A Self-Tuning Proportional-Integral-Derivative Controller for an Autonomous Underwater Vehicle, Based On Taguchi Method
A Self-Tuning Proportional-Integral-Derivative Controller for an Autonomous Underwater Vehicle, Based On Taguchi Method
复制标题
基于田口法的自主水下航行器自整定比例积分微分控制器
DOI：
10.3844/jcssp.2010.862.871
复制
发表时间：
2010
影响因子：
--
通讯作者：
T. Asokan
中科院分区：
文献类型：
--
作者：
M. Santhakumar;T. Asokan
问题陈述：传统的比例-积分-微分（PID）控制器
一旦PID增益被适当地调整，则表现出适度良好的性能。但当
系统的动态特性取决于时间或系统的操作条件
变化时，需要重新调谐增益以获得期望的性能。这一情况使
研究人员和实践者对PID控制的兴趣。PID控制器的自整定已经成为一种
随着算法和计算机的出现和容易获得，新的和活跃的研究领域。
研究了水下机器人自主运动控制的自校正算法
车辆.方法：自调整机制将避免耗时的控制器手动调整
并通过提供最佳PID控制器设置作为系统动态特性来保证更好的结果，
操作点改变。文献中的大多数自调整方法都是基于
频率响应特性和搜索方法。在这项研究中，我们提出了一种基于田口的鲁棒设计方法的自主水下航行器控制器的自整定方法。该算法利用期望状态变量和实际状态变量，以较少的计算量实现了控制器增益的真实的实时鲁棒最优整定。它既可用于无对象数学模型的单输入单输出（SISO）系统，也可用于多输入多输出（MIMO）系统。结果如下：通过水平面上的AUV控制（偏航平面控制）仿真研究，验证了所提方案的性能和有效性。所提出的自整定方案的仿真结果进行了比较与传统的PID控制器，这是由齐格勒-尼科尔斯（ZN）和田口的调整方法调整。这些结果表明，积分平方误差（伊势）是显着减少，从传统的控制器。该方法的鲁棒性进行了验证，并通过数值模拟，使用实验水下机器人模型在不同的工作条件下的结果。结论/建议：通过使用该方案，PID控制器增益相对于系统动态或操作条件的变化被自动在线优化调整。发现这种技术比传统的调谐方法更有效，并且当植物的数学模型不可用时，它甚至非常方便。计算机仿真表明，该方法具有很好的跟踪性能和鲁棒性，即使在干扰的存在。该方法具有结构简单、鲁棒性强、易于计算等优点，非常适合于水下机器人的真实的实时控制，同时也为将该方法推广到三维水下机器人的跟踪控制提供了可能。
Problem statement: Conventional Proportional-Integral-Derivative (PID) controllers
exhibit moderately good performance once the PID gains are properly tuned. However, when the
dynamic characteristics of the system are time dependent or the operating conditions of the system
vary, it is necessary to retune the gains to obtain desired performance. This situation has renewed the
interest of researchers and practitioners in PID control. Self-tuning of PID controllers has emerged as a
new and active area of research with the advent and easy availability of algorithms and computers.
This study discusses self-tuning (auto-tuning) algorithm for control of autonomous underwater
vehicles. Approach: Self-tuning mechanism will avoid time consuming manual tuning of controllers
and promises better results by providing optimal PID controller settings as the system dynamics or
operating points change. Most of the self-tuning methods available in the literature were based on
frequency response characteristics and search methods. In this study, we proposed a method based on Taguchi’s robust design method for self-tuning of an autonomous underwater vehicle controller. The algorithm, based on this method, tuned the controller gains optimally and robustly in real time with less computation effort by using desired and actual state variables. It can be used for the Single-Input Single-Output (SISO) systems as well as Multi-Input Multi-Output (MIMO) systems without mathematical models of plants. Results: A simulation study of the AUV control on the horizontal plane (yaw plane control) was used to demonstrate and validate the performance and effectiveness of the proposed scheme. Simulation results of the proposed self-tuning scheme are compared with the conventional PID controllers which are tuned by Ziegler-Nichols (ZN) and Taguchi’s tuning methods. These results showed that the Integral Square Error (ISE) is significantly reduced from the conventional controllers. The robustness of this proposed self-tuning method was verified and results are presented through numerical simulations using an experimental underwater vehicle model under different working conditions. Conclusion/Recommendations: By using this scheme, the PID controller gains are optimally adjusted automatically online with respect to the system dynamics or operating condition changes. This technique found to be more effective than conventional tuning methods and it is even very convenient when mathematical models of plants are not available. Computer simulations showed that the proposed method has very good tracking performance and robustness even in the presence of disturbances. The simple structure, robustness and ease of computation of the proposed method make it very attractive for real time implementation for controlling of underwater vehicle and it offers a chance to extend the same technique to the three dimensional vehicle tracking control as well.