Adaptive Path Tracking Controller for Intelligent Driving Vehicles
                    for Large Curvature Paths

Liu, Jie; Yang, Can

doi:10.4271/12-06-02-0013

Cited by 3 publications

(1 citation statement)

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“…Its upper controller was designed based on a linear time-varying MPC algorithm with parameters improved by the PSO algorithm, and its lower controller was designed based on a neural network algorithm to track the desired steering angle quickly. Liu et al [7] developed a lateral controller with a steering angle compensation method and adaptively changing prediction horizon parameters. Brown et al [8] developed a framework for an intelligent vehicle motion control system.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Control of Intelligent Driving Vehicles Based on MPC and Fuzzy PID

Yang

Liu

2023

Mathematical Problems in Engineering

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To improve the stability and accuracy of quintic polynomial trajectory tracking, an MPC (model predictive control) and fuzzy PID (proportional-integral-difference)- based control method are proposed. A lateral tracking controller is designed by using MPC with rule-based horizon parameters. The lateral tracking controller controls the steering angle to reduce the lateral tracking errors. A longitudinal tracking controller is designed by using a fuzzy PID. The longitudinal controller controls the motor torque and brake pressure referring to a throttle/brake calibration table to reduce the longitudinal tracking errors. By combining the two controllers, we achieve satisfactory trajectory tracking control. Relative vehicle trajectory tracking simulation is carried out under common scenarios of quintic polynomial trajectory in the Simulink/Carsim platform. The result shows that the strategy can avoid excessive trajectory tracking errors which ensures a better performance for trajectory tracking with high safety, stability, and adaptability.

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Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Control of Intelligent Driving Vehicles Based on MPC and Fuzzy PID

Yang

Liu

2023

Mathematical Problems in Engineering

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Control Law for Fast, Non-Ringing Transients for Direct Injector Needle Actuators and Other Dynamic Systems

Bright

2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">This paper discloses the simultaneous and interdependent development of an actuator and its novel control law. Magnetostrictive alloy terfenol-d offers high energy density. When packaged properly, quantum mechanics within terfenol-d maintains its performance. The novel control law tames and directs its untapped potential. Therefore, terfenol-d can provide lifetime direct operation of the needle in an injector for a compression-ignition engine, potentially improving emissions, efficiency, fuel flexibility, and combustion noise.</div><div class="htmlview paragraph">The novel control law yields a custom forcing function for desired boundary conditions such as either non-ringing or deliberately ringing transients for this actuator as well as other non-magnetostrictive dynamic systems. The two key characteristics that enable such a custom forcing function are to (1) use the Bright Principle of modeling all energy terms and (2) setting up a specific polynomial to solve for the desired boundary conditions for a particular dynamic system.</div><div class="htmlview paragraph">Test data from an actuator aided the development of this general and flexible control law. Implementing the control law, a computer predicts the necessary voltage with respect to time to achieve a set displacement within a defined transient time. A difference in frequency content appears between fast Fourier transform (FFT) spectra of non-ringing followed by deliberately ringing displacement data.</div></div>

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Trajectory Tracking for Autonomous Multi-Axle Vehicles Based on Linear Quadratic Regulator

Huang,

Fu,

Yuan

et al. 2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">To enhance the precision of trajectory tracking for an intelligent vehicle driven by a multi-axle wheel hub motor, a lateral control strategy based on the linear quadratic regulator (LQR) is proposed. First, a two-degrees-of-freedom dynamics model of the four-wheeled vehicle and a trajectory tracking error model are established. Second, a trajectory tracking controller employing the lateral LQR control algorithm is designed, while the longitudinal velocity is controlled using a PID controller. Furthermore, direct yaw moment control is incorporated to enhance the control precision and stability during trajectory tracking. Through joint simulations in TruckSim and Simulink under both low-speed and high-speed conditions, the control algorithm is evaluated. The simulation results demonstrate that the control algorithm is capable of effectively conducting joint simulation experiments under various operational scenarios. It accurately follows the predefined path model, maintaining a tracking distance deviation of less than 0.18 m, a yaw rate of under 14.5 degrees per second, and a lateral deviation angle of less than 3 degrees. This algorithm exhibits excellent trajectory tracking precision and a commendable level of stability.</div></div>

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Adaptive Path Tracking Controller for Intelligent Driving Vehicles for Large Curvature Paths

Cited by 3 publications

References 0 publications

Trajectory Tracking Control of Intelligent Driving Vehicles Based on MPC and Fuzzy PID

Trajectory Tracking Control of Intelligent Driving Vehicles Based on MPC and Fuzzy PID

Control Law for Fast, Non-Ringing Transients for Direct Injector Needle Actuators and Other Dynamic Systems

Trajectory Tracking for Autonomous Multi-Axle Vehicles Based on Linear Quadratic Regulator

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