Position tracking control for chaotic permanent magnet synchronous motors via indirect adaptive neural approximation

Command filtering-based adaptive control for chaotic permanent magnet synchronous motors considering practical considerations

In this paper, an efficient adaptive control is designed for chaotic Permanent Magnet Synchronous Motors (PMSMs) with full-state asymmetric time-varying constraints in the input saturation presence. The strategy that is suggested in this work is equipped with the command filtering for addressing the problem of the "explosion of complexity" available in the common backstepping method. In addition, the filtering errors are incorporated into the control design procedure for improving the control system performance. During the control design, the asymmetric barrier Lyapunov functions (BLFs) are employed so that the restriction of state variables in the given intervals is guaranteed. In the suggested control method, for approximating unknown nonlinear functions, the Bessel series is utilized as a simple but effective function approximation approach as a universal approximator. The presented design provides this advantage that by considering practical considerations, a reduced-order observer is also designed so that there is no need to mount the physical sensors to measure the position and the velocity of the chaotic PMSM. The Lyapunov stability theory is used to establish the boundedness of all the closed-loop signals. According to the comparative results obtained with neural networks, the presented control design is able to suppress the chaotic behavior of the PMSM drive system while ensuring an excellent tracking performance.

Field Weakening Operation Control Strategies of PMSM Based on Feedback Linearization

Based on current research into the mathematical model of the permanent magnet synchronous motor (PMSM) and the feedback linearization theory, a control strategy established upon feedback linearization is proposed. The Lie differential operation is performed on the output variable to obtain the state feedback of the nonlinear system, and the dynamic characteristics of the original system are transformed into linear dynamic characteristics. A current controller based on the input–output feedback linearization algorithm is designed to realize the input–output linearization control of the PMSM. The current controller decouples the d–q axis current from the flux linkage information of the motor and outputs a control voltage. When the motor speed reaches above the base speed, the field-forward and straight-axis current components are newly distributed to achieve field weakening control, which can realize the smooth transition between the constant torque region and weak magnetic region. Simulation and experimental results show the feasibility and viability of the strategy.

PMSM Vector Control Strategy Based on Active Disturbance Rejection Controller

Based on current research into the vector control principles of the permanent magnet synchronous motor (PMSM), a control strategy founded upon an Active Disturbances Rejection Controller (ADRC) is proposed. This control strategy consists of an ADRC speed loop and current controller. By studying the factors affecting the running state of a PMSM, a mathematical model is established, and the design principle of the active disturbances rejection controller is analyzed in order to design the ADRC speed loop. The speed loop considers errors caused by uncertain factors, such as external disturbances, to be the disturbance amount, which is observed and then compensated for by the ADRC, thereby improving the dynamic and static performance as well as the anti-disturbance capability of the system. In order to achieve the strong coupling of the PMSM, the current controller was also designed to decouple the d–q axis current. Our simulation and experimental results demonstrate the feasibility and practicability of this control strategy.

Sliding Mode Observer-Based Parameter Identification and Disturbance Compensation for Optimizing the Mode Predictive Control of PMSM

This paper reports on the optimal speed control problem in permanent magnet synchronous motor (PMSM) systems. To improve the speed control performance of a PMSM system, a model predictive control (MPC) method is incorporated into the control design of the speed loop. The control performance of the conventional MPC for PMSM systems is destroyed because of system disturbances such as parameter mismatches and external disturbances. To implement the MPC method in practical applications and to improve its robustness, a compensated scheme with an extended sliding mode observer (ESMO) is proposed in this paper. Firstly, for observing if and when the system model is mismatched, the ESMO is regarded as an extended sliding mode parameter observer (ESMPO) to identify the main mechanical parameters. The accurately obtained mechanical parameters are then updated into the MPC model. In addition, to overcome the influence of external load disturbances on the system, the observer is regarded as an extended sliding mode disturbance observer (ESMDO) to observe the unknown disturbances and provide a feed-forward compensation item based on the estimated disturbances to the model predictive speed controller. The simulation and experimental results show that the proposed ESMO can accurately observe the mechanical parameters of the system. Moreover, the optimized MPC improves the dynamic response behavior and exhibits a satisfactory disturbance rejection performance.

Adaptive Sliding Mode Control of Dynamic Systems Using Double Loop Recurrent Neural Network Structure

In this paper, an adaptive sliding mode control system using a double loop recurrent neural network (DLRNN) structure is proposed for a class of nonlinear dynamic systems. A new three-layer RNN is proposed to approximate unknown dynamics with two different kinds of feedback loops where the firing weights and output signal calculated in the last step are stored and used as the feedback signals in each feedback loop. Since the new structure has combined the advantages of internal feedback NN and external feedback NN, it can acquire the internal state information while the output signal is also captured, thus the new designed DLRNN can achieve better approximation performance compared with the regular NNs without feedback loops or the regular RNNs with a single feedback loop. The new proposed DLRNN structure is employed in an equivalent controller to approximate the unknown nonlinear system dynamics, and the parameters of the DLRNN are updated online by adaptive laws to get favorable approximation performance. To investigate the effectiveness of the proposed controller, the designed adaptive sliding mode controller with the DLRNN is applied to a -axis microelectromechanical system gyroscope to control the vibrating dynamics of the proof mass. Simulation results demonstrate that the proposed methodology can achieve good tracking property, and the comparisons of the approximation performance between radial basis function NN, RNN, and DLRNN show that the DLRNN can accurately estimate the unknown dynamics with a fast speed while the internal states of DLRNN are more stable.