Experimental backward integration for state-dependent differential Riccati equation (SDDRE): A case study on flapping-wing flying robot

IF 5.4 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
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Abstract

Backward integration (BI) is a classical approach for solving optimal control arising from linear quadratic regulator (LQR) design in differential form. It proposes a two-round solution, the first one starting from a final boundary condition to the initial one to generate the optimal gain, and the next one, solving the control system in a forward loop. Implementation of the BI on the nonlinear optimal control, the state-dependent differential Riccati equation (SDDRE), is a challenge since in the backward loop, the state information is missing and is not straightforward like the LQR. Hence, a control for backward motion is required to regulate the system from the terminal to the initial desired states. While there have been some valuable works on the theoretical implementation of the BI in simulations for the SDDRE, this approach has not been reported in experiments for the best knowledge of the authors. Here in this work, the SDDRE is solved using the BI and two backward and forward solution steps, and it is experimentally applied for a flapping-wing flying robot (FWFR). The execution of the control system is done onboard using Raspberry Pi 4B as the processor which has limited computational capacity. The trajectory tracking of a line in a closed limited space is proposed for the FWFR flight. The objective is to position the robot bird at the corner of the rectangular limited space with minimum error in translation. The results have been presented for 21 flights to show the repeatability and presented the best-case minimum error of 10 (cm) at the end of the trajectory, in the YZ-plane. Considering approximately 12 (m) flight path, the error is found less than 1 percent of the travel distance. The results were compared with forward integration to confirm the correctness of the computation. The experimental flight dataset, MATLAB simulation codes, and experimental Python codes are available as supplementary material for this work in the online version of the paper.

Abstract Image

状态相关微分里卡提方程(SDDRE)的实验后向积分:拍翼飞行机器人案例研究
后向积分法(BI)是解决由线性二次调节器(LQR)差分设计引起的最优控制问题的一种经典方法。它提出了一种两轮求解方法,第一轮是从最终边界条件到初始边界条件,以生成最优增益;第二轮是在前向回路中求解控制系统。在非线性最优控制的状态依赖微分里卡提方程(SDDRE)上实现 BI 是一项挑战,因为在后向环路中,状态信息缺失,不像 LQR 那样简单明了。因此,需要一种后向运动控制来调节系统从终端状态到初始期望状态。虽然在 SDDRE 的模拟中已经有一些关于 BI 理论实现的有价值的工作,但就作者所知,这种方法还没有在实验中报道过。在这项工作中,我们使用 BI 和两个前后求解步骤求解 SDDRE,并在实验中将其应用于拍翼飞行机器人(FWFR)。控制系统的执行使用 Raspberry Pi 4B 作为处理器,该处理器的计算能力有限。针对 FWFR 飞行,提出了在封闭的有限空间内进行直线轨迹跟踪的方法。目标是以最小的平移误差将机器鸟定位在矩形有限空间的角落。为了显示重复性,我们展示了 21 次飞行的结果,并提出了在 YZ 平面上,轨迹末端误差最小为 10(厘米)的最佳情况。考虑到飞行路径约为 12(米),发现误差小于飞行距离的 1%。计算结果与正积分进行了比较,以确认计算的正确性。实验飞行数据集、MATLAB 仿真代码和 Python 实验代码作为本文的补充材料,可在本文的在线版本中查阅。
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来源期刊
Control Engineering Practice
Control Engineering Practice 工程技术-工程:电子与电气
CiteScore
9.20
自引率
12.20%
发文量
183
审稿时长
44 days
期刊介绍: Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice''s sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper. The scope of Control Engineering Practice matches the activities of IFAC. Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome.
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