Optimization Algorithms in Control Systems

Blondin, Maude Josée

doi:10.1007/978-3-030-64541-0_1

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…The increase in dimension of the AVs system as long as the increase in tasks makes it a more complicated optimization issue that requires the utilization of AI techniques. These techniques are utilized widely in various fields for planning and control 42 . The tuned controllers based on AI techniques have proved sufficient and superior performance compared with the conventional methods.…”

Section: Ai‐based Optimalitymentioning

confidence: 99%

“…These techniques are utilized widely in various fields for planning and control. 42 The tuned controllers based on AI techniques have proved sufficient and superior performance compared with the conventional methods. 43 The fact of a large number of adjusting parameters and trapping in local optima are main challenges that against the utilization of AI techniques.…”

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confidence: 99%

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Optimal design of low computational burden model predictive control based on SSDA towards autonomous vehicle under vision dynamics

Elsisi

Ebrahim

2021

Int J of Intelligent Sys

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The adjusting of autonomous vehicles (AVs) steering angle represents a challenging issue in the intelligent vehicular applications. Among different control strategies, the model predictive control (MPC) is devoted to providing efficacious steering control for the AVs. However, the formulation of MPC with a few parameters is the main challenge to overcome the complicated computation that happens due to the long prediction and control horizons. Hybridization between discrete‐time Laguerre function (DTLF) and MPC is suggested to formulate the MPC with few parameters. While the hybrid DTLF–MPC requires an adequate tuning technique for their gains to improve the system performance. A new low computational burden design is proposed in this paper to adjust the gains of the hybrid DTLF–MPC based on the social ski driver algorithm (SSDA). This algorithm is developed as a recently artificial intelligence technique. The tuned hybrid DTLF–MPC by SSDA is devoted to control AV's steering angle. Furthermore, the proposed system takes into account the vision dynamics. The system performance based on the tuned DTLF–MPC by SSDA is evaluated with the MPC based on the neural network algorithm for comparison. Different scenarios are carried out to confirm the superiority of the suggested control mechanism to handle small as well as large‐scale variations of road curvatures.

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Section: Ai‐based Optimalitymentioning

confidence: 99%

mentioning

confidence: 99%

Optimal design of low computational burden model predictive control based on SSDA towards autonomous vehicle under vision dynamics

Elsisi

Ebrahim

2021

Int J of Intelligent Sys

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“…The nonlinear and hardly coupled differential equations of the robotic manipulator make the tuning of the controller more difficult. The OA provides an effective solution for this issue in various control applications for conventional methods [61,62]. The solution process of the OA ensures the determination of the optimal global solutions for complex optimization models [63][64][65][66][67][68][69].…”

Section: Optimization Algorithms-based Optimality 21 Overviewmentioning

confidence: 99%

Improvement of Trajectory Tracking by Robot Manipulator Based on a New Co-Operative Optimization Algorithm

et al. 2021

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The tracking of a predefined trajectory with less error, system-settling time, system, and overshoot is the main challenge with the robot-manipulator controller. In this regard, this paper introduces a new design for the robot-manipulator controller based on a recently developed algorithm named the butterfly optimization algorithm (BOA). The proposed BOA utilizes the neighboring butterflies’ co-operation by sharing their knowledge in order to tackle the issue of trapping at the local optima and enhance the global search. Furthermore, the BOA requires few adjustable parameters via other optimization algorithms for the optimal design of the robot-manipulator controller. The BOA is combined with a developed figure of demerit fitness function in order to improve the trajectory tracking, which is specified by the simultaneous minimization of the response steady-state error, settling time, and overshoot by the robot manipulator. Various test scenarios are created to confirm the performance of the BOA-based robot manipulator to track different trajectories, including linear and nonlinear manners. Besides, the proposed algorithm can provide a maximum overshoot and settling time of less than 1.8101% and 0.1138 s, respectively, for the robot’s response compared to other optimization algorithms in the literature. The results emphasize the capability of the BOA-based robot manipulator to provide the best performance compared to the other techniques.

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Intelligent Self-tuning Control Design for Wastewater Treatment Plant Based on PID and Model Predictive Methods

Ujjwal

Verma

Jain

2023

Advanced Technologies and Societal Change

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Optimization Algorithms in Control Systems

Cited by 4 publications

References 24 publications

Optimal design of low computational burden model predictive control based on SSDA towards autonomous vehicle under vision dynamics

Optimal design of low computational burden model predictive control based on SSDA towards autonomous vehicle under vision dynamics

Improvement of Trajectory Tracking by Robot Manipulator Based on a New Co-Operative Optimization Algorithm

Intelligent Self-tuning Control Design for Wastewater Treatment Plant Based on PID and Model Predictive Methods

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