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Abstract

This work presents a method for controlling the travel routes that tracked robots use to get around their environments. The algorithm utilized in model-predictive control could be implemented in a vehicle to make the vehicle capable of driving itself if an emergency arises. It is anticipated that soon, collaboration between edge clouds will become more commonly used to connect and control various pieces of rescue equipment. The Linear Model-Predictive Control algorithm, also known as LMPC, is an excellent choice for controlling the path that the rescue robots will take because it makes efficient use of the limited computational resources available and delivers accurate results in real-time. This makes it an excellent choice for controlling the rescue robots' route. Despite this, the driving conditions are hazardous, and there are significant shifts in how the road is curved at various points in this scenario. The level of accuracy that can be achieved when tracking a route with significant curvature variations is limited because only linearized feed-forward data can be entered into the LMPC. This places a restriction on the level of accuracy that can be achieved. The authors of this study suggest utilizing a preview linear MPC as a means of locating a solution to this problem and a means of addressing it. The test controller has had a simulation loaded into it that was created in MATLAB with the assistance of Simulink. Despite the significant variations in the curvature of the route, performance regarding route tracking was improved by utilizing the suggested strategy. This was possible due to more accurate route tracking.

Keywords

Autonomous robots, MatLab simulation, Model prediction control, Path tracking control, Wireless networks

Subject Area

Mathematics

Article Type

Article

First Page

2675

Last Page

2681

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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