PID Neural Network Control System Research Articles

Control of unmanned agricultural vehicles using neural network-based control system

Recent advances in the fields of robotics, control systems, and wireless communication technologies have accelerated the studies related with unmanned agricultural vehicles. The unmanned agricultural vehicles have been developed to perform various functions in agricultural fields such as seeding, spraying, hoeing, weed and pest control, and harvest. Given the reduction in the number of people working in agriculture and industry, it is obvious that intelligent unmanned agricultural vehicles will be an indispensable part of agriculture in the future. In this paper, PID controller and model-based neural network PID control system has been applied for controlling an unmanned agricultural vehicle. To test the performance of control structures developed, three different input signals were used. When the results of both control structures were examined in the way of transient-state response (such as rise time, settling time, peak time, peak value, settling time) and steady-state response, the control system produced had favorable results.

Control System Design of Anti-rolling Tank Swing Bench Using BP Neural Network PID Based on LabVIEW

The anti-rolling tank swing bench is a typical hydraulic position servo system. Its function is to simulate the ship roll motion and to verify the performance of the anti-rolling tank. Generally, the PID control method can achieve an ideal control effect. But the uncertainties, such as the couplings and the nonlinearty of the swing bench, make it difficult to establish the precise mathematical model of system. During experiments, the different disturbance torques are generated according to the different dimension scales of the anti-rolling tanks. Changes of the natural characteristics of the system are caused by changes of load, and the fixed parameters of PID controller are difficult to continue to ensure a good control effect. In order to reduce the influences caused by the factors mentioned above, a BP neural network PID control system based on virtual instrument LabVIEW is designed. By simulating the responses of the system in real time control, the verification tests using actual system are avoided. Finally, the experiments of swing bench are carried out. The results show that the controller can ensure the control accuracy and stability. The parameters tuning process efficiency is improved. The operation is convenient. The data acquisition process is simple and practical.

Research on Neural Network Control Technology for Satellite-Based Laser Communication Tracking Turntable

In space laser communication tracking turntable work environment characteristics, we design a neural network PID control system which makes the system’s parameter self-tuning. The control system cans self-tune parameters under the changes of the object’ mathematic model, it solves the problem for the control object’s model changes under the space environment. It also looks for method for optimum control through the function of neural network's self-learning in order to solve the problem of the precision’s decline which arouse from vibration and disturbance. The simulation experiments confirmed the self-learning ability of neural network, and described the neural PID controller dynamic performance is superior to the classical PID controller through the output characteristic curves contrast.

Design on Fuzzy Neural Network PID Control System of Diesel Engine

The diesel engA control system of diesel engine speed regulation based on the structure of fuzzy neural network is proposed in this paper, which is combined with PID controller in order to regulate PID parameters dynamically. The simulation result proves that the system has good control capability and robust stability, as well as good self-study and self-adaptive ability.

Improved Artificial Fish Algorithm for Parameters Optimization of PID Neural Network

In order to solve problems such as initial weights are difficult to be determined, training results are easy to trap in local minima in optimization process of PID neural network parameters by traditional BP algorithm, this paper proposed a new method based on improved artificial fish algorithm for parameters optimization of PID neural network. This improved artificial fish algorithm uses a composite adaptive artificial fish algorithm based on optimal artificial fish and nearest artificial fish to train network weights parameters of PID neural network. By comparing food consistence in preying behavior to adaptively select vision and step of artificial fish, this method overcomes shortcomings such as slow convergence speed, low optimization accuracy of basic artificial fish algorithm. Simulations of PID neural network system whose parameters are trained respectively through BP algorithm and improved artificial fish algorithm are conducted respectively in the MATLAB environment. The simulation result shows that the PID neural network control system whose parameters are trained by the improved artificial fish algorithm has a better control effect, especially for nonlinear systems

Development of PID Neural Network Control System for Temperature of Resistance Furnace

Development of PID Neural Network Control System for Temperature of Resistance Furnace

Application of High-Speed Wind Tunnel Control Based on PID Neural Network

The traditional PID control in nonlinear system such as high-speed wind tunnel has limitations, and the range of using is limited. The BP neural network has been widely applied to the optimization of the PID controller parameter adjustment. The PID neural network control system is introduced in the conventional PID control, which has advantages such as simple structure, physical meaning clear parameters, but also has a neural network of parallel structure and the function of learning and memory and nonlinear mapping capability. The controller uses BP (error back propagation) algorithm to correct connection weights, through on-line training and learning and make objective function to achieve optimal value. This improvement scheme can not only improve algorithm in the training process, and the convergence speed in the wind tunnel, the control valve control system response speed, high precision, meet the steady-state real-time control requirements.

PS-FB-ZVZCS PWM Converter Modeling and Simulation Based on the PID Neural Network

In order to overcome the disadvantage of the traditional control methods and general neural network control methods, the above two control methods which used to be applied to the PS-FB-ZVZCS-PWM(Phase-shifted Full-bridge Zero-voltage Zero-current-switching Pulse-Width Modulation, PS-FB-ZVZCS-PWM)converters modeling has been replaced by the PID(Proportional-Integral-Derivative , PID)neural network control. The first PID neural network subnet was used as the outer voltage loop control and the second PID neural network subnet was used as the inner current loop control. The output of the first PID neural network subnet was used as the reference input of the second PID neural network subnet. By the tight integration of two neural network subnets, a dual loop PID neural network control system was got. The result of the simulation which was got by MATLAB software showed the use of PID neural network as a regulator of the double close loop model was not only to achieve the nice control characteristics which are no overshoot, no static error, fast response, short transition time, good tracking performance, but also man-made regulation time was significantly reduced.