Intelligent Control of Unmanned Vehicles

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 15 October 2024 | Viewed by 3018

Special Issue Editors


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Guest Editor
Informatik für Luft- und Raumfahrt, Universität Würzburg, 97074 Würzburg, Germany
Interests: real time dependable distributed control systems; aerospace applications; real time operating systems; real time communication protocols and middleware; UAS/UAV Drones/unmanned areal vehicles and systems; AUVs under autonomous underwater vehicles; satellites and space vehicles
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Informatik für Luft- und Raumfahrt, Universität Würzburg, 97074 Würzburg, Germany
Interests: embedded systems; Drones; navigation

Special Issue Information

Dear Colleagues,

Intelligent control of unmanned vehicles, such as self-driving cars, unmanned aerial vehicles (UAVs), autonomous underwater vehicles (AUVs), and satellites and micro launchers, is a rapidly evolving field empowered by artificial intelligence (AI). AI plays a crucial role in enabling these vehicles to perceive their surroundings, make informed decisions, and execute precise actions. However, the advancements in AI for unmanned vehicles bring about several considerations, including an increase in hardware requirements and power consumption, the need for cost-effective solutions, and safety concerns, to name a few.

This will Special Issue address the current state of the art, demonstrating the effectiveness of intelligent control algorithms, methods, and systems within the target applications above and providing cross-disciplinary ideas to address current and future challenges.            

Topics of interest include, but are not limited to:

  • intelligent control architectures;
  • perception, decision-making, and action execution of unmanned vehicles;
  • hardware requirements and optimizations for efficient implementation;
  • cost-effective strategies for integrating AI in unmanned vehicles;
  • safety considerations and risk mitigation techniques;
  • verification and validation for ensuring reliable operations.

Prof. Dr. Sergio Montenegro
Dr. Michael Strohmeier
Guest Editors

Manuscript Submission Information

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Published Papers (4 papers)

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Research

26 pages, 2918 KiB  
Article
Model Simplification for Asymmetric Marine Vehicles in Horizontal Motion—Verification of Selected Tracking Control Algorithms
by Przemyslaw Herman
Electronics 2024, 13(10), 1820; https://doi.org/10.3390/electronics13101820 - 8 May 2024
Viewed by 324
Abstract
This paper addresses a trajectory tracking control algorithm for underactuated marine vehicles moving horizontally in which the current in the North–East–Down frame is constant. This algorithm is a modification of a control scheme based on the input-output feedback linearization method, for which the [...] Read more.
This paper addresses a trajectory tracking control algorithm for underactuated marine vehicles moving horizontally in which the current in the North–East–Down frame is constant. This algorithm is a modification of a control scheme based on the input-output feedback linearization method, for which the application condition was that the vehicle was symmetric with respect to the left and right sides. The proposed control scheme can be applied to a fully asymmetric model, and, therefore, the geometric center can be different from the center of mass in both the longitudinal and lateral directions. A velocity transformation to generalized vehicle equations of motion was used to develop a suitable controller. Theoretical considerations were supported by simulation tests performed for a model with 3 degrees of freedom, in which the performance of the proposed algorithm was compared with that of the original algorithm and the selected control scheme based on a combination of backstepping and integral sliding mode control approaches. Full article
(This article belongs to the Special Issue Intelligent Control of Unmanned Vehicles)
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18 pages, 6688 KiB  
Article
Prediction Horizon-Varying Model Predictive Control (MPC) for Autonomous Vehicle Control
by Zhenbin Chen, Jiaqin Lai, Peixin Li, Omar I. Awad and Yubing Zhu
Electronics 2024, 13(8), 1442; https://doi.org/10.3390/electronics13081442 - 11 Apr 2024
Viewed by 549
Abstract
The prediction horizon is a key parameter in model predictive control (MPC), which is related to the effectiveness and stability of model predictive control. In vehicle control, the selection of a prediction horizon is influenced by factors such as speed, path curvature, and [...] Read more.
The prediction horizon is a key parameter in model predictive control (MPC), which is related to the effectiveness and stability of model predictive control. In vehicle control, the selection of a prediction horizon is influenced by factors such as speed, path curvature, and target point density. To accommodate varying conditions such as road curvature and vehicle speed, we proposed a control strategy using the proximal policy optimization (PPO) algorithm to adjust the prediction horizon, enabling MPC to achieve optimal performance, and called it PPO-MPC. We established a state space related to the path information and vehicle state, regarded the prediction horizon as actions, and designed a reward function to optimize the policy and value function. We conducted simulation verifications at various speeds and compared them with an MPC with fixed prediction horizons. The simulation demonstrates that the PPO-MPC proposed in this article exhibits strong adaptability and trajectory tracking capability. Full article
(This article belongs to the Special Issue Intelligent Control of Unmanned Vehicles)
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25 pages, 14797 KiB  
Article
Comparative Analysis of Metaheuristic Optimization Methods for Trajectory Generation of Automated Guided Vehicles
by Eduardo Bayona, Jesús Enrique Sierra-García and Matilde Santos
Electronics 2024, 13(4), 728; https://doi.org/10.3390/electronics13040728 - 11 Feb 2024
Viewed by 619
Abstract
This paper presents a comparative analysis of several metaheuristic optimization methods for generating trajectories of automated guided vehicles, which commonly operate in industrial environments. The goal is to address the challenge of efficient path planning for mobile robots, taking into account the specific [...] Read more.
This paper presents a comparative analysis of several metaheuristic optimization methods for generating trajectories of automated guided vehicles, which commonly operate in industrial environments. The goal is to address the challenge of efficient path planning for mobile robots, taking into account the specific capabilities and mobility limitations inherent to automated guided vehicles. To do this, three optimization techniques are compared: genetic algorithms, particle swarm optimization and pattern search. The findings of this study reveal the different efficiency of these trajectory optimization approaches. This comprehensive research shows the strengths and weaknesses of various optimization methods and offers valuable information for optimizing the trajectories of industrial vehicles using geometric occupancy maps. Full article
(This article belongs to the Special Issue Intelligent Control of Unmanned Vehicles)
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23 pages, 8094 KiB  
Article
Neural Network Approach Super-Twisting Sliding Mode Control for Path-Tracking of Autonomous Vehicles
by Hakjoo Kim and Seok-Cheol Kee
Electronics 2023, 12(17), 3635; https://doi.org/10.3390/electronics12173635 - 28 Aug 2023
Cited by 2 | Viewed by 1133
Abstract
This paper proposes a neural network approach adaptive super-twisting sliding mode control algorithm for autonomous vehicles. An adaptive and robust control algorithm in autonomous vehicles is needed to compensate for disturbance and parametric uncertainty from the variable environment and vehicle conditions. The sliding [...] Read more.
This paper proposes a neural network approach adaptive super-twisting sliding mode control algorithm for autonomous vehicles. An adaptive and robust control algorithm in autonomous vehicles is needed to compensate for disturbance and parametric uncertainty from the variable environment and vehicle conditions. The sliding mode control (SMC) is a robust controller that compensates for robust and reasonable control performance against disturbance and parametric uncertainty. However, the inherent limitation of the sliding mode control, namely the chattering phenomenon, has a negative effect on the system. Additionally, when the disturbance exceeds the defined boundaries, the control stability is compromised. To overcome these limitations, this study incorporates the radial basis function neural network (RBFNN) and Lyapunov function to estimate disturbance and parametric uncertainty. The estimated disturbance is reflected in the super-twisting sliding mode control (STSMC) to reduce the chattering phenomenon and achieve enhanced robust performance. The performance evaluation of the proposed neural network approach control algorithm is conducted using the double lane change (DLC) scenario and rapid path-tracking (RPT) scenario, implemented in the CarMaker and Matlab/Simulink environments, respectively. Full article
(This article belongs to the Special Issue Intelligent Control of Unmanned Vehicles)
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