An Algorithm Research Of An Adaptive PID Control

PID control system is the most commonly used control system in industrial process control. In practice, an important issue is model parameters tuning problems. In the more complex cases, the controlled object which is nonlinear, time-varying and hysteresis. Process parameters and even the structure of the model will change under the influence of noise, load disturbance and other factors. So the PID control model, not only requires parameter tuning does not depend on the mathematical model of the object, but also requires the ability to adjust the parameters online to meet the requirements of real-time control. Adaptive PID controller is an effective way to solve such problems. The basic idea is to make the control system has a certain ability to adapt to the controlled object, such as recognizing changes in environmental conditions and automatically controlled process parameter identification, auto-tuning control parameters, thus allowing the system to achieve optimal or suboptimal control.The ability to construct an adaptive PID control system, you need to solve two basic problems:(1) identification, is the controlled object online modeling;(2) PID controller tuning parameters online. The work is to Radica Systems Limited Automobile heating fuel heater LLYCO-30KW-type electronic control unit control algorithm design problem for the engineering background, research work undertaken for the above two problems.First, this work designed a three-tier system based on neural network model identifier. The reader is characterized, using a weight coefficient initialization strategy under the guidance of a priori knowledge, thereby speeding up the search process converges to satisfactory solution speed, more conducive to fast online identification and online modeling control object.System Identification Network topology design process, due to the cell structure is closely related to the problem domain and the hidden layer, the actual work object as the target for study of a series of simulation experiments to optimize the structure of the hidden layer neurons effectively avoid the practice exists due to the fitting or over-fitting problems.Secondly, this work has also designed a neural network PID controller based on the model. The controller includes learning to control two basic modules to be controlled object as the output function of the network. Its characteristics are the control parameters can be online tuning to meet varying system for real-time control requirements.Based on the above work, the work to build an adaptive PID controller model. The model consists of system identification, PID control of two modules. Work process, identification module is responsible for the controlled object online modeling, the controlled object output current moment is a mathematical model as a parameter input to the PID controller learner, learner accordingly adjust the control parameters of the controller Finally, to achieve the control parameters of the line setting and the target for the entire closed-loop control system.One group was the control object Simulink simulation results are shown in LLYCO-30KW fuel heater, the adaptive controller can well realize the third-order nonlinear, time-varying slowly, lagging accurate and effective identification system closed-loop control.