Event-Based Adaptive Neural Control For Permanent Magnet Synchronous Motors

With the advantages of simple structure,small size and high efficiency,permanent magnet synchronous motor(PMSM)has been studied and applied widely in recent years.Besides,with the rapid development and highly application of computer network technology,networked structure has become an important development trend of PMSM control system.However,due to the limited network bandwidth resources,the congested network environment will destroy the control performance of the system,and even lead to system instability.Considering the superiority of the event-triggered control method in saving network bandwidth resources,how to use the event-triggered mechanism to overcome the network problem has become a challenging and hot research topic.This thesis focuses on the adaptive neural control for discrete-time PMSM based on the event-triggered mechanism.Specifically,the event-based adaptive neural control schemes are investigated for the discrete-time PMSM system subject to the limited network bandwidth resources,unknown system dynamics and mismatched disturbances.The main works of this thesis are summarized as follows:Firstly,for PMSM with the precisely known model,the prediction technology is applied to transform the controlled model into a prediction one,which avoids the noncausal problem during the backstepping design procedure.Besides,considering the network bandwidth resources are limited,an absolute event-triggered mechanism with dead-zone operator is introduced into the network channel between the controller and the actuator.The proposed event-triggered control scheme guarantees that the networked PMSM can achieve the good control performance and reduce the consumption of network bandwidth resources at the same time,thereby reducing or avoiding the bad influence of network congestion on control performance.Secondly,considering the PMSM contains unknown dynamics and external disturbances in the actual application,the higher-order neural network is applied to approximate the unknown dynamics of the system,and the disturbance observer is designed to estimate the external disturbances.Therefore,the influence of the unknown dynamics and external disturbances can be compensated and then the robustness of the system is improved.Moreover,because of the existence of the mismatched disturbance,a novel variable substitution technology is used for the original controlled system to deal with the noncausal problem caused by the mismatched disturbance.Then,the absolute event-triggered mechanism is also introduced into the network channel between the controller and the actuator,which can effectively save the network bandwidth resources while realizing the system tracking performance.Finally,for the limited network bandwidth resources,a dual channel event-triggered mechanism is designed,which makes it unnecessary to realize the continuous information flow transmission between the sensor and the controller,and further reduces the consumption of network bandwidth resources.At the same time,the designed event-triggered threshold contains the system states information.Compared with the absolute event-triggered mechanism,it can coordinate the relationship between the system control performance and the event-triggered threshold,so that the network communication resources can be used more effectively.