mathematical model generation casting machine tension in the BOPP production line, for the constant tension control of casting machine, put forward a kind of improved PID control method based on RBF neural network. By the method of Jacobian information identification of RBF neural network, combined with the incremental PID algorithm to realize the self-tuning tension control parameters, control simulation and implementation of the model using Matlab software programming. The simulation results show that, the improved algorithm has better control effect than the general PID.

基于PSO-BP模糊PID的变距取苗机构控制系统设计

为满足番茄、辣椒等蔬菜作物的移栽需求,基于向下取苗原理设计了一种适用72穴和128穴两种主要番茄钵苗穴盘规格的变距取苗机构,通过建立数学模型获得了取苗机械手参数的目标函数,并利用粒子群和模拟退火混合算法对其结构参数进行优化。同时,为实现变距取苗机构的精确控制,提出了一种基于PSO-BP的模糊PID算法以提高控制精度,介绍了系统的结构与工作原理,并通过选型计算与分析建模建立了控制系统的数学模型。针对传统PID控制器稳定性差、响应速度慢等不足之处,利用PSO-BP模糊PID对控制器的参数进行在线调整,以满足控制过程中对参数的不同需求。仿真结果与试验数据的分析表明:在参数相同条件下,基于PSO-BP模糊PID控制系统系统稳定性更好、响应速度更快,具有良好的鲁棒性,提升取苗成功率的同时降低了基质损伤率,能够满足变距取苗机构高精度快速稳定控制的需求。

Trajectory tracking guidance of interceptor via prescribed performance integral sliding mode with neural network disturbance observer

This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots.

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.

Design of Robotic Visual Servo Control Based on Neural Network and Genetic Algorithm

A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation （BP） neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP Mgorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.

Research on Power Control of Wind Power Generation Based on Neural Network Adaptive Control

For the characteristics of wind power generation system is multivariable, nonlinear and random, in this paper the neural network PID adaptive control is adopted. The size of pitch angle is adjusted in time to improve the perfomance of power control. The PID parameters are corrected by the gradient descent method, and Radial Basis Functiion （RBF） neural network is used as the system identifier in this method. Sinlation results show that by using neural network adaptive PID controller the generator power control can inhibit effectively the speed and affect the output prover of generator. The dynamic performnce and robustness of the controlled system is good, and the peformance of wind power system is improved.

A Novel Adaptive Neural Network Compensator as Applied to Position Control of a Pneumatic System

Considerable research has been conducted on the control of pneumatic systems. However, nonlinearities continue to limit their performance. To compensate, advanced nonlinear and adaptive control strategies can be used. But the more successful advanced strategies typically need a mathematical model of the system to be controlled. The advantage of neural networks is that they do not require a model. This paper reports on a study whose objective is to explore the potential of a novel adaptive on-line neural network compensator (ANNC) for the position control of a pneumatic gantry robot. It was found that by combining ANNC with a traditional PID controller, tracking performance could be improved on the order of 45% to 70%. This level of performance was achieved after careful tuning of both the ANNC and PID components. The paper sets out to document the ANNC algorithm, the adopted tuning procedure, and presents experimental results that illustrate the adaptive nature of NN and confirms the performance achievable with ANNC. A major contribution is demonstration that tuning of ANNC requires no more effort than the tuning of PID.

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.

Adaptive proportional integral differential control based on radial basis function neural network identification of a two-degree-of-freedom closed-chain robot

A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical-use. This paper presents an adaptive proportional integral differential （PID） control algorithm based on radial basis function （RBF） neural network for trajectory tracking of a two-degree-of-freedom （2-DOF） closed-chain robot. In this scheme, an RBF neural network is used to approximate the unknown nonlinear dynamics of the robot, at the same time, the PID parameters can be adjusted online and the high precision can be obtained. Simulation results show that the control algorithm accurately tracks a 2-DOF closed-chain robot trajectories. The results also indicate that the system robustness and tracking performance are superior to the classic PID method.

Novel flow control mechanism based on improved BP neural network in cognitive packet network

In this paper, a novel flow control mechanism in cognitive packet network (CPN) based on the improved back propagation (BP) neural network is proposed, considering the flow distribution status predicted by BP neural network when packets are routed. The objective is to increase the capacity of CPN and improve the quality of service (QoS) by achieving flow balance. Besides, considering the slow convergence speed of traditional BP algorithm and the quick change of the flow status in cognitive packet network, an improved BP algorithm with dynamic learning rate is designed in order to achieve a higher convergence speed. The mechanism, which we propose, regards the predicated traffic data as an important factor when packets are routed to implement flow control. By achieving balance, the quality of network can be improved obviously. The simulation results show that the proposed mechanism provides better average time delay and packets loss ratio.

Application of Artificial Neural Network in Robotic Hybrid Position/Force Control

A hybrid position/force controller is designed for the joint 2 and the joint 3 of thePUMA 560 robot.The hybrid controller includes a multilayered neural network,which canidentify the dynamics of the contacted environment and can optimize the parameters of PIDcontroller.The experimental results show that after having been trained,the robot has sta-ble response to the training patterns and strong adaptive ability to the situation between thepatterns.