Real-time obstacle avoidance of mobile robots using state-dependent Riccati equation approach

Rostami, Seyyed Mohammad Hosseini; Sangaiah, Arun Kumar; Wang, Jin; Kim, Hye-Jin

doi:10.1186/s13640-018-0319-1

Cited by 23 publications

(29 citation statements)

References 11 publications

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“…Simulation results show the effectiveness, feasibility, and robustness of the ISMC method. Other robust methods have also been used for uncertainty removal [38][39][40]. Energy replenish method for robots is provided in [41,42].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Leader-Follower Formation Control of Under-actuated Surface Vessels with Model Uncertainties and Input Constraints

2019

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This paper deals with the leader-follower formation control of underactuated autonomous surface vehicles in the presence of model uncertainties and input constraints. In a leader-follower formation, an autonomous surface vehicle (ASV) called leader tracks a pre-described trajectory and other ASVs called followers that are controlled to follow the leader with a desired distance and desired relative bearing. To this end, some adaptive robust techniques are adopted to guarantee the robustness of the closed-loop system against model uncertainties, external disturbances, and input saturation constraints. Based on the Lyapunov synthesis, it is proven that with the developed formation controllers, the closed-loop system is stable and all the formation errors converge to a small neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Adaptive Leader-Follower Formation Control of Under-actuated Surface Vessels with Model Uncertainties and Input Constraints

2019

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“…Researchers around the world have been researching about artificial potential field defects and have presented suggestions for improving this method [25][26][27][28][29][30][31][32][33][34][35][36][37]. Here, a regulative agent has been added to improve the artificial potential field algorithm with the target of overcoming the minimum local and inaccessible target.…”

Section: Algorithm Designmentioning

confidence: 99%

Obstacle avoidance of mobile robots using modified artificial potential field algorithm

Rostami

Sangaiah

Wang

et al. 2019

J Wireless Com Network

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171

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In recent years, topics related to robotics have become one of the researching fields. In the meantime, intelligent mobile robots have great acceptance, but the control and navigation of these devices are very difficult, and the lack of dealing with fixed obstacles and avoiding them, due to safe and secure routing, is the basic requirement of these systems. In this paper, the modified artificial potential field (APF) method is proposed for that robot avoids collision with fixed obstacles and reaches the target in an optimal path; using this algorithm, the robot can run to the target in optimal environments without any problems by avoiding obstacles, and also using this algorithm, unlike the APF algorithm, the robot does not get stuck in the local minimum. We are looking for an appropriate cost function, with restrictions that we have, and the goal is to avoid obstacles, achieve the target, and do not stop the robot in local minimum. The previous method, APF algorithm, has advantages, such as the use of a simple math model, which is easy to understand and implement. However, this algorithm has many drawbacks; the major drawback of this problem is at the local minimum and the inaccessibility of the target when the obstacles are in the vicinity of the target. Therefore, in order to obtain a better result and to improve the shortcomings of the APF algorithm, this algorithm needs to be improved. Here, the obstacle avoidance planning algorithm is proposed based on the improvement of the artificial potential field algorithm to solve this local minimum problem. In the end, simulation results are evaluated using MATLAB software. The simulation results show that the proposed method is superior to the existing solution.

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“…In the present paper, a model predictive control (MPC) method has been used for controlling temperature and humidity which was previously only introduced for controlling temperature in [28][29][30]. Furthermore, different techniques have been employed to reduce the cost function [31][32][33][34][35]. On the one hand, creating an optimized space for a MPC design problem to simultaneously monitor temperature and humidity as two effective factors in providing comfortable atmosphere for residents is the key idea.…”

Section: Related Workmentioning

confidence: 99%

Modeling and Controlling of Temperature and Humidity in Building Heating, Ventilating, and Air Conditioning System Using Model Predictive Control

et al. 2019

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Nowadays, by huge improvements in industrial control and the necessity of efficient energy consumption for buildings, unified managing systems are established to monitor and control mechanical equipment and energy usage. One of the main portions of the building management system (BMS) is the cooling and heating equipment called heating and ventilation and air-conditioning (HVAC). Based on temperature slow dynamic and presented uncertainty in modeling, a model predictive control (MPC) strategy to track both temperature and humidity is proposed in this study. The main goal of this study is to provide a framework to describe temperature and humidity elements required for dynamic modeling. Following that, by utilizing a predictive approach, a control strategy is obtained, which optimizes the tracking error of two interactional channel and performs the effort control by minimizing the optimization index. Other articles have mostly only had control over the temperature variable, but in our article, we tried to study the equations of temperature and humidity as well as their interference and according to the ASHRAE standard, both temperature and humidity controls must be accurate. The humidity was the novelty in our article. Simulation results proved the effectiveness of the proposed approach compared to the conventional proportional-integral controller. Evidently, the key idea behind the control objective is providing the comfort condition while consuming the least possible energy.

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Real-time obstacle avoidance of mobile robots using state-dependent Riccati equation approach

Cited by 23 publications

References 11 publications

Adaptive Leader-Follower Formation Control of Under-actuated Surface Vessels with Model Uncertainties and Input Constraints

Adaptive Leader-Follower Formation Control of Under-actuated Surface Vessels with Model Uncertainties and Input Constraints

Obstacle avoidance of mobile robots using modified artificial potential field algorithm

Modeling and Controlling of Temperature and Humidity in Building Heating, Ventilating, and Air Conditioning System Using Model Predictive Control

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