Optimized self-adaptive PID speed control for autonomous vehicles

Kebbati, Yassine; Ait-Oufroukh, Naïma; Vigneron, Vincent; Ichalal, Dalil; Gruyer, Dominique

doi:10.23919/icac50006.2021.9594131

Cited by 24 publications

(17 citation statements)

References 17 publications

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“…The longitudinal dynamics of a vehicle consist of several subsystems that represent the chassis, the engine, the transmission, the wheels, the tires and the brakes. Vehicle body dynamics can be modeled by the following equation [5]:…”

Section: A Longitudinal Dynamics Modelingmentioning

confidence: 99%

“…The propulsion system of electric vehicles is composed of the motor and the transmission. The motor can be modeled statically using the motor efficiency map and the transmission is modeled as a single speed gearbox as in equations ( 2) and ( 3) respectively [5]:…”

Section: A Longitudinal Dynamics Modelingmentioning

confidence: 99%

“…η is the motor efficiency where the exponent k = 1 if T e ω g ≥ 0, otherwise k = 0. The wheels can be modeled by equations ( 4) under the assumption of negligible slip [5], and the tires are modeled by the magic formula given in ( 5):…”

Section: A Longitudinal Dynamics Modelingmentioning

confidence: 99%

“…In a similar way, Jin et al [4] developed a fuzzy-PID for maintaining low speeds while driving downhill. Kebbati et al [5] developed a self-adaptive PID controller for the speed tracking task. They used genetic algorithms and neural networks to tune and optimize the PID gains for better tracking and disturbance rejection.…”

Section: Introductionmentioning

confidence: 99%

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Coordinated PSO-PID based longitudinal control with LPV-MPC based lateral control for autonomous vehicles

Kebbati

Oufroukh

Vigneron

et al. 2022

2022 European Control Conference (ECC)

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Autonomous driving is achieved by controlling the coupled nonlinear longitudinal and lateral vehicle dynamics. Longitudinal control greatly affects lateral dynamics and must preserve lateral stability conditions, while lateral controllers must take into account actuator limits and ride comfort. This work deals with the coordinated longitudinal and lateral control for autonomous driving. An improved particle swarm optimized PID (PSO-PID) is proposed to handle the task of speed tracking based on nonlinear longitudinal dynamics. An enhanced linear parameter varying model predictive controller (LPV-MPC) is also designed to control lateral dynamics, the latter is formulated with an adaptive LPV model in which the tire cornering stiffness coefficients are estimated by a recursive estimator. The proposed LPV-MPC is enhanced with an improved cost function to provide better performance and stability. Matlab/Carsim cosimulations are carried out to validate the proposed controllers.

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Section: A Longitudinal Dynamics Modelingmentioning

confidence: 99%

Section: A Longitudinal Dynamics Modelingmentioning

confidence: 99%

Section: A Longitudinal Dynamics Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Coordinated PSO-PID based longitudinal control with LPV-MPC based lateral control for autonomous vehicles

Kebbati

Oufroukh

Vigneron

et al. 2022

2022 European Control Conference (ECC)

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“…In vehicle trajectory tracking control, combining PID control with the genetic algorithm as well as designing a fuzzy controller based on interval type-3 can improve the stability of lateral control for vehicles [11,12]. The trajectory tracking accuracy can be improved by optimizing the controller parameters and adding data-driven control [13,14].…”

Section: Introductionmentioning

confidence: 99%

Battery Energy Consumption Analysis of Automated Vehicles Based on MPC Trajectory Tracking Control

Pei

Zhang

2022

Electrochem

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In the field of automated technology research and development, trajectory tracking plays a crucial role in the energy consumption of the vehicle’s power battery. Reducing the deviation between the actual trajectory and the reference trajectory is the focus of trajectory tracking research. This paper proposes the use of the model predictive control (MPC) method to reduce the deviation of lateral and longitudinal position between the actual driving trajectory and the reference trajectory. First, the driving conditions of the vehicle are reflected by establishing the vehicle dynamics model. Then, the MPC trajectory tracking controller is built by designing the objective function with constraints; Finally, the feasibility of this approach was verified by a joint Carsim-Simulink simulation. The simulation results show that the MPC controller designed in this paper can track the trajectory better, and reduce the lateral and longitudinal position deviation. To a certain extent, the battery energy consumption is reduced and the accuracy of the tracking trajectory and the safety of vehicle driving are improved.

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Lateral control for autonomous wheeled vehicles: A technical review

Kebbati

Ait-Oufroukh

Ichalal

et al. 2022

Asian Journal of Control

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Autonomous driving has the ability to reshape mobility and transportation by reducing road accidents, traffic jams, and air pollution. This can yield energy efficiency, convenience, and more productivity as significant driving time will be gained and used in other activities instead. Autonomous vehicles are complex systems consisting of several modules that perform perception, decision-making, planning, and control. Control is essential for achieving automatic driving; it is basically divided into longitudinal control that handles speed tracking and lateral control which ensures accurate steering. The latter is primordial in path tracking applications and recent research has witnessed a huge leap in this field. The aim of this paper is to provide a technical survey of the latest research on the lateral control of autonomous vehicles as well as to highlight technical challenges and limits for further developments.

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Optimized self-adaptive PID speed control for autonomous vehicles

Cited by 24 publications

References 17 publications

Coordinated PSO-PID based longitudinal control with LPV-MPC based lateral control for autonomous vehicles

Coordinated PSO-PID based longitudinal control with LPV-MPC based lateral control for autonomous vehicles

Battery Energy Consumption Analysis of Automated Vehicles Based on MPC Trajectory Tracking Control

Lateral control for autonomous wheeled vehicles: A technical review

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