Grey Wolf Optimizer-Based Approaches to Path Planning and Fuzzy Logic-based Tracking Control for Mobile Robots

Precup, Radu‐Emil; Voisan, Emil-Ioan; Petriu, Emil M.; Tomescu, Marius-Lucian; David, Radu-Codruţ; Szedlak-Stînean, Alexandra-Iulia; Roman, Raul‐Cristian

doi:10.15837/ijccc.2020.3.3844

Cited by 35 publications

(12 citation statements)

References 61 publications

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“…Purcaru et al [25] developed a new Gravitational Search Algorithm (GSA)-based model to generate optimal paths for a robot that travels in partly-unidentified environment under the presence of many barriers. Precup et al [26] developed Grey Wolf Optimizer (GWO) algorithm for path planning (PaPl) and Proportional-Integral (PI)-fuzzy controller tuning problems. Gao et al [27] analyzed the earning prediction of blockchain financial products on the basis of Particle Swarm Optimization algorithm.…”

Section: Related Workmentioning

confidence: 99%

An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products

et al. 2020

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The booming applications of bitcoin Blockchain technologies made investors concerned about the return and risk of financial products. So, the return rate of bitcoin must be foreseen in prior. This research article devises an effective return rate prediction technique for Blockchain financial products based on Optimal Least Square Support Vector Machine (OLS-SVM) model. The parameter optimization of the LS-SVM model was performed using hybridization of Grey Wolf Optimization (GWO) with Differential Evolution (DE), called optimal GWO (OGWO) algorithm. The hybridization process is performed to eliminate the local optima problem of GWO and enhance the diversity of the population. To verify the goodness of the proposed model, the Ethereum (ETH) return rate was chosen as the target and experimental analysis was performed on it to verify the predictive results on the time series. The experimental outcome was analyzed in terms of two performance measures namely Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The obtained simulation outcome infers that the OLS-SVM model yielded better predictive outcome of the return rate of financial products.

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Section: Related Workmentioning

confidence: 99%

An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products

et al. 2020

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“…In the Fig (2), the inner loop is modified with FO-IMC controller for disturbance rejection whereas, the FOPI-FOPD controller designed in the primary loop is used for stabilizing the system so that the desired performances can be obtained [54]. The primary loop FOPI-FOPD controller is designed using Fractional order calculus like the secondary FO-IMC controller calculations.…”

Section: B Fopi-fopd Based Modification Of Primary Loopmentioning

confidence: 99%

Modified Cascade Controller Design for Unstable Processes With Large Dead Time

Chandran

Murugesan

Gurusamy³

et al. 2020

IEEE Access

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In this paper, a simple controller design with the fractional order calculations applies to unstable cascade processes with a significant time delay. Series cascade control system consists of two loops in which the inner loop is designed using the IMC principle based on the synthesis method. The primary controller is designed with the FOPI-FOPD controller to ensure stable and satisfactory closed-loop performances for the unstable processes. For the primary controller, five tuning parameters are involved, which tuned using the centroid of the convex stability region method. Different examples are given to illustrate the superiority of the proposed design over some existing designs. Results obtained from simulation reveals that with the proposed control design, enhanced closed-loop control performances are obtained for nominal and perturbed conditions.

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“…The most common types of designed controller in mobile robot are divided into non-optimization [24][25][26][27] and optimization [28][29][30] approaches. Turennout et al [24] proposed the robot-follow-the wall and discussed its control problem characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…This method controls the direction and movement speed of the mobile robot when performing wall-tracking tasks [29]. Grey wolf optimizer-based approaches were proposed for the optimal path planning and optimal tuning of tracking fuzzy controllers for nonholonomic wheeled mobile robots by Precup et al [30].…”

Section: Introductionmentioning

confidence: 99%

Mobile Robot Wall-Following Control Using Fuzzy Logic Controller with Improved Differential Search and Reinforcement Learning

2020

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In this study, a fuzzy logic controller with the reinforcement improved differential search algorithm (FLC_R-IDS) is proposed for solving a mobile robot wall-following control problem. This study uses the reward and punishment mechanisms of reinforcement learning to train the mobile robot wall-following control. The proposed improved differential search algorithm uses parameter adaptation to adjust the control parameters. To improve the exploration of the algorithm, a change in the number of superorganisms is required as it involves a stopover site. This study uses reinforcement learning to guide the behavior of the robot. When the mobile robot satisfies three reward conditions, it gets reward +1. The accumulated reward value is used to evaluate the controller and to replace the next controller training. Experimental results show that, compared with the traditional differential search algorithm and the chaos differential search algorithm, the average error value of the proposed FLC_R-IDS in the three experimental environments is reduced by 12.44%, 22.54% and 25.98%, respectively. Final, the experimental results also show that the real mobile robot using the proposed method can effectively implement the wall-following control.

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Grey Wolf Optimizer-Based Approaches to Path Planning and Fuzzy Logic-based Tracking Control for Mobile Robots

Cited by 35 publications

References 61 publications

An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products

An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products

Modified Cascade Controller Design for Unstable Processes With Large Dead Time

Mobile Robot Wall-Following Control Using Fuzzy Logic Controller with Improved Differential Search and Reinforcement Learning

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