This paper introduces the election optimization algorithm (EOA), a meta-heuristic approach for engineering optimization problems. Inspired by the democratic electoral system, focusing on the presidential election, EOA emulates the complete election process to optimize solutions. By simulating the presidential election, EOA introduces a novel position-tracking strategy that expands the scope of effectively solvable problems, surpassing conventional human-based algorithms, specifically, the political optimizer. EOA incorporates explicit behaviors observed during elections, including the party nomination and presidential election. During the party nomination, the search space is broadened to avoid local optima by integrating diverse strategies and suggestions from within the party. In the presidential election, adequate population diversity is maintained in later stages through further campaigning between elite candidates elected within the party. To establish a benchmark for comparison, EOA is rigorously assessed against several renowned and widely recognized algorithms in the field of optimization. EOA demonstrates superior performance in terms of average values and standard deviations across the twenty-three standard test functions and CEC2019. Through rigorous statistical analysis using the Wilcoxon rank-sum test at a significance level of 0.05, experimental results indicate that EOA consistently delivers high-quality solutions compared to the other benchmark algorithms. Moreover, the practical applicability of EOA is assessed by solving six complex engineering design problems, demonstrating its effectiveness in real-world scenarios.

The methods of Bayesian predictive inference and variation index are vital in the constant false alarm rate (CFAR) detection, which adaptively predicts and evaluates the background level to achieve target detection. However, in complex scenarios, the interference effect can lead to imprecise prediction and complex calculations of the background level, which results in severely degraded CFAR performance. To solve the above situation, a robust variation index CFAR detector based on Bayesian interference control (BVI-CFAR) is proposed. The clutter range profile (CRP) is equally divided and the decision-making selection is optimized to dynamically evaluate the background clutter level in this algorithm; meanwhile, the clutter level will feedback control the former. Then, based on the Bayesian interference control theory, the Bayesian classification interference control idea is proposed to simultaneously predict inference multiple targets in the background. Therefore, the anti-interference ability of the detector is improved while reducing the computational complexity. The false alarm rate and decision expression are given, and the detection is extended when the segmentation and interference are arbitrary. Both the simulation results and the field test results verify the favorable performance of the algorithm in complex scenarios.

Precision machining fields often require worktables with different stroke sizes. To address the need for scalability and facilitate manufacturing, this study proposes a novel infinite expansion magnetically levitated planar motor (MLPM) based on PCB stator coils. Different from existing magnetic levitation systems that use PCB coils, the design presented in this paper utilizes smaller coil units, with each coil being independent of one another. The coils are structured in a spiral pattern on a 16-layer PCB, comprising 15 layers of coils, while the last layer is dedicated to wiring and other circuits. Magnetic field modeling is conducted for both the stator coil and the 2D Halbach array structure employed in the system. A simple table lookup method is employed to accurately account for the prevalent end effects observed during system motion. Additionally, the decoupling effect of magnetic force and torque is evaluated by solving for the current vector at different points along a specific trajectory. To verify the accuracy of the proposed system’s modeling, a prototype is developed and tested. Experimental results demonstrate that compared to traditional harmonic model methods, the proposed approach improves the calculation accuracy of magnetic force by 50.31% and torque by 70.65%. This study presents a new MLPM system with vast potential applications in precision manufacturing and robotics. The innovative design and improved performance characteristics make it a promising technology for enhancing the capabilities of worktables in precision machining fields.

The precision positioning equipment is essential in many manufacture areas. This paper develops a novel noncontact six-degree-of-freedom maglev planar actuator (MLPA) for applications in precision manufacturing area. To achieve the tracking and positioning of MLPA, a model predictive control (MPC) algorithm is introduced. However, the high frequency feedback control of maglev system up to kilohertz level poses the challenges in the online optimization of MPC. We argue that the field-programmable gate array (FPGA), a high throughput and low latency processor, accelerates the computation of MPC, and the High-Level synthesis (HLS) tool simplifies the implementation of the optimization algorithm, which is the most complex module in MPC. In this work, the dual-gradient projection method is introduced to realize the online optimization with its characteristics of parallelism. Benefiting from the parallel architecture of FPGA, the dual-gradient projection method successfully runs in the millisecond level with the acceleration performance highlighted by comparing with active set and interior point methods. Furthermore, the FPGA-based MPC is deployed for MLPA to inhibit the trajectory tracking error and improve the step response speed. The synthesis of the proposed FPGA-based MPC scheme indicates the prediction horizon of MPC is able to theoretically reach about 20, and the experiments undertaken on the real-time system shows that tracking performance of MLPA is improved with the prediction horizon expanding.

Sensitivity analysis is an important step in power system parameter identification. It is used to find the key parameters that need to be identified and have a greater impact on the model output among the numerous model parameters. However, the existing parameter identification methods simultaneously identify all key parameters and do not make full use of the difference between high and low parameter sensitivity to further improve the identification accuracy. In this paper, the scatter plot method of global sensitivity analysis is improved, and a plane observation method of high dimensional parameter optimization space is proposed, and it is applied to the parameter identification of the synthesis load model. Based on this method, the search range of parameters can be continuously reduced according to the inflection point of the envelope of the lowest point of the planar graph in each parameter optimization subspace. The value of some high-sensitivity parameters can be determined directly through the narrowing search range. The method proposed in this paper can be used to further reduce the parameters that need to be identified at the same time and may help to further improve the accuracy of parameter identification.