Trajectory tracking control of ball on plate system using weighted Artificial Fish Swarm Algorithm based PID

Hussein, Suleiman U.; Muhammed, B. M.; Sikiru, Tajudeen H.; Umoh, Ifiok J.; Salawudeen, Ahmed Tijani

doi:10.1109/nigercon.2017.8281925

Cited by 10 publications

(2 citation statements)

References 16 publications

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“…Researchers in [119] took advantage of Gaussian optimization, namely simultaneous perturbation stochastic approximation, to tune the same controller for a robot arm balancing an inverted pole. Particle swarm optimization was also investigated to tune a PID controller [120]. The resulting controller has been applied to a ball on plate trajectory tracking system.…”

Section: Data-driven Controller Weights Adjustmentmentioning

confidence: 99%

Multibody dynamics and control using machine learning

et al. 2023

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Artificial intelligence and mechanical engineering are two mature fields of science that intersect more and more often. Computer-aided mechanical analysis tools, often multibody system software, are very versatile and have revolutionalized many industries. However, as shown by the literature presented in this review, combining the advantages of multibody system dynamics and machine learning creates new and exciting possibilities. For example, the multibody method can assist machine learning by providing synthetic data, while machine learning can provide fast and accurate subsystem models. The intersection of both approaches results in surrogate and hybrid modeling techniques, advanced control algorithms, and optimal design applications. A notable example is the development of autonomous systems for vehicles, robots, and mobile machinery. In our review we have found nontrivial, innovative, and even surprising applications of machine learning and multibody dynamics. This review focuses on applying neural networks, mainly deep learning, in connection with the multibody system method. Over one hundred and fifty papers are covered, and three main research areas are identified and introduced: data-driven modeling, model-based control and estimation, and data-driven control. The paper starts with a primer on machine learning and concludes with future research directions. The main goal is to provide a comprehensive and up-to-date review of existing literature to inspire further research.

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Section: Data-driven Controller Weights Adjustmentmentioning

confidence: 99%

Multibody dynamics and control using machine learning

et al. 2023

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“…There exist other forms of heuristic approaches integrated to classical controllers based on an expectation to either yield optimal stability or track the ball dynamics of a B&P system. Previous works of (Hussein, Muhammed et al, 2017) demonstrated that a weighted Artificial Fish Swarm Algorithm PID controller has the potential to tune desirable control parameters while providing trajectory tracking of the B&P system in a double feedback loop structure. Furthermore, Roy et al (2015) constructed a Particle Swarm Optimization for tuning the PD controller gain parameters and hence was used to control trajectory tracking of the B&P system.…”

Section: Introductionmentioning

confidence: 99%

Heuristic and deep reinforcement learning-based PID control of trajectory tracking in a ball-and-plate system

Okafor

Udekwe

Ibrahim

et al. 2020

Journal of Information and Telecommunication

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The manual tuning of controller parameters, for example, tuning proportional integral derivative (PID) gains often relies on tedious human engineering. To curb the aforementioned problem, we propose an artificial intelligence-based deep reinforcement learning (RL) PID controller (three variants) compared with genetic algorithm-based PID (GA-PID) and classical PID; a total of five controllers were simulated for controlling and trajectory tracking of the ball dynamics in a linearized ball-and-plate (B&P) system. For the experiments, we trained novel variants of deep RL-PID built from a customized deep deterministic policy gradient (DDPG) agent (by modifying the neural network architecture), resulting in two new RL agents (DDPG-FC-350-R-PID & DDPG-FC-350-E-PID). Each of the agents interacts with the environment through a policy and a learning algorithm to produce a set of actions (optimal PID gains). Additionally, we evaluated the five controllers to assess which method provides the best performance metrics in the context of the minimum index in predictive errors, steady-state-error, peak overshoot, and timeresponses. The results show that our proposed architecture (DDPG-FC-350-E-PID) yielded the best performance and surpasses all other approaches on most of the evaluation metric indices. Furthermore, an appropriate training of an artificial intelligencebased controller can aid to obtain the best path tracking.

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Development of a Hybrid Optimization Strategy Based on a Bacterial Foraging Algorithm (BFA) and a Particle Swarming Algorithm (PSO) to Tune the PID Controller of a Ball and Plate System

Ponce

Montalvo

2022

Lecture Notes in Electrical Engineering

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Trajectory tracking control of ball on plate system using weighted Artificial Fish Swarm Algorithm based PID

Cited by 10 publications

References 16 publications

Multibody dynamics and control using machine learning

Multibody dynamics and control using machine learning

Heuristic and deep reinforcement learning-based PID control of trajectory tracking in a ball-and-plate system

Development of a Hybrid Optimization Strategy Based on a Bacterial Foraging Algorithm (BFA) and a Particle Swarming Algorithm (PSO) to Tune the PID Controller of a Ball and Plate System

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