Journal of Control Science and Engineering

The existing particle swarm optimization (PSO) algorithm has the disadvantages of application limitations and slow convergence speed when solving the problem of mobile robot path planning. This paper proposes an improved PSO integration scheme based on improved details, which integrates uniform distribution, exponential attenuation inertia weight, cubic spline interpolation function, and learning factor of enhanced control. Compared with other standard functions, our improved PSO (IPSO) can achieve better optimal results with less number of iteration steps than the different four path planning algorithms developed in the existing literature. IPSO makes the optimal path length with less than 20 iteration steps and reduces the path length and simulation time by 2.8% and 1.1 seconds, respectively.

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二次冷却和最后的电磁搅拌（F-EMS）是用于连续铸造两者的关键技术。这些参数通常优化和单独地控制这造成了不稳定的条件下的内部质量的波动。在本文中，基于多目标粒子群优化协调最优控制策略（MOPSO）算法进行二次冷却和F-EMS，这是基于多目标粒子群优化（MOPSO）算法求解的参数优化。固化和热传递模型为坯料温度和凝固的计算显影，并且自适应网格方法用于提高最优解的多样性和鲁棒性。次级冷却水和F-EMS”搅拌当前基于优化结果动态地控制。现场试验的结果表明，钢坯中的最大碳偏析等质量指标可以显著改善。

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小型无人机很容易受到外部干扰，特别是在大气环境中的风场干扰。其结果是，UAV的状态，包括姿态，速度和位置通常是无法跟踪所希望的控制命令。在本文中，不同类型的容易影响总结于UAV风场;此外，影响了他们的无人机风场的机制，首先严格分析。接着，新的“拒绝外部干扰”飞行模式UAV被提出抵消由侧风，这使得使用由空速和UAV的地面速度所获得的风速信息的轨迹偏差。为了实施“拒绝外部干扰”飞行模式下，基于理论Lyapunov稳定性变量模型参考自适应控制（VMRAC）系统提出的，它也可以处理的风切变和湍流对UAV飞行的不利影响。最后，仿真结果表明，该策略可以显著提高下风的干扰无人机的轨迹追随质量。

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本文介绍了电压反馈使用由元启发式优化PI控制器调谐增益对于四相位控制交错升压转换器。的元启发式优化，粒子群优化（PSO），遗传算法（GA）和禁忌搜索（TS）被施加到找到所提出的控制系统的最佳增益。在实验中，所设计的控制系统与MATLAB / Simulink的DSP板TMS320F28335实现。在本文中，有在测试控制系统的两个条件，无负载和有负载。通过PSO所提出的控制系统调谐增益的响应结果是没有过冲和方法，以稳定状态比GA和TS方法更好。此外，它能够根据这两个无负载和有负载条件的控制信号在恒定的水平，以维持输出电压反馈。其结果是，四相交错式升压转换是通过用PSO PI控制器调谐增益，其能有效地保持两个等级的电压调节。

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一个聪明的滚动接触疲劳试验设备ent is developed, and the control methods are presented. For obtaining the slip accurately, the control method based on master-slave synchronization is proposed. For controlling the loads in high precision, the control method took into consideration the influence by two factors, displacement and the load. The nonlinear interference and excess torque in load control are effectively suppressed. Based on the SIMOTION D425 which is the Siemens integrated motion control system, the control system architecture of the intelligent rolling contact fatigue test equipment is constructed. The solutions of slip ratio and the experimental load controlled by these methods are satisfactory with the requirement of design precision. In the validation experiment, the load control accuracy is ±3%, the average error of load control is 1.77%, and the average error of slip control is 0.26%. The experiment results show the proposed control methods are feasible and effective.

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蚱蜢优化算法（GOA）是一种新颖的元启发式算法。由于其易于部署和精度高的，它被广泛应用于各种工业场景，取得了良好的解决方案。但是，在同一时间，该算法GOA有一些缺点：（1）原线性收敛参数将导致勘探和开采不平衡的处理;（2）不稳定的收敛速度;和（3）容易陷入局部最优。在本文中，我们提出使用非线性收敛参数，小生机制增强的蚂蚱优化算法（EGOA），以及β-hill climbing technique to overcome the abovementioned shortcomings. In order to evaluate EGOA, we first select the benchmark set of GOA authors to test the performance improvement of EGOA compared to the basic GOA. The analysis includes exploration ability, exploitation ability, and convergence speed. Second, we select the novel CEC2019 benchmark set to test the optimization ability of EGOA in complex problems. According to the analysis of the results of the algorithms in two benchmark sets, it can be found that EGOA performs better than the other five metaheuristic algorithms. In order to further evaluate EGOA, we also apply EGOA to the engineering problem, such as the bin packing problem. We test EGOA and five other metaheuristic algorithms in SchWae2 instance. After analyzing the test results by the Friedman test, we can find that the performance of EGOA is better than other algorithms in bin packing problems.

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