受约束的PSO六参数模糊PID调优方法用于水下车辆深度跟踪性能的平衡优化
Constrained PSO Six-Parameter Fuzzy PID Tuning Method for Balanced Optimization of Depth Tracking Performance in Underwater Vehicles
AI总结：
本文提出一种受约束的PSO六参数模糊PID调优方法，用于提升水下车辆深度跟踪性能，通过优化控制能量和饱和水平，显著降低误差和超调，提高系统响应速度。
摘要：
在工程应用中，水下车辆的深度控制必须同时满足快速跟踪、低超调和执行器约束的要求。传统的模糊PID调优往往依赖于经验方法，难以在性能提升和控制成本之间实现稳定且可重复的平衡解决方案。本文提出了一种受约束的粒子群优化（PSO）方法，用于调优六参数模糊PID控制器。通过调整基准PID参数以及模糊控制器的输入量化因子和输出比例增益，实现了模糊PID系统整体调优强度和动态响应特性的协同优化。为确保优化结果的工程可行性，引入了时间加权绝对误差积分、调节时间、相对超调控制能量和饱和占用率等指标。将控制能量约束应用于构建一个约束驱动的综合评价系统，抑制仅通过增加控制输入所实现的伪改进。仿真结果表明，在保持一致的控制能量和饱和水平的情况下，所提方法显著提升了深度跟踪性能：时间加权绝对误差积分从0.2631降低到0.1473，调节时间从2.301秒缩短到1.613秒，相对超调从0.1494降低到0.01839。控制能量从7980变化到7935，满足了能量约束，而饱和占用率从0.004降低到0.003。这些结果验证了所提出的受约束的六参数联合调优策略在水下车辆导航场景中深度控制的有效性和工程意义。
英文摘要：
Depth control of underwater vehicles in engineering applications must simultaneously satisfy requirements for rapid tracking, low overshoot, and actuator constraints. Traditional fuzzy PID tuning often relies on empirical methods, making it difficult to achieve a stable and reproducible equilibrium solution between performance enhancement and control cost. This paper proposes a constrained particle swarm optimization (PSO) method for tuning six-parameter fuzzy PID controllers. By adjusting the benchmark PID parameters alongside the fuzzy controller's input quantization factor and output proportional gain, it achieves synergistic optimization of the overall tuning strength and dynamic response characteristics of the fuzzy PID system. To ensure engineering feasibility of the optimization results, a time-weighted absolute error integral, adjustment time, relative overshoot control energy, and saturation occupancy rate are introduced. Control energy constraints are applied to construct a constraint-driven comprehensive evaluation system, suppressing pseudo-improvements achieved solely by increasing control inputs. Simulation results demonstrate that, while maintaining consistent control energy and saturation levels, the proposed method significantly enhances deep tracking performance: the time-weighted absolute error integral decreases from 0.2631 to 0.1473, the settling time shortens from 2.301 s to 1.613 s, and the relative overshoot reduces from 0.1494 to 0.01839. Control energy varied from 7980 to 7935, satisfying the energy constraint, while saturation occupancy decreased from 0.004 to 0.003. These results validate the effectiveness and engineering significance of the proposed constrained six-parameter joint tuning strategy for depth control in underwater vehicle navigation scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
