This article presents two computationally efficient methods for selecting the optimal modulated voltage that can achieve superior dynamic performance for surface-mounted permanent magnet synchronous motors (SPMSMs). Specifically, when an SPMSM suffers a large reference or sudden load change, the controller might command a voltage reference, which is beyond the range of voltages that a modulator can synthesize. In such cases, the transient behavior of the motor can deteriorate when the demanded voltage is not properly limited to the voltage boundary. To address this issue, a simple overmodulation method based on common-mode-saturation injection (CMSI) is proposed. This strategy comes with very low computational cost and can easily find the voltage vector on the boundary, which is nearest to the reference voltage vector. Moreover, an alternative control method, referred to as quadratic program (QP) based deadbeat (DB) control, is proposed that also ensures optimal system performance during overmodualtion. According to this strategy, the control problem is formulated as a constrained QP, which is solved with an efficient solver based on an active-set method. Finally, extensive simulative and experimental investigations for an SPMSM are presented to demonstrate the effectiveness of the proposed overmodulation methods.

Intelligent control algorithms have been extensively utilized for adaptive controller parameter adjustment. While the Particle Swarm Optimization (PSO) algorithm has several issues: slow convergence speed requiring a large number of iterations, a tendency to get trapped in local optima, and difficulty escaping from them. It is also sensitive to the distribution of the solution space, where uneven distribution can lead to inefficient contraction. On the other hand, the Beetle Antennae Search (BAS) algorithm is robust, precise, and has strong global search capabilities. However, its limitation lies in focusing on a single individual. As the number of iterations increases, the step size decays, causing it to get stuck in local extrema and preventing escape. Although setting a fixed or larger initial step size can avoid this, it results in poor stability. The PSO algorithm, which targets a population, can help the BAS algorithm increase diversity and address its deficiencies. Conversely, the characteristics of the BAS algorithm can aid the PSO algorithm in finding the optimal solution early in the optimization process, accelerating convergence. Therefore, considering the combination of BAS and PSO algorithms can leverage their respective advantages and enhance overall algorithm performance. This paper proposes an improved algorithm, W-K-BSO, which integrates the Beetle Antennae Search strategy into the local search phase of PSO. By leveraging chaotic mapping, the algorithm enhances population diversity and accelerates convergence speed. Additionally, the adoption of linearly decreasing inertia weight enhances algorithm performance, while the coordinated control of the contraction factor and inertia weight regulates global and local optimization performance. Furthermore, the influence of beetle antennae position increments on particles is incorporated, along with the establishment of new velocity update rules. Simulation experiments conducted on nine benchmark functions demonstrate that the W-K-BSO algorithm consistently exhibits strong optimization capabilities. It significantly improves the ability to escape local optima, convergence precision, and algorithm stability across various dimensions, with enhancements ranging from 7 to 9 orders of magnitude compared to the BAS algorithm. Application of the W-K-BSO algorithm to PID optimization for the Pointing and Tracking System (PTS) reduced system stabilization time by 28.5%, confirming the algorithm’s superiority and competitiveness.

This study concerns the problem of integrated optimization of structure and control based on a fast steering mirror, aiming to achieve simultaneous optimization of the mechanical structure and control system. The traditional research and development path of the fast steering mirror involves a lengthy process from the initial design to the final physical manufacture. In the prior process, it was necessary to produce physical prototypes for repeated debugging and iterative optimization to achieve design requirements, but this approach consumes a significant amount of time and cost. To expedite this process and reduce unnecessary experimental costs, this study proposes an integrated optimization of structure and control (IOSC) method. With the use of IOSC, it is possible to achieve simultaneous optimization of structure and control. Specifically, the use of non-dominated sorting genetic algorithm II (NSGA-II) obtains globally optimal controller parameters and mechanical structure parameters under certain performance indices. This achieves an effective balance between the resonance frequency generated by the system and the working bandwidth, providing a high-precision reference for the research and development of fast steering mirrors.

Measurement delays and model parametric uncertainties are meaningful issues in actual systems. Addressing the simultaneous existence of random model parametric uncertainties and constant measurement delay in the discrete-time linear systems, this study proposes a novel robust estimation method based on the combination of Kalman filter regularized least-squares (RLS) framework and state augmentation. The state augmentation method is elaborately designed, and the cost function is improved by considering the influence of modelling errors. A recursive program similar to the Kalman filter is derived. Meanwhile, the asymptotic stability conditions of the proposed estimator and the boundedness conditions of its error covariance are analyzed theoretically. Numerical simulation results show that the proposed method has a better processing capability for measurement delay and better robustness to model parametric uncertainties than the Kalman filter based on nominal parameters.

This article presents a Robust Servo Linear Quadratic Regulator (RSLQR) method based on the Linear Extended State Observer (LESO), which solves the problem of optimal tracking control of electro-optical system under disturbance. For the problem that the accuracy of the electro-optical tracking system is reduced due to disturbance and modeling error, the LESO is adopted in this article to obtain and compensate the influence of internal and external disturbances in the controlled object model, so as to improve the disturbance suppression ability of the electro-optical tracking system. Considering the influence of weighting matrix in the RSLQR method on system's dynamic response performance, this article presents a selection criterion of weighting matrix in the RSLQR to meet the performance requirements of closed-loop system. Through simulation analysis and experimental verification, the design method proposed in this article has great reference value for the optimization of the dynamic response performance of the electro-optical tracking system and the improvement of the disturbance suppression ability.

For dual three-phase permanent magnet synchronous machines (DPMSMs), this article presents three dynamic overmodulation methods for selecting optimal duty cycles with maximum dc-link utilization. Concretely, the voltage references computed by the current controller exceed the modulating constraints when suffering a large reference or sudden load change, resulting in a reduced performance under transient. To address this issue, this paper uses geometric projection to synthesize the voltage reference by selecting one or two active vectors, which are produced by two separate three-phase frames. Moreover, as vector space decomposition (VSD) models dual three-phase machines into two-dimensional orthogonal αβ and xy subspaces, the other two proposed methods solve the optimal duty cycles by a quadratic program (QP) solver for the four active voltage vectors. The difference between these two methods lies in the different selection of active voltage vectors in αβ and xy frames. Specifically, these two strategies based on the QP solver allow the cost function formulated as a single linear and bound constraint QP, which is calculated by an active set method. Finally, the experimental investigations for a DPMSM are presented to prove the viability and flexibility of the proposed overmodulation methodologies.