Quadrotor UAV attitude stabilization using fuzzy robust control

In this paper, a robust controller for attitude stabilization of a small quadrotor helicopter is developed. The TS (Takagi-Sugeno) fuzzy model approach and the [Formula: see text] robust control are combined to produce the proposed algorithm. Besides, disturbances and parametric uncertainties are considered. First, the nonlinear model of the vehicle is linearized around several operating points to obtain the representation of a TS fuzzy model, which represents the nonlinearity of the system dynamics. Then, a robust fuzzy controller is synthesized which guarantees desired control performances. The given controller is designed using numerical tools such as linear matrix inequalities (LMI). Finally, simulation results and real-time experiments are presented to validate the performance of the proposed scheme to robustly stabilize the quadrotor dynamics at the desired reference.

Discovery of Human Emotion using Fuzzy based Cognitive Model

The emotion of a human could be identified using Speech, Image and Question and answer session. Also, the emotion in speech is identified using the pitch and intensity. The emotion identification with image is done using Support Vector Machine. The present chapter envisages into an intelligent system, which is designed to understand human emotions more precisely speech emotion identification and intends to generate actions via cognitive system. It has mainly focused on developing an online incremental learning system of human emotions using Takagi-Sugeno (TS) Fuzzy model. The main objective of this system is to detect whether the observed emotion needs a new corresponding multi-model action to be generated or it can be attributed to one of the existing actions in memory. The multi-model consists of voice input, facial expression. The combined results have been classified using TS Fuzzy Model.

Parallel distribution compensation PID based on Takagi-Sugeno fuzzy model applied on egyptian load frequency control

This paper presents a new technique for a Takagi-Sugeno (TS) fuzzy parallels distribution compensation-PID'S (TSF-PDC-PID'S) to improve the performance of Egyptian load frequency control (ELFC). In this technique, the inputs to a TS Fuzzy model are the parameters of the change of operating points. The TS Fuzzy model can definite the suitable PID control for a certain operating point. The parameters of PID'S controllers are obtained by ant colony optimization (ACO) technique in each operating point based on an effective cost function. The system controlled by the proposed TSF-PDC-PID’S is investigated under different types of disturbances, uncertainty and parameters variations. The simulation results ensure that the TSF-PDC-PID'S can update the suitable PID controller at several operating points so, it has a good dynamic response under many types of disturbances compared to fixed Optimal PID controller.

Nonlinear control design using Takagi-Sugeno fuzzy applied to under-actuated visual servo system

This paper presents the Takagi-Sugeno (TS) fuzzy control design for nonlinear stabilization and tracking control of a ball on plate system. To deal with the plant nonlinearity and the fuzzy convergence issue, we formulate the parallel distributed compensator (PDC) TS fuzzy model to characterize the global behaviour of the nonlinear system and synthesize a feasible control framework using a velocity compensation scheme. The nonlinear dynamics of the ball on plate system is obtained using the Euler-Lagrangian energy based approach. To identify the moving objects in the video stream, a background subtraction algorithm using thresholding technique is formulated. Moreover, the stability analysis of the TS fuzzy control is reduced to linear matrix inequality (LMI) problem and solved using the Lyapunov direct method. The potential benefits of the proposed control structure for real time test cases are experimentally assessed using hardware in loop (HIL) testing on a ball on plate system. Experimental results substantiate that the TS fuzzy scheme can significantly improve not only the tracking performance but also the robustness of the closed loop system.

Robust Stabilization And Control Of Takagi-Sugeno Fuzzy Systems With Parameter Uncertainties And Disturbances Via State Feedback And Output Feedback

Most of the systems in the industry contain extreme non-linearity and uncertainties, which are hard to design and control utilizing general nonlinear systems. To conquer this sort of troubles, different plans have been produced in the most recent two decades, among which a popular methodology is Takagi-Sugeno fuzzy control. In this article, we present robust stabilization and control of Takagi-Sugeno (T-S) fuzzy systems with parameter uncertainties and disturbances. Initially, Takagi and Sugeno (TS) fuzzy model is used to represent a nonlinear system. Based on this T-S fuzzy model, fuzzy controller design schemes for state feedback and output feedback is also developed. Then, necessary conditions are derived for robust stabilization in the intelligence of Lyapunov asymptotic stability and are expressed in the arrangement of linear matrix inequalities (LMIs). The proposed system is implemented in the working platform of MATLAB and the simulation results are provided to illustrate the effectiveness of the proposed methods.

Fault estimation based on observer for chaotic Lorenz system with bifurcation problem

In this paper, the simultaneous estimation of the process and sensor fault of a chaotic Lorenz system in a noisy environment is investigated. The problem of the process fault leads to the occurrence of a bifurcation in the Lorenz system. The purpose of this article is to combine the concept of fault and bifurcation. Fault diagnosis of nonlinear systems becomes more practicable when it is managed over Takagi-Sugeno (TS) approximated fuzzy models. TS fuzzy model unknown input observer can estimate faults and states. In this respect, a TS fuzzy model augmented by a proportional plus integral, for fault modeling observer (FO) is lined up for the estimation of the unmeasured signal. The simulation conclusions hint that the observer runs well in estimating process fault, states, and sensor fault. Using these estimates, the deviation value of a parameter is determined from its actual value, which is the same as the low amount of deviation in this article because the bifurcation occurs in the system. In the first part of the simulation, the process fault occurs and drives system behavior into chaos, and the bifurcation diagram uses to explain it. In the second part, the system is influenced by actuator fault. The conclusion is validated through extensive simulations.