Laser Constant Current Source Controller Based on CDKF-RBFPID
Stable operation of lasers requires a constant current source control system with high precision and stable output cur-rent.An algorithm based on the combination of central differential Kalman filtering(CDKF)and improved radial basis function(RBF)neural network adaptive PID control,named CDKF-RBFPID,is proposed to address the problem that the output accu-racy of laser constant current source system is poor in noisy environment and the parameters are difficult to be adjusted by PID al-gorithm.By using CDKF,we update the state and covariance of the constant current source system,so as to filter out the state noise and measurement noise in the system.To accomplish adaptive parameter tuning,the RBF-PID parameters are modified us-ing the reinforcement learning Actor-Critic architecture.Comparing the output current of the constant current source system and the output power of a laser,the experimental results demonstrate that the CDKF-RBFPID method can effectively lessen the im-pact of noise on the system,further enhance the accuracy of the constant current source output current and the stability of the la-ser output power,with the response time improved by 58.3%,the steady-state error reduced by 71.4%,and the output current control accuracy reaching 1%.
constant current sourcePID controlCDKFRBF neural networkActor-Critic
万方数据
维普
