Long Wang

To improve the control accuracy and address the parameter disturbance issues of joint-driven permanent magnet synchronous motors in intelligent manufacturing, this paper proposes an improved deadbeat predictive current predictive control (DPCC) scheme that eliminates dead zones. This scheme establishes a multi-parameter identification model based on the error equation of the d-q axis predicted current, which improves the problem of not being able to identify all parameters caused by insufficient input signals. It also implements decoupling compensation for the coupling between the d-q axis inductance, stator resistance, and magnetic flux linkage. To meet the anticipated control objectives and account for external disturbances, a low-complexity specified performance tracking controller (LCSPC) based on output target error signals has been designed. This mitigates output delay issues arising from nonlinear components during motor operation. Finally, simulation analysis and experimental testing demonstrate that the proposed control scheme achieves high identification accuracy with minimal delay, thus meeting the transient control performance requirements for motors in intelligent manufacturing processes.

In order to improve the control performance of the position sensorless control system of permanent magnet synchronous motors and to reduce the influence of external uncertainties on the control system, such as inertia ingestion and load disturbance, this paper proposes a novel position sensorless control algorithm for permanent magnet synchronous motors based on an interleaved parallel extended sliding mode observer. Firstly, in order to identify the time-varying moment of inertia, load torque and viscous friction coefficient of the system, a novel interleaved parallel extended sliding mode observer based on a single-observer model is proposed, and a robust activator is designed to reduce the coupling between the parameters to be measured. Then, a new predefined-time sliding mode controller is designed for the face-mounted permanent magnet synchronous motor using sliding film control theory, which improves the response speed and control accuracy of the system. Then, the proposed novel interleaved parallel extended sliding mode observer and predefined-time sliding mode controller are used to design the permanent magnet synchronous motor control system, and the stability of the system is proved using the Lyapunov stability theorem. Finally, through simulation analysis and experimental tests, it is verified that the control strategy proposed in this paper can improve the identification accuracy of the motor parameters, reduce the time of identification, and improve the control accuracy and tracking speed.

In order to improve the observation accuracy of rotor positions in the sensorless control of permanent magnet synchronous motors and to simplify the parameter adjustment process, this paper proposes an improved finite-time adaptive super-twisting sliding mode observer. First, a linear gain term is introduced into the conventional super-twisting sliding mode observer model as a way of improving the identification accuracy of the observer. Then, for the multi-parameter variable problem in the traditional observer model, a rotational speed variable function design is presented, which simplifies the multi-variables into a single adaptive variable. This reduces the complexity of the observer model while further improving the observation accuracy and stability of the improved observer algorithm (which is verified using Lyapunov’s stability theory). A new back EMF filter and an adaptive phase-locked loop are then used to improve the model’s speed tracking capability. Finally, through simulation and experimental tests, the improved algorithm’s ability to quickly observe changes in rotor position and speed, as well as its fast convergence, small jitter and high accuracy characteristics, are verified.