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 efficient and that disruptions are effectively eliminated. In robust control, the main concern of engineers is about improving safety, security, and convenience. With robust control, engineers can effectively deal with uncertainties that may mar controller design. In normal instances, robust control are made to perform optimally so long as some parameters and are found within a specific set. In the presence of bounded modeling errors, these systems can achieve robust performance.  During the years, several researches have been carried out to boost the robustness of the state-space robustness. In the past robust control was used along deterministic approaches. However, recently, there has been a fierce criticism of the approach on grounds that it is non-flexible and cannot be descriptive of uncertainties.  Ultimately, the targets and aims of robust control is to avoid disturbance. The main devices that are used include; automatic excavation and multiple manipulators. The remote control of robotics has since been significantly improved in their various uses in the environment.

Robust control happens to be a method that aims at bounding the available uncertainty instead of expressing it in a distribution manner. Following a bound of the uncertainty, it becomes possible to deliver results that are in line with the requirements of a system in all cases.  In robust control, some performance may end up being sacrificed to make sure that certain requirements of the system are met. However, this type of sacrifice is a common in cases involving robotic systems (Pothukuchi, Pothukuchi, Voulgaris & Torrellas, 2017).

Statement of Purpose

There is thus a need to research on robust control of systems as it is a chance to develop efficient control systemshelp 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. It is true that the available control systems are effective in the various roles where they are used but there is a need for more improvement of robust control.

There is a special concern for extreme operations that may occur in robust systems which are known to have safety implication. It is in such extremes that one may not be able to predict what happens to robust control which has a special application in this case. It is true that there have been a lot of research about robust control and through this; a various techniques have been developed. There have been a number of tools for use in the robotic industry but a number of issues to do with correctness of these tools especially when they are used to simplify system that are complex in nature. My enthusiasm with pure mathematics has been an inspiration to me and I have, once in a while been part of projects to build parts of robotics arm which was a programmable microcontroller supporting additional circuitry for driving the stepper motors for rotation. This is just one of the projects I have done. Others include a ball balancing beam. In my study days I began my research by co-authoring Blind De-convolution of blurred images with Fuzzy Size Detection of Point Spread Function which sought to find the point spread function dimension of an optical system. The kind of challenges that robust systems are marred with have inspired further research that is intended to make improvements on the current robust control method to make sure that improved processes and tools are in place as a good way to close or minimize the gap that exist between robust control theory and its varied application.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

References:

Pothukuchi, R. P., Pothukuchi, S. Y., Voulgaris, P., & Torrellas, J. (2017, September).

Multilayer Compute Resource Management with Robust Control Theory. In Parallel Architectures and Compilation Techniques (PACT), 2017 26th International Conference on (pp. 376-376). IEEE.

 

 

 

 

 

 

 

 

 

 

 

 

 

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