Experimental results for autonomous model-predictive trajectory planning tuned with machine learning

Yousefi, Shahram; Givigi, Sidney N.; Jardine, Peter T.

doi:10.1109/syscon.2017.7934801

Cited by 11 publications

(6 citation statements)

References 12 publications

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“…Some papers have handled the premises for figuring out the physical effects in mixed ways. 40–45 It is via testing to get coefficients. It can also be through mathematical terms. Table 1 shows the nomenclature of the variables included.…”

Section: Equations Of Motion For a Racing Dronementioning

confidence: 99%

Co-simulation platform for geometric design, trajectory control and guidance of racing drones

Quintero¹,

García-Nieto²,

Simarro³

et al. 2022

International Journal of Micro Air Vehicles

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The design of racing drones brings quite a thrilling challenge from a flight dynamics point of view. This work aims to offer a single-based simulation platform combining its geometric design, trajectory control, and guidance of racing drones. Also, it is reckoned from a pilot’s view in a classic FPV competition. Hence, it is an active platform for studying racing drones’ design founded on dynamics, with fifteen different drone models. It is one of the few existing platforms that combine all aspects of racing drones in a single simulation environment. Also, it is open access via Matlab Central - File Exchange.

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Section: Equations Of Motion For a Racing Dronementioning

confidence: 99%

Co-simulation platform for geometric design, trajectory control and guidance of racing drones

Quintero¹,

García-Nieto²,

Simarro³

et al. 2022

International Journal of Micro Air Vehicles

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“…However, utilizing fuzzy logic requires in-depth understanding of the underlying system dynamics which may not be available for new or inexpert practitioners. Recently, two other articles [11], [12] have adopted reinforcement learning (RL) for NMPC tuning as applied to UAVs. In the former, RL is utilized to tune NMPC for various flight conditions that are encountered within load transportation tasks, while in the latter, it is exploited for computing NMPC weights wherein NMPC is used to perform trajectory planning along with collision avoidance.…”

Section: Introductionmentioning

confidence: 99%

Automated Tuning of Nonlinear Model Predictive Controller by Reinforcement Learning

Mehndiratta

Camci

Kayacan

2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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One of the major challenges of model predictive control (MPC) for robotic applications is the non-trivial weight tuning process while crafting the objective function. This process is often executed using the trial-and-error method by the user. Consequently, the optimality of the weights and the time required for the process become highly dependent on the skill set and experience of the user. In this study, we present a generic and user-independent framework which automates the tuning process by reinforcement learning. The proposed method shows competency in tuning a nonlinear MPC (NMPC) which is employed for trajectory tracking control of aerial robots. It explores the desirable weights within less than an hour in iterative Gazebo simulations running on a standard desktop computer. The real world experiments illustrate that the NMPC weights explored by the proposed method result in a satisfactory trajectory tracking performance.

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“…For example, a reinforcement learning (RL) technique called learning automata (LA) has been used to tune the gains of proportional-integral-derivative (PID) controllers. 13 Recently, the application of this learning technique to linear 14,15 and nonlinear MPC 16 has been explored. The application areas for such works have been unmanned aerial vehicles [13][14][15] and ground vehicles.…”

Section: Introductionmentioning

confidence: 99%

“…13 Recently, the application of this learning technique to linear 14,15 and nonlinear MPC 16 has been explored. The application areas for such works have been unmanned aerial vehicles [13][14][15] and ground vehicles. 16 While previous work has demonstrated LA can be used to select suitable parameters, no formal analysis of the optimality of the resultant parameters yet exists.…”

Section: Introductionmentioning

confidence: 99%

Adaptive predictive control of a differential drive robot tuned with reinforcement learning

Jardine

Kogan

Givigi

et al. 2018

Adaptive Control & Signal

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One of the most important steps in designing a model predictive control strategy is selecting appropriate parameters for the relative weights of the objective function. Typically, these are selected through trial and error to meet the desired performance. In this paper, a reinforcement learning technique called learning automata is used to select appropriate parameters for the controller of a differential drive robot through a simulation process. Results of the simulation show that the parameters always converge, although to different values. A controller chosen by the learning process is then ported to a real platform. The selected controller is shown to control the robot better than a standard model predictive control. KEYWORDS feedback linearization, machine learning, model predictive control, reinforcement learning Abbreviations: FALA, finite action-set learning automata; LA, learning automata; MPC, model predictive control; RL, reinforcement learning.410

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Experimental results for autonomous model-predictive trajectory planning tuned with machine learning

Cited by 11 publications

References 12 publications

Co-simulation platform for geometric design, trajectory control and guidance of racing drones

Co-simulation platform for geometric design, trajectory control and guidance of racing drones

Automated Tuning of Nonlinear Model Predictive Controller by Reinforcement Learning

Adaptive predictive control of a differential drive robot tuned with reinforcement learning

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