Force-Driven Path Tracking Control Strategy Based on Vehicle Side-Slip Angle Observation

2021 ◽  
Author(s):  
Qiangqiang Yao ◽  
Ying Tian ◽  
Qun Wang
Keyword(s):  
Control Strategy ◽  
Tracking Control ◽  
Path Tracking ◽  
Slip Angle ◽  
Side Slip Angle ◽  
Angle Observation

scholarly journals A HEURISTIC CASCADING FUZZY LOGIC APPROACH TO REACTIVE NAVIGATION FOR UAV

2014 ◽  
Vol 15 (2) ◽  
Author(s):  
Yew-Chung Chak ◽  
Renuganth Varatharajoo
Keyword(s):  
Fuzzy Logic ◽  
Obstacle Avoidance ◽  
Control Strategy ◽  
Tracking Control ◽  
Path Tracking ◽  
Reactive Navigation ◽  
Logic Control ◽  
Fuzzy Logic Approach ◽  
Avoidance Control ◽  
Logic Approach

ABSTRACT: The capability of navigating Unmanned Aerial Vehicles (UAVs) safely in unknown terrain offers huge potential for wider applications in non-segregated airspace. Flying in non-segregated airspace present a risk of collision with static obstacles (e.g., towers, power lines) and moving obstacles (e.g., aircraft, balloons). In this work, we propose a heuristic cascading fuzzy logic control strategy to solve for the Conflict Detection and Resolution (CD&R) problem, in which the control strategy is comprised of two cascading modules. The first one is Obstacle Avoidance control and the latter is Path Tracking control. Simulation results show that the proposed architecture effectively resolves the conflicts and achieve rapid movement towards the target waypoint.ABSTRAK: Keupayaan mengemudi Kenderaan Udara Tanpa Pemandu (UAV) dengan selamat di kawasan yang tidak diketahui menawarkan potensi yang besar untuk aplikasi yang lebih luas dalam ruang udara yang tidak terasing. Terbang di ruang udara yang tidak terasing menimbulkan risiko perlanggaran dengan halangan statik (contohnya, menara, talian kuasa) dan halangan bergerak (contohnya, pesawat udara, belon). Dalam kajian ini, kami mencadangkan satu strategi heuristik kawalan logik kabur yang melata untuk menyelesaikan masalah Pengesanan Konflik dan Penyelesaian (CD&R), di mana strategi kawalan yang terdiri daripada dua modul melata. Hasil simulasi menunjukkan bahawa seni bina yang dicadangkan berjaya menyelesaikan konflik dan mencapai penerbangan pesat ke arah titik laluan sasaran.KEYWORDS: fuzzy logic; motion planning; obstacle avoidance; path tracking; reactive navigation; UAV

Direct yaw-moment control of vehicles based on phase plane analysis

2021 ◽  
pp. 095440702110523
Author(s):  
Jun Liu ◽  
Jian Song ◽  
Hanjie Li ◽  
He Huang
Keyword(s):  
Control Strategy ◽  
Phase Plane ◽  
Stable Region ◽  
Slip Angle ◽  
State Variables ◽  
Side Slip Angle ◽  
Direct Yaw Moment Control ◽  
Heading Direction ◽  
Yaw Moment Control ◽  
Moment Control

In view of the problems related to vehicle-handling stability and the real-time correction of the heading direction, nonlinear analysis of a vehicle steering system was carried out based on phase plane theory. Subsequently, direct yaw-moment control (DYC) of the vehicle was performed. A four-wheel, seven-degree-of-freedom nonlinear dynamic model that included the nonlinear characteristics of the tire was established. The stable and unstable regions of the vehicle phase plane were divided, and the stable boundary model was established by analyzing the side slip angle–yaw rate ([Formula: see text]) and side slip angle–side slip angle rate [Formula: see text] phase planes as functions of the vehicle state variables. In the unstable region of the phase plane, taking the instability degree as the control target, a fuzzy neural network control strategy was utilized to determine the additional yawing moment of the vehicle required for stability restoration, which pulled the vehicle back from an unstable state to the stable region. In the stable region of the phase plane, a fuzzy control strategy was utilized to determine the additional yawing moment so that the actual state variables followed the ideal state variables. In this way, the vehicle responded rapidly and accurately to the steering motion of the driver. A simulation platform was established in MATLAB/Simulink and three working condition was tested, that is, step, sine with dwell, and sine amplification signals. The results showed that the vehicle handling stability and the instantaneous heading-direction adjustment ability were both improved due to the control strategy.

scholarly journals Adaptive Coordinated Path Tracking Control Strategy for Autonomous Vehicles with Direct Yaw Moment Control

2022 ◽  
Vol 35 (1) ◽  
Author(s):  
Ying Tian ◽  
Qiangqiang Yao ◽  
Peng Hang ◽  
Shengyuan Wang
Keyword(s):  
Control Strategy ◽  
Autonomous Vehicles ◽  
Tracking Control ◽  
High Speed ◽  
Front Wheel ◽  
Path Tracking ◽  
Vehicle Stability ◽  
Tracking Accuracy ◽  
Large Curvature ◽  
Yaw Moment

AbstractIt is a striking fact that the path tracking accuracy of autonomous vehicles based on active front wheel steering is poor under high-speed and large-curvature conditions. In this study, an adaptive path tracking control strategy that coordinates active front wheel steering and direct yaw moment is proposed based on model predictive control algorithm. The recursive least square method with a forgetting factor is used to identify the rear tire cornering stiffness and update the path tracking system prediction model. To adaptively adjust the priorities of path tracking accuracy and vehicle stability, an adaptive strategy based on fuzzy rules is applied to change the weight coefficients in the cost function. An adaptive control strategy for coordinating active front steering and direct yaw moment is proposed to improve the path tracking accuracy under high-speed and large-curvature conditions. To ensure vehicle stability, the sideslip angle, yaw rate and zero moment methods are used to construct optimization constraints based on the model predictive control frame. It is verified through simulation experiments that the proposed adaptive coordinated control strategy can improve the path tracking accuracy and ensure vehicle stability under high-speed and large-curvature conditions.

scholarly journals A Two-Layer Controller for Lateral Path Tracking Control of Autonomous Vehicles

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3689
Author(s):  
Zhiwei He ◽  
Linzhen Nie ◽  
Zhishuai Yin ◽  
Song Huang
Keyword(s):  
Autonomous Vehicles ◽  
Tracking Control ◽  
Linear Time ◽  
Lower Layer ◽  
Path Tracking ◽  
Current Control ◽  
Slip Angle ◽  
Vehicle Stability ◽  
Tracking Accuracy ◽  
Steering Angle

This paper presents a two-layer controller for accurate and robust lateral path tracking control of highly automated vehicles. The upper-layer controller, which produces the front wheel steering angle, is implemented with a Linear Time-Varying MPC (LTV-MPC) whose prediction and control horizon are both optimized offline with particle swarm optimization (PSO) under varying working conditions. A constraint on the slip angle is imposed to prevent lateral forces from saturation to guarantee vehicle stability. The lower layer is a radial basis function neural network proportion-integral-derivative (RBFNN-PID) controller that generates electric current control signals executable by the steering motor to rapidly track the target steering angle. The nonlinear characteristics of the steering system are modeled and are identified on-line with the RBFNN so that the PID controller’s control parameters can be adjusted adaptively. The results of CarSim-Matlab/Simulink joint simulations show that the proposed hierarchical controller achieves a good level of path tracking accuracy while maintaining vehicle stability throughout the path tracking process, and is robust to dynamic changes in vehicle velocities and road adhesion coefficients.

Composite Path Tracking Control for Tractor–Trailer Vehicles Via Constrained Model Predictive Control and Direct Adaptive Fuzzy Techniques

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Ming Yue ◽  
Xiaoqiang Hou ◽  
Wenbin Hou
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Tracking Control ◽  
Path Tracking ◽  
Reference Trajectory ◽  
Control Effect ◽  
Adaptive Fuzzy ◽  
Dynamic Controller ◽  
Fuzzy Techniques

Tractor–trailer vehicles will suffer from nonholonomic constraint, uncertain disturbance, and various physical limits, when they perform path tracking maneuver autonomously. This paper presents a composite path tracking control strategy to tackle the various problems arising from not only vehicle kinematic but also dynamic levels via two powerful control techniques. The proposed composite control structure consists of a model predictive control (MPC)-based posture controller and a direct adaptive fuzzy-based dynamic controller, respectively. The former posture controller can make the underactuated trailer midpoint follow an arbitrary reference trajectory given by the earth-fixed frame, as well as satisfying various physical limits. Meanwhile, the latter dynamic controller enables the vehicle velocities to track the desired velocities produced by the former one, and the global asymptotical convergence of dynamic controller is strictly guaranteed in the sense of Lyapunov stability theorem. The simulation results illustrate that the presented control strategy can achieve a coordinated control effect for the sophisticated tractor–trailer vehicles, thereby enhancing their movement performance in complex environments.

scholarly journals Emergency collision avoidance control strategy based on four-wheel steering and differential braking

2021 ◽  
Author(s):  
Haiqing Li ◽  
Yongfu Li ◽  
Taixiong Zheng ◽  
Jiufei Luo ◽  
Zonghuan Guo
Keyword(s):  
Collision Avoidance ◽  
Obstacle Avoidance ◽  
Control Strategy ◽  
Tracking Control ◽  
Development Trend ◽  
Path Tracking ◽  
Intelligent Vehicles ◽  
Control Mode ◽  
Avoidance Control ◽  
Driving Stability

Abstract Path tracking control strategy of emergency collision avoidance is the research hotspot for intelligent vehicles, and active four-wheel steering and integrated chassis control such as differential braking are the development trend for the control system of intelligent vehicle. Considering both driving performance and path tracking performance, an active obstacle avoidance controller integrated with four-wheel steering (4WS), active rear steering (ARS) and differential braking control (RBC) based on adaptive model predictive theory (AMPC) is proposed. The designed active obstacle avoidance control architecture is composed of two parts, a supervisor and an MPC controller. The supervisor is responsible for selecting the appropriate control mode based on driving vehicle information and threshold of lateral and roll stability. In addition, a non-linear predict model is employed to obtain the future states of the driving vehicle. Then the AMPC is used to calculate the desired steering angle and differential braking toque when the driving stability indexes exceed the safety threshold. Finally, the proposed collision avoidance path tracking control strategy was simulated under emergency conditions via Carsim-Simulink co-simulation. The results show that the controller based on AMPC can be used to tracking the path of obstacle avoidance and has good performance in driving stability under emergencies.

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