Control Systems and Robotics Engineering

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Reducing Uncertainties Using Robust Control

Introduction

There are many uncertainties that affect the functioning of control systems that are used in robotics.  It is for this reason that robust control is utilized to respond to these uncertainties to make sure that the functioning of the systems remains at the maximum and that disruptions are effectively eliminated. The robust control introduces new designs that are strategically meant to involve processes that will manage to overcome the uncertainties such that the control results are at the maximum. Most important about robust control is the use of robotic manipulators that function through an integration with kinematic redundancy (He, Chen & Yin, 2016).  Through the use of this technology, it becomes possible to add the degree to which operational freedom occurs and this makes it possible to attain full maneuverability. This also makes it possible to achieve more success in predictable remote operation in cases where join failures are involved. The main devices that are used in this case include; automatic excavation and multiple manipulators that end up being used in one object. This is a good introduction that has made it possible to improve remote control of robotics used in environments determined to be hazardous.

There are discrete event systems that are utilized a way of characterizing systems that are determined to be complex. Good examples of such systems include physical events that end up occurring at discrete events at specific times. It is in such events that optimization algorithms are known to produce quality results despite the uncertainties that are commonly encountered. Neural networks are utilized in designing the systems and devices used in robotic manipulators where they are combined with fuzzy inference. The success in this has so far introduced mathematical models meant for human immune systems to provide useful insights on how serum drug doses can be optimized with the aim of reducing the recovery time of patients.

Statement of Purpose

There is thus a need to research on robust control of systems as this is a chance to develop optimized systems to help solve the common problems that are being experienced in the robotics industry. The research will try to find out the available potential in robust control that can be manipulated to develop effective control systems that can be used in the varied environment especially those that are inhabitable by human beings. Success in adopting robust control in industrial processes is a good way to reduce the indicated operational costs as well as increasing the production levels. It is true that the available control systems are effective in the various roles where they are used but robust control is meant to add to their current success in terms of efficiency and productivity. Optimization of the systems means that the number of disruptions will be reduced and that the risk of operational accidents will be reduced. New designs to make changes to the available systems will only deliver their indicated outcomes after fully incorporation and utilization of robust controls. Systems are bound to be as efficient as possible and this depends on the available information of the suitable technologies that can be used to make it happen and thus the need for research on robust control of systems.

 

 

 

 

References:

He, W., Chen, Y., & Yin, Z. (2016). Adaptive neural network control of an uncertain robot with

full-state constraints. IEEE transactions on cybernetics, 46(3), 620-629.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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