Intelligent fractional-order sliding mode control based maneuvering of an autonomous vehicle

3区 计算机科学 Q1 Computer Science
Raghavendra M. Shet, Girish V. Lakhekar, Nalini C. Iyer
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Abstract

This article proposes a new intelligent trajectory tracking control law for the precise maneuvering of an autonomous vehicle in the presence of parametric uncertainties and external disturbances. The controller design includes a fuzzy sliding mode algorithm for smooth motion control subjected to steering saturation and curvature constraints. Along with the Salp Swarm Optimization technique, explored for optimal selection of surface coefficient in fractional order Proportional-Derivative type \(P{D}^{\alpha }\) sliding manifold. The sliding variable on the surface approaches zero in a finite time. Further, the trajectory tracking control rule offers the stability of closed-loop tracking on the predetermined path and ensures finite time convergence to the sliding surface. In addition, to estimate the hitting gain in online mode, a supervisory fuzzy logic controller system is used. Therefore, it is not necessary to determine upper bounds on uncertainty in the dynamic parameters of autonomous vehicles. Lyapunov theory verifies the global asymptotic stability of the entire closed-loop control strategy. The major control issue is the input constraints arising primarily due to the capability of the steering actuating module, which causes significant deviation or vehicle instability. Consequently, it is desirable to design a robust adaptive stable controller, such as Adaptive Backstepping Control (ABC), even though it requires vehicle model information. Therefore, the proposed model-free intelligent sliding mode technique offers better tracking performance and vehicle stability in adverse conditions. Finally, the efficacy of the proposed control technique was confirmed through a comparative analysis based on numerical simulation using MATLAB/SIMULINK and experimental validation using Quanser’s self-driving car module. A quantitative study was conducted to elucidate the superior tracking performance of intelligent control over the traditional SMC and adaptive backstepping control methods.

Abstract Image

基于智能分数阶滑动模式控制的自动驾驶汽车操纵系统
本文提出了一种新的智能轨迹跟踪控制法,用于在存在参数不确定性和外部干扰的情况下精确操纵自主车辆。控制器设计包括一种模糊滑动模式算法,用于在转向饱和度和曲率约束条件下进行平滑运动控制。与 Salp Swarm Optimization 技术一起,探索了在分数阶比例-派生类型(P{D}^{\alpha }\ )滑动流形中表面系数的最优选择。表面上的滑动变量在有限的时间内趋近零。此外,轨迹跟踪控制规则提供了在预定路径上闭环跟踪的稳定性,并确保在有限时间内收敛到滑动曲面。此外,在在线模式下,为了估算打击增益,使用了监督模糊逻辑控制器系统。因此,无需确定自动驾驶车辆动态参数不确定性的上限。李亚普诺夫理论验证了整个闭环控制策略的全局渐近稳定性。主要的控制问题是输入限制,这主要是由于转向执行模块的能力造成的,它会导致重大偏差或车辆不稳定。因此,设计一种鲁棒的自适应稳定控制器(如自适应逆向控制 (ABC))是可取的,尽管它需要车辆模型信息。因此,所提出的无模型智能滑模技术能在不利条件下提供更好的跟踪性能和车辆稳定性。最后,通过使用 MATLAB/SIMULINK 进行数值模拟,并使用 Quanser 的自动驾驶汽车模块进行实验验证,对比分析证实了所提出的控制技术的有效性。通过定量研究,阐明了智能控制的跟踪性能优于传统的 SMC 和自适应反步进控制方法。
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来源期刊
Journal of Ambient Intelligence and Humanized Computing
Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.60
自引率
0.00%
发文量
854
期刊介绍: The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to): Pervasive/Ubiquitous Computing and Applications Cognitive wireless sensor network Embedded Systems and Software Mobile Computing and Wireless Communications Next Generation Multimedia Systems Security, Privacy and Trust Service and Semantic Computing Advanced Networking Architectures Dependable, Reliable and Autonomic Computing Embedded Smart Agents Context awareness, social sensing and inference Multi modal interaction design Ergonomics and product prototyping Intelligent and self-organizing transportation networks & services Healthcare Systems Virtual Humans & Virtual Worlds Wearables sensors and actuators
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