In order to improve robustness and dynamic tracking performance, this article proposes a deadbeat control method based on an enhanced predictive model and an exponential extended state observer (EDB-EESO). By incorporating parameter mismatches and load perturbation as lumped disturbance, extended state-space realizations of permanent magnet synchronous motor (PMSM) speed and stator current mathematical model are presented. To achieve higher robustness under the disturbance, the exponential extended state observer (EESO) is proposed based on the extended state-space realizations. Combining the EESO, the enhanced predictive model is designed to improve overshoot and settling time caused by the system inertial link. To eliminate the effect of time delay, a two-step predictive control method is adopted in the enhanced predictive model. Furthermore, the cost function of EDB-EESO is evaluated by utilizing the deadbeat control method and the stability of EDB-EESO is proved. The experimental results validate the strong robustness and excellent dynamic tracking performance of EDB-EESO in PMSM drives compared with conventional methods.

The magnetic levitation technology has attracted blooming attention from all walks of life thanks to its superiorities such as noncontact and active control. While the intrinsic features of open-loop instability and strong nonlinearity, along with the uncertain external disturbance make the levitation control particularly significant and difficult. For speeding up the convergence and obtaining a better stability and disturbance robustness, this article proposes an ultralocal model-free adaptive supertwisting nonsingular terminal sliding mode control strategy for the magnetic levitation system. Different from the conventional model-based control, ultralocal model-free control breaks the constraint of a precise system model. Therefore, an improved second-order ultralocal model is established for the magnetic levitation system. For the benefit of finite time convergence and nonsingularity, the nonsingular terminal sliding mode surface is chosen. As a second-order algorithm, supertwisting (ST) algorithm can fundamentally suppress the chattering effect of the sliding mode control. An adaptive ST algorithm is designed for the further improvement of the system dynamic response. The stability of the proposed scheme is strictly verified based on the Lyapunov stability theory. Simulation and experimental comparisons with the existing control methods validate the exceeding performance of the proposed strategy under various operating conditions.

Since a data-driven model is adopted to describe the operating state of the plant in the model-free predictive control, it has been widely used in the motor driving realm to eliminate the influences caused by parameter mismatches and enhance the robustness of the system. However, due to the fixed model structure and heavy calculating process, it is difficult to obtain an improved control performance using time-series models in continuous-control-set (CCS) predictive algorithms. To solve these problems, a model-free predictive current control (MF-PCC) using adaptive ultra-localized time-series (AULTS) is proposed in this paper, and applied to a permanent magnet synchronous motor (PMSM) driving system as the current controller. The model structure is improved as a variable, and its orders are online adjusted according to the designed adaptive law and the current operating state of the system. The complex discrete-time transfer functions in the model are ultra-localized to simplify the realization in the CCS -type controller. All required coefficients in the model are estimated by the recursive least squares (RLS) algorithm, and the optimal gain is also found by the particle swarm optimization (PSO) algorithm. The effectiveness of the proposed method is demonstrated by the experimental results, as well as the advantages of the proposed method, including better model accuracy and current quality with suitable robustness compared with the conventional time-series-based MF-PCC.

Recently, data analysis is used in model-free predictive control to mitigate the effects of parameter mismatches in parametric models. However, the finite-control-set (FCS) type cannot fully satisfy high-quality requirements due to the variable switching frequency, and it is necessary to consider the continuous-control-set (CCS) type to achieve better control performances. Nevertheless, the use of conventional time series structures in CCS model-free predictive control algorithms poses a challenge due to the complex design of control laws. To address this issue, this article proposes a CCS model-free predictive control based on a time-series subspace, which is then applied to a permanent magnet synchronous motor (PMSM) driving system. This method constructs a time-series subspace model from data and creates a suitable control law using the recursive least squares algorithm and Lagrange method without any time-varying physical parameters, to predict the future behavior of the stator voltage. The stability of the proposed method is analyzed through Bode diagrams and zero/pole maps under different conditions. A complete set of experiments proves the feasibility and advantages including improved current quality, tracking performances, and system noises compared to the conventional control strategies

For multi-point magnetic levitation systems (MMLSs), cooperative control is crucial, as inconsistent disturbances to each levitation point (LP) will result in control failure of the entire system. Therefore, to enhance the overall disturbance-resisting performance of the MMLS, this paper proposes an ultra-local sliding mode adjacent cross-coupling controller (USMACC). First, a general model of the MMLS is established. The coupling relationship between LPs is deduced considering the composite motion of the system centroid. Then, an ultra-local sliding mode controller (USMC) is designed, which eliminates the parameter dependency of an accurate system model and possesses excellent dynamic performance for each LP. Next, adjacent cross-coupling control (ACC) is integrated into USMC, effectively reducing tracking errors of each LP and synchronization errors between LPs. Last, comparative experiments with conventional PID and USMC demonstrate that the MMLS adopting the proposed USMACC possesses exceptional overall disturbance-resisting performance under various inconsistent external disturbances.

To address the challenges of high-current harmonic distortion, excessive switching losses, and less desirable dynamic response in the single-phase LCL grid-connected inverter, this article proposes a multiobjective model predictive control (MOMPC) method based on an improved gray wolf optimization (IGWO) algorithm. The proposed method first establishes the mathematical model of the LCL inverter and formulates the cost function of FCS-MPC. Subsequently, the voltage of the capacitor is introduced to impose constraints on the cost function, thereby constructing the MOMPC to further enhance the control performance. Finally, the IGWO algorithm is employed to perform offline multiobjective optimization on current error, total harmonic distortion, and switching frequency to design the optimal weighting coefficients of MOMPC. In contrast to traditional MPC methods, the IGWO-based MOMPC can reduce total harmonic distortion, switching frequency, and control errors while enhancing system robustness and dynamic performance. The simulation and experimental results validate the feasibility and effectiveness of the proposed method.