Tham khảo tài liệu 'pid control implementation and tuning part 10', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Neural Network Based Tuning Algorithm for MPID Control 173 So the function of the neural network is to receive the desired position 9ref and the manipulator tip payload Mt with the classical PD controller gains Kp Kd. The neural network will give out the relation between the vibration control gain Kvc and the criterion function at a certain inputs 9ref Mt Kp Kd. From this relation the value of the value of optimum vibration control gain Kvc is corresponding to the minimum criterion function. A flow chart for the training process of the neural network with the parameters of the manipulator and gains of the controller is shown in Fig. 7. The details of the learning algorithm and how is the weight in changed will be discussed later in the training of the neural network. Fig. 7. Flow chart for the training of the neural network. 174 PID Control Implementation and Tuning Fig. 8. Relation between vibration control gain and criterion function. By trying many criterion function to select one of them as a measurement for the output response from the simulation. We put in mind when selecting the criterion function to include two parameters. The first one is the amplitude of the defection of the end effector and the second one is the corresponding time. A set of criterion function like ft tỗ2dt ft 10tJ2dt ft d2etdt is tried and a comparison between the behave for all of them and the vibration control gain Kvc is done. The value of ts here represent the time for simulation and on this research we take it as 10 seconds. The criterion function J0ts ỗ2etdt is selected as its value is always minimal when the optimum vibration control gain is used. The term optimum vibration control gain Kvc pointed here to the value of Kvc which give a minimum criterion function f0s ỗ2etdt and on the same time keep stability of the system. The neural network is trained on the results from the simulation with different Qref Mt Kp Kd Kvc. The neural network is trying to find how the error in the .
