Evolutive Tuning Optimization of a PID Controller for Autonomous Path-Following Robot

Abajo, Mikel Rico; Sierra-García, J. Enrique; Santos, Matilde

doi:10.1007/978-3-030-87869-6_43

Cited by 11 publications

(3 citation statements)

References 17 publications

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“…Utilizing a Fractional Order PID (FOPID) controller, Al-Mayyahi et al [18] focused on controlling a non-holonomic autonomous ground vehicle's movement to follow a specific reference path, with its parameters refined using particle swarm optimization (PSO). A soft computing evolutionary method, specifically the GA, was applied by Abajo et al [19] to find the optimal PID regulator settings for varying routes. Gao et al [20] proposed a segmented fuzzy PID controller enhanced by a hybrid PCAG algorithm that combines adaptive PSO and GA. Hajjami et al [21] utilized the butterfly optimization algorithm (BOA) to establish an optimal PID controller for improving the lateral dynamics of AVs.…”

Section: Introductionmentioning

confidence: 99%

A Novel Simulation-Based Optimization Method for Autonomous Vehicle Path Tracking with Urban Driving Application

Chen,

2023

Mathematics

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Autonomous driving technology heavily depends on accurate and smooth path tracking. Facing complex urban driving scenarios, developing a suite of high-performance and robust parameters for controllers becomes imperative. This paper proposes a stochastic simulation-based optimization model for optimizing the Proportional–Integral–Differential (PID) controller parameters, with tracking accuracy and smoothness as bi-objectives, and solves it using a domination-measure-based efficient global optimization (DMEGO) algorithm. In this model, the tracking accuracy and smoothness are indexed by the normalized dynamic time warping (NDTW) and the mean absolute lateral acceleration (MALA), respectively. In addition, we execute the PID controller in a realistic simulation environment using a CARLA simulator, which consider various city scenes, diverse routes, different vehicle types, road slopes, etc., to provide a comprehensive and reliable evaluation for the designed PID controller. In the DMEGO method, each solution undergoes evaluation using a fixed number of costly simulations. Then, utilizing the solutions and their estimated bi-objective values, two surrogate models for the bi-objectives are constructed using the Gaussian process (GP) model. The preliminary nondominated solutions can be obtained by optimizing the two surrogate models. Finally, a novel performance metric known as the domination measure is employed to evaluate the quality of each solution. This metric is then integrated with the crowding distance to selectively retain a candidate solution exhibiting superior performance and good diversity for the next iteration. In our numerical experiments, we first test the DMEGO algorithm against three other counterparts using a stochastic FON benchmark. The proposed approach is then employed to optimize the PID parameters considering the complexity and uncertainty of urban traffic. The numerical results demonstrate that the nondominated solutions obtained by DMEGO exhibit excellent performance in terms of tracking accuracy and smoothness under limited simulation budgets. Overall, the proposed approach may be a viable tool for solving multi-objective simulation-based optimization problem under uncertainties.

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Section: Introductionmentioning

confidence: 99%

A Novel Simulation-Based Optimization Method for Autonomous Vehicle Path Tracking with Urban Driving Application

Chen,

2023

Mathematics

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“…Los resultados obtenidos para trayectorias complejas superan de manera significativa a los logrados por un controlador Proporcional Integral Derivativo (PID). En (Abajo et al, 2021) se emplea un algoritmo genético para la sintonización de un controlador PID para el trazado de trayectorias, en el que podemos ver cuan relevante es conseguir un ajuste fino.…”

Section: Introductionunclassified

Diseño de un control de velocidad mediante redes neuronales y algoritmos genéticos para vehículos autónomos

Argente-Mena,

Santos,

Sierra García

2023

XLIV Jornadas De Automática: Libro De Actas: Universidad De Zaragoza, Escuela De Ingeniería Y Arquitectura, 6, 7 Y 8 De Septiem

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Los Vehículos Autónomos Guiados (AGV) son cada vez más populares en lo que a logística interna de las fábricas se refiere debido a su capacidad para transportar cargas pesadas y su alto grado de autonomía. No obstante, la dinámica de estos robots puede sufrir cambios debido a variaciones en la carga que transportan y/o a desgaste mecánico, lo cual implica una mayor complejidad en el control de velocidad. En muchas ocasiones se emplean controladores de tipo Proporcional Integral (PI) para dicho control. Sin embargo, este controlador exige un ajuste fino y carece de suficiente robustez ante variaciones de sus condiciones de trabajo. Con el fin de mejorar el rendimiento del control de velocidad, en este artículo se presenta el diseño de un neuro-controlador. Dado que encontrar unos valores óptimos para los hiperparámetros de aprendizaje puede ser difícil y requiere múltiples pruebas y ajustes, se opta por utilizar un Algoritmo Genético (AG) para buscar una solución válida de entre las óptimas.

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“…However, it is important to note that the effectiveness of PID control hinges on precise parameter tuning. Abajo et al [8] addressed the challenge of parameter tuning by employing a genetic algorithm (GA) for optimizing PID parameters during trajectory tracking. This approach offers an automated and data-driven method for achieving optimal control performance.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Autonomous Vehicle Lateral Control: A Linear Complementarity Model-Predictive Control Approach

Ye,

Wang,

Dai

2023

Applied Sciences

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Model-predictive control (MPC) offers significant advantages in addressing constraint-related challenges and plays a pivotal role in self-driving car technology. Its primary goal is to achieve precise trajectory tracking while prioritizing vehicle stability and safety. However, real-time operations often face challenges related to computational demands and low computational efficiency. To address these challenges, this paper introduces a novel lateral control algorithm for self-driving vehicles, which utilizes the linear complementarity problem (LCP) instead of the conventional quadratic programming (QP) method as the MPC optimization solution. This innovative approach incorporates the electric steering system into the vehicle dynamics model, allowing for precise torque regulation of the steering motor and enhancing control accuracy. The MPC algorithm adopts the LCP solution method to calculate control signals based on the vehicle’s state, ensuring both rapid and stable vehicle control. Simulation results demonstrate that the proposed MPC algorithm, utilizing the LCP solution method, effectively addresses efficiency issues in the lateral motion of self-driving cars. This leads to improvements in both driving stability and real-time performance. Overall, this innovative approach lays a solid foundation for the practical implementation of self-driving cars.

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Evolutive Tuning Optimization of a PID Controller for Autonomous Path-Following Robot

Cited by 11 publications

References 17 publications

A Novel Simulation-Based Optimization Method for Autonomous Vehicle Path Tracking with Urban Driving Application

A Novel Simulation-Based Optimization Method for Autonomous Vehicle Path Tracking with Urban Driving Application

Diseño de un control de velocidad mediante redes neuronales y algoritmos genéticos para vehículos autónomos

Enhancing Autonomous Vehicle Lateral Control: A Linear Complementarity Model-Predictive Control Approach

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