Trajectory tracking and obstacle avoidance of a ball and plate system using fuzzy theory

Rastin, M. Ali; Moosavian, S. Ali A.; Talebzadeh, Erfan; Alaeddin, Mojtaba

doi:10.1109/ifsc.2013.6675631

Cited by 9 publications

(3 citation statements)

References 5 publications

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“…For improving time domain response fuzzy PID and Reinforcement Learning (RL) controllers are investigated in [5]. There were various fuzzy controller proposed for motion control and trajectory tracking of ball and beam system in [6][7][8][9]. Moreover a fuzzy based adaptive integral control action had been proposed that significantly reduces the steady state error because of the integral control [10].…”

Section: Introductionmentioning

confidence: 99%

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Metaheuristic Optimization Algorithm Based Cascaded Control Schemes for Nonlinear Ball and Balancer System

Zafar,

Malik,

Ali

et al. 2024

Processes

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The ball and balancer system is a popular research platform for studying underactuated mechanical systems and developing control algorithms. It is a well-known two-dimensional balancing problem that has been addressed by a variety of controllers. This research work proposes two controllers that are proportional integral derivative-second derivative-proportional integrator (PIDD2-PI) controller and tilt integral derivative with filter (TID-F) controller in a multivariate, electromechanical, and nonlinear under-actuated ball and balancer system. Integral Time Absolute Error (ITAE) is an objective function used for designing controllers because of its ability to be more sensitive to overshooting as well as reduced settling time and steady-state error. As part of the analysis, four metaheuristic optimization algorithms are compared in the optimization of proposed control strategies for cascaded control of the ball and balancer system. The algorithms are the Grey Wolf optimization algorithm (GWO), Cuckoo Search algorithm (CSA), Gradient Base Optimization (GBO), and Whale Optimization Algorithm (WOA). The effectiveness of proposed controllers PIDD2-PI and TID-F is investigated to be better in terms of transient time response than proportional integral derivative (PID), proportional integral-derivative (PI-D), proportional integral-proportional derivative (PI-PD) and proportional integral derivative-second derivative-proportional derivative (PIDD2-PD). Moreover, these two proposed controllers have also been compared with recently published work. During the analysis, it is shown that the proposed control strategies exhibit significantly greater robustness and dynamic responsiveness compared to other structural controllers. The proposed controller WOA-PIDD2-PI reduced the 73.38% settling time and 88.16% rise time compared to classical PID. The other proposed controller GWO-TID-F reduced 58.06% the settling time and 26.96% rise time compared to classical PID. These results show that proposed controllers are particularly distinguished in terms of rise time, settling time, maximum overshoot, and set-point tracking.

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Section: Introductionmentioning

confidence: 99%

“…There are different methods to help with tuning these PID parameters. There are several examples of PID tuning [13,14] with ITAE [8,[15][16][17]. ITAE have strong emphasis on minimizing steady-state error and settling time.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic Optimization Algorithm Based Cascaded Control Schemes for Nonlinear Ball and Balancer System

Zafar,

Malik,

Ali

et al. 2024

Processes

View full text Add to dashboard Cite

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“…Other forms of research improvement on B&P system using expert controllers include fuzzy sliding mode controller (Negash & Singh, 2015), comparison between sliding mode control and FLC methods (Kasula et al, 2018). The researchers reported the use of dual-level FLCs (Rastin et al, 2013) to actualize obstacle avoidance and trajectory tracking.…”

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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Virtual Laboratory of the Ball and Plate System

et al. 2015

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Trajectory tracking and obstacle avoidance of a ball and plate system using fuzzy theory

Cited by 9 publications

References 5 publications

Metaheuristic Optimization Algorithm Based Cascaded Control Schemes for Nonlinear Ball and Balancer System

Metaheuristic Optimization Algorithm Based Cascaded Control Schemes for Nonlinear Ball and Balancer System

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

Virtual Laboratory of the Ball and Plate System

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