Commparison Between Different Methods of Control of Ball and Plate System with 6DOF Stewart Platform

Kassem, Aghiad; Haddad, Hassan; Albitar, Chadi

doi:10.1016/j.ifacol.2015.09.158

Cited by 40 publications

(19 citation statements)

References 8 publications

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“…The dynamic modeling of a 2 degree of freedom (2-DOF) B&P system as shown in Figure 1 and can be modeled using the principle of Euler Lagrange (Kassem et al, 2015;Spaček, 2016).…”

Section: Non-linear Derivation Of the Bandp Systemmentioning

confidence: 99%

“…The enumerated systems are often expected to follow a time parameterized reference path. Most of the laboratory-based B&P (Dong et al, 2011) systems are inherently non-linear and unstable (Kassem et al, 2015) due to irregular swings of the plate and the ball positioning. Hence results in a B&P system trajectory tracking problem, which is a classical control challenge that put forward a premise that allows the testing of different control algorithms or techniques.…”

Section: Introductionmentioning

confidence: 99%

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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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“…The dynamic modeling of a 2 degree of freedom (2-DOF) B&P system as shown in Figure 1 and can be modeled using the principle of Euler Lagrange (Kassem et al, 2015;Spaček, 2016).…”

Section: Non-linear Derivation Of the Bandp Systemmentioning

confidence: 99%

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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“…Where t sd is the establishment time and M pd is the percentage of over-impulse. From this, the damping coefficient ξ and the natural frequency ω n are calculated using the expressions (13) and (14).…”

Section: B!ab!"!mentioning

confidence: 99%

“…In [13] four control methods are compared: PID controller, sliding mode controller, fuzzy controller and LQR controller; all applied to a ball and plate system. The results of this work relate the responses of the four controllers graphically and with values associated with establishment time, over-impulse and steady-state error.…”

Section: Introductionmentioning

confidence: 99%

Control of a ball-and-plate system using a State-feedback controller

Núñez

Acosta

Jiménez

2020

Ingeniare. Rev. chil. ing.

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A ball-and-plate platform is a common example of the dynamical system used to apply modeling and control concepts studied in some engineering courses. In this kind of systems, a spherical body is positioned on a mobile plate with two degrees of freedom. The system is operated by two servo motors that cause displacement of the sphere in the x and y axis in the plane of the platform. It is known that such a system is highly unstable and non-linear. Initially, from a phenomenological analysis we obtained a state-space representation of the system. Then, a state feedback controller using the pole assignment method was designed to maintain a desired position of the sphere over the plane. A computer vision technique allowed us measuring the two-dimensional position of the sphere over the plane in real time. Model of the plant under study and controller design for sphere positioning were experimentally validated considering different set-points. Obtained results let us know that our plant-controller is stable and responds satisfactorily to position disturbances around a reference point.

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“…Los sistemas dinámicos basados en una bola rodante sobre una guía [10,15] y su extensión a la bola rodante sobre un plano [7,11,12,16] son fáciles de implementar y dan a los estudiantes e investigadores la oportunidad de ensayar diferentes estrategias de control. En ambos casos el desafío consiste en estabilizar la bola ya sea en un punto de la guía o del plano, lo cual implica que la posición de la bola sea conocida en todo momento por medio de sensores, para así modificar la posición y orientación de la guía o del plano por medio de actuadores [1,9,13].…”

Section: Introductionunclassified