This paper designs an improved sliding mode observer (ISMO) compound control scheme combined with a disturbance observer to solve the chattering and anti-disturbance problems of the traditional sliding mode observer (SMO) for permanent magnet synchronous motor (PMSM) in a sensorless control system. First, the sign function is replaced by an exponential type input function, and the fuzzy control rules are developed to automatically regulate the boundary layer control coefficient of the exponential input function, thereby changing the convergence characteristics of the exponential input function and improving the system observation accuracy. Then, the integral sliding mode surface and the quadratic radical term function of the square of the state variable are introduced to reduce system chattering. The proposed ISMO is proved using Lyapunov’s law to guarantee the whole system is stable. Based on the exponential input function and the integral sliding surface, an improved sliding mode disturbance observer (ISMDO) is constructed as a feed-forward compensator, which can optimize the dynamic performance of the improved observation system and ensure the strong robustness of the system by compensating the q-axis current. Finally, MATLAB/Simulink simulation and experimental platform verification have been carried out, which confirms the feasibility of the proposed composite control scheme.

A piezoelectric actuator (PEA) has the characteristics of high control precision and no electromagnetic interference. To improve the degree of freedom (DOF) to adapt to more working scenes, a piezoelectric–electromagnetic hybrid-driven two-DOF actuator is proposed. The PEA adopts the composite structure of the lever amplification mechanism and triangular amplification mechanism. The structure effectively amplifies the output displacement of the piezoelectric stack and increases the clamping force between the driving foot and the mover. The electromagnetic actuator (EMA) adopts a multi-stage fractional slot concentrated winding permanent magnet synchronous actuator, which can better match the characteristics of PEA. The structure and working principle of the actuator are introduced, the dynamic analysis is carried out, and the factors affecting the clamping force are obtained. At the same time, the air gap magnetic field is analyzed, and the structural size of the actuator is optimized. The experiment shows that the maximum driving speed can reach 348 mm/s, the load capacity is 3 kg, the optimal initial rotor angle is 49°, the maximum torque is 2.9 N·m and the maximum speed is 9 rad/s, which proves the stability and feasibility of the actuator.

Deflection switched reluctance motors (DSRM) are prone to chattering at low speeds, which always affects the output efficiency of the DSRM and the mechanical loss of the motor. Combining the characteristics of a traditional reluctance motor with the strong nonlinear and high coupling of the DSRM, a control system for a DSRM based on a flexible neural network (FNN) is proposed in this paper. Based on the better robustness and fault tolerance of fuzzy PI control, the given speed signal is adjusted and converted into a torque control signal. As a result, the FNN control module possesses the strong self-learning ability and adaptive adjustment ability necessary to obtain the control voltage signal. Through simulations and experiments, it was verified that the control system can run stably on DSRM and shows good dynamic performance and anti-interference ability.

Piezoelectric-driven multi-degree-of-freedom motors can turn off self-lock, withstand high and low temperatures, are small in size and compact in structure, and can easily achieve miniaturization. However, they have a short life cycle and limited applications. In addition, high-intensity operation will result in a decrease in their stability. Electromagnetic-driven multi-degree-of-freedom motors, on the other hand, are simple and highly integrated, but they are large in volume and lack positioning accuracy. Therefore, combining the two drive modes can achieve complementary advantages, such as improving the motor’s torque, accuracy, and output performance. Firstly, the structure of the hybrid drive motor is introduced and its working principle is analyzed. The motor can achieve single and hybrid drive control, which is beneficial to improving the performance of the motor. Secondly, the influence of magnetization mode, permanent magnet thickness, slot torque, and stator mode on the motor is analyzed. Thirdly, the structure of the motor is determined to be 6 poles and 15 slots, the thickness of the permanent magnet is 12 mm, and the radial magnetization mode is used. Finally, the mixed torque and speed of the motor in the multi-degree-of-freedom direction are tested by experiments, which indirectly verifies the rationality of the structure design.

To improve the chattering problem caused by the terminal sliding mode control (TSMC) and sliding mode observer (SMO) in permanent magnet synchronous linear motor (PMLSM) control systems, this study presents the design of a continuous terminal sliding mode control (CTSMC) controller and an improved SMO. By enhancing the sliding mode surface, CTSMC enhances the dynamic response and robustness of the system. The observer replaces the traditional sliding mode switching law with a super-twisting (ST) algorithm, a twisting algorithm that makes use of the structural characteristics of the second-order sliding mode to ensure output continuity and reduce the observed buffeting. The sliding mode gain of the ST algorithm is optimized using the particle swarm optimization (PSO) algorithm to acquire the optimal parameters and fully exploit the observer’s performance potential. Finally, the proposed method is simulated and tested. The comparison results show that the proposed method boosts the system’s dynamic response and robustness and reduces chattering.

In order to reduce the harm of harmonic disturbances of the permanent magnet synchronous motor (PMSM) control system to the drive and improve the accuracy of the control system, a harmonic injection control method based on asymptotic fading memory Kalman filtering is proposed. Compared with the traditional harmonic injection method, this method reduces the torque pulsation of the PMSM, converges faster, and realizes fast stabilization of the control system. In order to improve the control accuracy of the system, extended Kalman filtering is used to estimate the mechanical angular velocity and optimize the harmonic extraction process to reduce the interference of noise signals in the extraction process. At the same time, a fading memory factor is introduced to replace the fixed gain in the Kalman filter, which can correct the system error and effectively prevent the filtering dispersion, thus enhancing the system’s stability. Finally, the system is simulated and experimentally analyzed, and the results show that the method can improve the dynamic response speed, stability, and control accuracy of the system compared with the traditional harmonic injection method.