A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

Nonlinear Modeling and Control of Driving Interfaces and Continuum Robots for System Performance Gains

With the rise of (semi)autonomous vehicles and continuum robotics technology and applications, there has been an increasing interest in controller and haptic interface designs. The presence of nonlinearities in the vehicle dynamics is the main challenge in the selection of control algorithms for real-time regulation and tracking of (semi)autonomous vehicles. Moreover, control of continuum structures with infinite dimensions proves to be difficult due to their complex dynamics plus the soft and flexible nature of the manipulator body. The trajectory tracking and control of automobile and robotic systems requires control algorithms that can effectively deal with the nonlinearities of the system without the need for approximation, modeling uncertainties, and input disturbances. Control strategies based on a linearized model are often inadequate in meeting precise performance requirements. To cope with these challenges, one must consider nonlinear techniques. Nonlinear control systems provide tools and methodologies for enabling the design and realization of (semi)autonomous vehicle and continuum robots with extended specifications based on the operational mission profiles. This dissertation provides an insight into various nonlinear controllers developed for (semi)autonomous vehicles and continuum robots as a guideline for future applications in the automobile and soft robotics field. A comprehensive assessment of the approaches and control strategies, as well as insight into the future areas of research in this field, are presented.First, two vehicle haptic interfaces, including a robotic grip and a joystick, both of which are accompanied by nonlinear sliding mode control, have been developed and studied on a steer-by-wire platform integrated with a virtual reality driving environment. An operator-in-the-loop evaluation that included 30 human test subjects was used to investigate these haptic steering interfaces over a prescribed series of driving maneuvers through real time data logging and post-test questionnaires. A conventional steering wheel with a robust sliding mode controller was used for all the driving events for comparison. Test subjects operated these interfaces for a given track comprised of a double lane-change maneuver and a country road driving event. Subjective and objective results demonstrate that the driver’s experience can be enhanced up to 75.3% with a robotic steering input when compared to the traditional steering wheel during extreme maneuvers such as high-speed driving and sharp turn (e.g., hairpin turn) passing. Second, a cellphone-inspired portable human-machine-interface (HMI) that incorporated the directional control of the vehicle as well as the brake and throttle functionality into a single holistic device will be presented. A nonlinear adaptive control technique and an optimal control approach based on driver intent were also proposed to accompany the mechatronic system for combined longitudinal and lateral vehicle guidance. Assisting the disabled drivers by excluding extensive arm and leg movements ergonomically, the device has been tested in a driving simulator platform. Human test subjects evaluated the mechatronic system with various control configurations through obstacle avoidance and city road driving test, and a conventional set of steering wheel and pedals were also utilized for comparison. Subjective and objective results from the tests demonstrate that the mobile driving interface with the proposed control scheme can enhance the driver’s performance by up to 55.8% when compared to the traditional driving system during aggressive maneuvers. The system’s superior performance during certain vehicle maneuvers and approval received from the participants demonstrated its potential as an alternative driving adaptation for disabled drivers. Third, a novel strategy is designed for trajectory control of a multi-section continuum robot in three-dimensional space to achieve accurate orientation, curvature, and section length tracking. The formulation connects the continuum manipulator dynamic behavior to a virtual discrete-jointed robot whose degrees of freedom are directly mapped to those of a continuum robot section under the hypothesis of constant curvature. Based on this connection, a computed torque control architecture is developed for the virtual robot, for which inverse kinematics and dynamic equations are constructed and exploited, with appropriate transformations developed for implementation on the continuum robot. The control algorithm is validated in a realistic simulation and implemented on a six degree-of-freedom two-section OctArm continuum manipulator. Both simulation and experimental results show that the proposed method could manage simultaneous extension/contraction, bending, and torsion actions on multi-section continuum robots with decent tracking performance (e.g. steady state arc length and curvature tracking error of 3.3mm and 130mm-1, respectively). Last, semi-autonomous vehicles equipped with assistive control systems may experience degraded lateral behaviors when aggressive driver steering commands compete with high levels of autonomy. This challenge can be mitigated with effective operator intent recognition, which can configure automated systems in context-specific situations where the driver intends to perform a steering maneuver. In this article, an ensemble learning-based driver intent recognition strategy has been developed. A nonlinear model predictive control algorithm has been designed and implemented to generate haptic feedback for lateral vehicle guidance, assisting the drivers in accomplishing their intended action. To validate the framework, operator-in-the-loop testing with 30 human subjects was conducted on a steer-by-wire platform with a virtual reality driving environment. The roadway scenarios included lane change, obstacle avoidance, intersection turns, and highway exit. The automated system with learning-based driver intent recognition was compared to both the automated system with a finite state machine-based driver intent estimator and the automated system without any driver intent prediction for all driving events. Test results demonstrate that semi-autonomous vehicle performance can be enhanced by up to 74.1% with a learning-based intent predictor. The proposed holistic framework that integrates human intelligence, machine learning algorithms, and vehicle control can help solve the driver-system conflict problem leading to safer vehicle operations

A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration

RISE-Based Integrated Motion Control of Autonomous Ground Vehicles With Asymptotic Prescribed Performance

This article investigates the integrated lane-keeping and roll control for autonomous ground vehicles (AGVs) considering the transient performance and system disturbances. The robust integral of the sign of error (RISE) control strategy is proposed to achieve the lane-keeping control purpose with rollover prevention, by guaranteeing the asymptotic stability of the closed-loop system, attenuating systematic disturbances, and maintaining the controlled states within the prescribed performance boundaries. Three contributions have been made in this article: 1) a new prescribed performance function (PPF) that does not require accurate initial errors is proposed to guarantee the tracking errors restricted within the predefined asymptotic boundaries; 2) a modified neural network (NN) estimator which requires fewer adaptively updated parameters is proposed to approximate the unknown vertical dynamics; and 3) the improved RISE control based on PPF is proposed to achieve the integrated control objective, which analytically guarantees both the controller continuity and closed-loop system asymptotic stability by integrating the signum error function. The overall system stability is proved with the Lyapunov function. The controller effectiveness and robustness are finally verified by comparative simulations using two representative driving maneuvers, based on the high-fidelity CarSim-Simulink simulation

Autonomous Driving Vehicles and Control System Design

Autonomous driving vehicles and the control system design have been undergoing rapid changes in the last decade and affecting the concept and behaviour of human traffic. However, the control system design for autonomous driving vehicles is still a great challenge since the real vehicles are subject to enormous dynamic constraints depending on the vehicle physical limitations, environmental constraints and surrounding obstacles. This paper presents a new scheme of nonlinear model predictive control subject to softened constraints for autonomous driving vehicles. When some vehicle dynamic limitations can be converted to softened constraints, the model predictive control optimizer can be easier to find out the optimal control action. This helps to improve the system stability and the application for further intelligent control in the future. Simulation results show that the new controller can drive the vehicle tracking well on different trajectories amid dynamic constraints on states, outputs and inputs

고성능 한계 핸들링을 위한 인휠모터 토크벡터링 제어

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2021.8. 이경수.지난 10년 동안 차량 자세 제어시스템(ESC)은 치명적인 충돌을 방지하기 위해 많은 상용 차량에서 비약적으로 발전되고 개발되고 있다. 특히, 차량 자세 제어 시스템은 악천후로 인한 미끄러운 도로와 같은 위험한 도로에서 불안정한 차량 주행 조건에서 사고를 피하는데 큰 역할을 한다. 그러나, 최근의 경우, 고성능 차량 또는 스포츠카 등의 경우 제동제어의 빈번한 개입은 운전의 즐거움을 감소시키는 불만도 존재한다. 최근 차량의 전동화와 함께, 자량 자세 제어시스템의 작동 영역인 한계 주행 핸들링 조건에서 각 휠의 독립적인 구동을 적용 할 수 있는 시스템 중 하나인 인휠 모터 시스템을 사용하여 차량의 종, 횡방향 특성을 제어 가능하게 하는 토크 벡터링 제어기술에 대한 연구가 활발하다. 따라서, 본 연구에서는 차량의 선회 한계 핸들링 조건에서 안정성과 주행 다이나믹 성능을 향상시킬 수 있는 토크 벡터링 제어기를 제안하고자 한다. 먼저, 차량의 비선형 주행 구간인 한계 핸들링 조건에 대한 자동 드리프트 제어 알고리즘을 제안한다. 이 알고리즘을 이용하여 토크벡터링제어에 차량의 다이나믹한 주행모드에 대한 통찰력을 제공하고 미끄러운 도로에서 차량의 높은 슬립 각도의 안정성 제어를 제공 할 수 있다. 또한, 인휠 모터 시스템을 차량의 전륜에 2개 모터로 사용하여 차량 고유의 특성인 차량 언더스티어 구배를 직접적 제어를 수행하여, 차량의 핸들링 성능을 향상시켰다. 제어기의 채터링 효과를 줄이고 빠른 응답을 얻기 위해 새로운 과도 매개 변수가 이용하여 수식화하여 구성하였으며, 차량의 정상 상태 및 과도 특성 향상을 검증하기 위하여 ISO 기반 시뮬레이션 및 차량 실험을 수행하였다. 마지막으로 요 제어기와 횡 슬립 각도 제어기로 구성된 MASMC (Multiple Adaptive Sliding Mode Control) 접근 방식을 사용하는 4륜 모터 시스템을 사용한 동적 토크벡터링 제어를 수행하였다. 높은 비선형 특성을 가진 차량의 전후륜 타이어의 코너링 강성은 적응제어기법을 이용하여 예측하였다. 따라서, 안전모드와 다이나믹 모드를 구성하여, 운전자로 하여금 원하는 주행의 조건에 맞게 선택할 수 있는 알고리즘을 구현하였다. 이 MASMC 알고리즘은 향후 전동화 차량에 주행안정성 향상과 다이나믹한 주행의 즐거움을 주는 기술로써, 전차량 시뮬레이션을 이용하여 검증하였다.In the last ten decades, vehicle stability control systems have been dramatically developed and adapted in many commercial vehicles to avoid fatal crashes. Significantly, ESC (Electric Stability Control) system can help escape the accident from unstable driving conditions with dangerous roads such as slippery roads due to inclement weather conditions. However, for the high performed vehicle, frequent intervention from ESC reduces the pleasure of fun-to-drive. Recently, the development of traction control technologies has been taking place with that of the electrification of vehicles. The IWMs (In-Wheel Motor system), which is one of the systems that can apply independent drive of each wheel, for the limit handling characteristics, which are the operation areas of the ESC, is introduced for the control that enables the lateral characteristics of the vehicle dynamics. Firstly, the automated drift control algorithm can be proposed for the nonlinear limit handling condition of vehicles. This approach can give an insight of fun-to-drive mode to TV (Torque Vector) control scheme, but also the stability control of high sideslip angle of the vehicle on slippery roads. Secondly, using IWMs system with front two motors, understeer gradient of vehicle, which is the unique characteristics of vehicle can be used for the proposed control strategy. A new transient parameter is formulated to be acquired rapid response of controller and reducing chattering effects. Simulation and vehicle tests are conducted for validation of TV control algorithm with steady-state and transient ISO-based tests. Finally, dynamic torque vectoring control with a four-wheel motor system with Multiple Adaptive Sliding Mode Control (MASMC) approach, which is composed of a yaw rate controller and sideslip angle controller, is introduced. Highly nonlinear characteristics, cornering stiffnesses of front and rear tires are estimated by adaptation law with measuring data. Consequently, there are two types of driving modes, the safety mode and the dynamic mode. MASMC algorithm can be found and validated by simulation in torque vectoring technology to improve the handling performance of fully electric vehicles.Chapter 1 Introduction 7 1.1. Background and Motivation 7 1.2. Literature review 11 1.3. Thesis Objectives 15 1.4. Thesis Outline 15 Chapter 2 Vehicle dynamic control at limit handling 17 2.1. Vehicle Model and Analysis 17 2.1.1. Lateral dynamics of vehicle 17 2.1.2. Longitudinal dynamics of vehicle 20 2.2. Tire Model 24 2.3. Analysis of vehicle drift for fun-to-drive 28 2.4. Designing A Controller for Automated Drift 34 2.4.1. Lateral controller 35 2.4.2. Longitudinal Controller 37 2.4.3. Stability Analysis 39 2.4.4. Validation with simulation and test 40 Chapter 3 Torque Vectoring Control with Front Two Motor In-Wheel Vehicles 47 3.1. Dynamic Torque Vectoring Control 48 3.1.1. In-wheel motor system (IWMs) 48 3.1.2. Dynamic system modeling 49 3.1.3. Designing controller 53 3.2. Validation with Simulation and Experiment 59 3.2.1. Simulation 59 3.2.2. Vehicle Experiment 64 Chapter 4 Dynamic handling control for Four-wheel Drive In-Wheel platform 75 4.1. Vehicle System Modeling 76 4.2. Motion Control based on MASMC 78 4.2.1. Yaw motion controller for the inner ASMC 80 4.2.2. Sideslip angle controller for the outer ASMC 84 4.3. Optimal Torque Distribution (OTD) 88 4.3.1. Constraints of dynamics 88 4.3.2. Optimal torque distribution law 90 4.4. Validation with Simulation 91 4.4.1. Simulation setup 91 4.4.2. Simulation results 92 Chapter 5 Conclusion and Future works 104 5.1 Conclusion 104 5.2 Future works 106 Bibliography 108 Abstract in Korean 114박

Feasible, Robust and Reliable Automation and Control for Autonomous Systems

The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences

Optimized adaptive MPC for lateral control of autonomous vehicles

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksAutonomous vehicles are the upcoming solution to most transportation problems such as safety, comfort and efficiency. The steering control is one of the main important tasks in achieving autonomous driving. Model predictive control (MPC) is among the fittest controllers for this task due to its optimal performance and ability to handle constraints. This paper proposes an adaptive MPC controller (AMPC) for the path tracking task, and an improved PSO algorithm for optimising the AMPC parameters. Parameter adaption is realised online using a lookup table approach. The propose AMPC performance is assessed and compared with the classic MPC and the Pure Pursuit controller through simulationsPeer ReviewedPostprint (author's final draft