Interval type-II Takagi–Sugeno fuzzy-based strategy in control of autonomous systems

Abstract

The present research addresses a new control strategy with a focus on an interval type-II Takagi-Sugeno (T-S) fuzzy-based approach in the area of autonomous systems to handle a set of parameters under model uncertainties. In a word, the subject behind the research is to guarantee the desirable performance of a class of the autonomous space robot systems through the design of the aforementioned interval type-II T-S fuzzy-based control strategy, which can be considered based upon the moments of inertia, the center of mass, the profile of the thrust vector and the misalignments of the propellant engine to deal with mission operation plans, in finite burning time. Concerning the research contribution, there are no solid foundations to address the investigated strategy to deal with such a system in the literatures, and therefore the proposed three-axis fuzzy-based comprehensive control solution, in its unique form, can be of the novelty, as long as a wide range of the mission operation plans are supported. It is to address a set of parameters of the autonomous space robot systems under model uncertainties, in a synchronous manner, while the outcomes are researched in connection with the specifications of the system under control and subsequently to be analyzed with regard to a number of benchmarks to verify the acquired performance. The key core behind the proposed research is to present a solving problem through the new integration of system control for a specific space autonomous task under control, while highperformance results in correspondence with the state-of-the-art materials are acquired.

Enhancement of the Tracking Performance for Robot Manipulator by Using the Feed-forward Scheme and Reasonable Switching Mechanism

“…The KM approach mostly uses the type reduction method for type-2 FLS in order to compute the output. Later, many investigations have been introduced, such α-cut algorithm [22][23][24][25], to solve the problem of weighted value. In the other method [26,27], the KM approach was to generate a boundary function which was proved to be greater and more practicable.…”

“…In this controller, equation (24) describes the estimation of nonlinear functions 𝐹 ̂(𝑥, 𝛼 ̃𝑓) 𝐺 ̂(𝑥, 𝛼 ̃𝑔) in place of the real term of nonlinear function 𝐹(𝑥) and 𝐺(𝑥).…”