Obstacle avoidance and model‐free tracking control for home automation using bio‐inspired approach

Khan, Ameer Tamoor; Li, Shuai; Zhan, Lihua

doi:10.1002/adc2.63

Cited by 35 publications

(11 citation statements)

References 49 publications

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“…BAS is a single particle searching algorithm, where the particle optimizes an objective function by searching the search space iteratively. The utility of BAS has expanded to several real-world problems [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ], including the portfolio optimization. Ref.…”

Section: Introductionmentioning

confidence: 99%

Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem

et al. 2022

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The recently emerging multi-portfolio selection problem lacks a proper framework to ensure that client privacy and database secrecy remain intact. Since privacy is of major concern these days, in this paper, we propose a variant of Beetle Antennae Search (BAS) known as Distributed Beetle Antennae Search (DBAS) to optimize multi-portfolio selection problems without violating the privacy of individual portfolios. DBAS is a swarm-based optimization algorithm that solely shares the gradients of portfolios among the swarm without sharing private data or portfolio stock information. DBAS is a hybrid framework, and it inherits the swarm-like nature of the Particle Swarm Optimization (PSO) algorithm with the BAS updating criteria. It ensures a robust and fast optimization of the multi-portfolio selection problem whilst keeping the privacy and secrecy of each portfolio intact. Since multi-portfolio selection problems are a recent direction for the field, no work has been done concerning the privacy of the database nor the privacy of stock information of individual portfolios. To test the robustness of DBAS, simulations were conducted consisting of four categories of multi-portfolio problems, where in each category, three portfolios were selected. To achieve this, 200 days worth of real-world stock data were utilized from 25 NASDAQ stock companies. The simulation results prove that DBAS not only ensures portfolio privacy but is also efficient and robust in selecting optimal portfolios.

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Section: Introductionmentioning

confidence: 99%

Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem

et al. 2022

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“…Despite of many proposed solutions and their eminent advantages, advanced discoveries reveal that these methods have several inherent weaknesses [11] [12]. Main hurdles remain computational complexity, adaptability, time constraint, and convergence on local optima [13]. Since obstacle avoidance is quite challenging task, many approaches are proposed by different researchers to solve this problem [14].…”

Section: Introductionmentioning

confidence: 99%

“…BAS is a metaheuristic optimization algorithm inspired by the food search behavior of beetles [54]. There are several advantages of using BAS over other previously mentioned algorithms such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) [13].…”

Section: Introductionmentioning

confidence: 99%

Bio-inspired BAS: Run-time Path-planning And The Control of Differential Mobile Robot

Ijaz

Khan

2022

EAI Endorsed Trans AI Robotics

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Trajectory tracking and obstacle avoidance lies at the heart of autonomous navigation for mobile robots. In this paper, a control architecture for trajectory tracking while avoiding obstacles and controller tuning is proposed for a differential drive mobile robot (DMR). The framework of optimization algorithm is inspired by the food search behavior of beetles using their antennae. Path planning and controller tuning remain computationally demanding tasks despite of the proposed algorithms existing today. Our bio inspired approach unifies these two problems by minimizing the respective cost functions and solving the optimization problem efficiently. Trajectory tracking problem is based on the difference of the current and next pose of the robot while obstacle avoidance is achieved on the principle of maximizing the minimum distance between the robot and obstacle in the path of the robot. The proposed architecture is simulated in V-REP environment using MATLAB. Simulation results have verified that beetle antennae search can successfully plan and track the reference path by tuning the PID controller efficiently.

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“…Moreover, for the purpose of showing its advantage in accuracy, practicability and convergence, in that paper, two different path-tracking cases were carried out by comparing to the original ZD controller. In another paper, for obstacle avoidance and tracking reference trajectory for the robot manipulator, zeroing neural network based on the concept of the ZD was proposed by Khan et al (2021) and designed with beetle antennae search algorithm. In that paper, in simulation results by using two different trajectories, the proposed control algorithm was able to achieve the tracking reference trajectories as avoiding the obstacle successfully and accurately.…”

Section: Introductionmentioning

confidence: 99%

Real-time trajectory tracking control of Stewart platform using fractional order fuzzy PID controller optimized by particle swarm algorithm

Bingül

Karahan

2021

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Purpose The purpose of this paper is to address a fractional order fuzzy PID (FOFPID) control approach for solving the problem of enhancing high precision tracking performance and robustness against to different reference trajectories of a 6-DOF Stewart Platform (SP) in joint space. Design/methodology/approach For the optimal design of the proposed control approach, tuning of the controller parameters including membership functions and input-output scaling factors along with the fractional order rate of error and fractional order integral of control signal is tuned with off-line by using particle swarm optimization (PSO) algorithm. For achieving this off-line optimization in the simulation environment, very accurate dynamic model of SP which has more complicated dynamical characteristics is required. Therefore, the coupling dynamic model of multi-rigid-body system is developed by Lagrange-Euler approach. For completeness, the mathematical model of the actuators is established and integrated with the dynamic model of SP mechanical system to state electromechanical coupling dynamic model. To study the validness of the proposed FOFPID controller, using this accurate dynamic model of the SP, other published control approaches such as the PID control, FOPID control and fuzzy PID control are also optimized with PSO in simulation environment. To compare trajectory tracking performance and effectiveness of the tuned controllers, the real time validation trajectory tracking experiments are conducted using the experimental setup of the SP by applying the optimum parameters of the controllers. The credibility of the results obtained with the controllers tuned in simulation environment is examined using statistical analysis. Findings The experimental results clearly demonstrate that the proposed optimal FOFPID controller can improve the control performance and reduce reference trajectory tracking errors of the SP. Also, the proposed PSO optimized FOFPID control strategy outperforms other control schemes in terms of the different difficulty levels of the given trajectories. Originality/value To the best of the authors’ knowledge, such a motion controller incorporating the fractional order approach to the fuzzy is first time applied in trajectory tracking control of SP.

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Obstacle avoidance and model‐free tracking control for home automation using bio‐inspired approach

Cited by 35 publications

References 49 publications

Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem

Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem

Bio-inspired BAS: Run-time Path-planning And The Control of Differential Mobile Robot

Real-time trajectory tracking control of Stewart platform using fractional order fuzzy PID controller optimized by particle swarm algorithm

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