Model Identification And Optimal Control Of Large Inertia And Nonlinear Thermal Process

In the field of thermal control,the controlled objects often show the characteristics of large inertia and non-linearity,which increases the difficulty of model identification and optimal control of thermal objects.At present,the model identification of thermal process is mainly based on dynamic characteristic test,but the test is often limited and difficult to operate.The neural network identification of thermal process based on field data can well avoid these problems.At present,the PID control widely used in thermal process of thermal power unit cannot fulfill the requirement of large inertia and non-linear object.It is necessary to control large inertia and non-linear process with some advanced control strategies.To solve the above problems,this paper studies the identification of thermal process model based on field data and neural network,and combines it with Smith predictive control,and presents a set of solutions to large inertia,non-linear thermal process control problems.The main research contents and achievements are as follows:The identification performance of BP neural network is optimized from two aspects.Firstly,using the sum of change rate error squares and traditional error squares as the performance indicators of the neural network,the contradiction between recognition accuracy and generalization ability in dynamic process neural network model identification with traditional performance indicators is resolved.Secondly,on the basis of the new performance indicators,the sum of the square of the weights of the input nodes and the hidden layer nodes of the neural network is added,and the ant colony algorithm is used to prune the neural network to further improve the generalization ability of the neural network.The simulation results show that the structure of the pruned neural network model is simpler,the generalization ability is better,and the process order is accurately reflected by using the new performance index.An improved first-order model building method for self-balanced high-order objects is presented,which refers to the step response curve modeling method for self-balanced high-order objects,and a new first-order model inertia time value based on this method is presented.The introduction of this value avoids the problem that the traditional inertial time value is too large.This value is more convenient to use,and an appropriate value of inertial time can be obtained without scaling the traditional value of inertial time.The simulation results show that the first-order model based on the improved inertial time value is more accurate than that based on the traditional inertial time value and its scale value.A new performance evaluation index,square of traditional error and square of output slope efficiency of PID controller,is presented,which can effectively avoid the problem that Smith predictive control fluctuates continuously after the PID parameters are optimized by genetic algorithm.The simulation results show that the PID parameter based on the improved performance index not only performs well in Smith predictive control,but also obtains good results in single-loop and cascade control.A Smith control scheme for large inertia and non-linear thermal processes based on an adaptive predictor is presented.This scheme can take advantage of the good performance of Smith predictive control in large inertial process control and avoid the control problems caused by the change of non-linear process characteristics.Compared with cascade control system,this scheme uses single loop control,which simplifies the structure of the control system and is conducive to application.The simulation results show that the scheme effectively reduces the inertia of the control process,greatly reduces the influence of non-linearity on the control performance,and has a higher control quality than cascade control.An application scheme for identification and control of large inertia and non-linear thermal processes is presented.This scheme,based on Smith predictive control,takes the superheated steam temperature process of a supercritical unit as an example,obtains the characteristic parameters of the process of superheated steam temperature corresponding to the load and coal quality point according to the network model,which can be used to adjust the parameters of Smith predictor in real time.The adaptive operation of Smith predictor can solve the non-linear control problem caused by the change of working condition.It is demonstrated by simulation that this scheme is effective.