In this paper, the optimal position control of an underactuated robotic finger is presented. Two trajectories, one for proximal and other medial phalanx, are proposed in order to emulate finger’s flexion/extension movements. A Mandani fuzzy due lack a precise dynamical model system. obtain parameters, optimization strategy based on membership functions applied. Genetic algorithms (GA) commonly ...

In this research paper we have presented control architecture for robotic arm movement and trajectory planning using Fuzzy Logic (FL) and Genetic Algorithms (GAs). This architecture is used to compensate the uncertainties like; movement, friction and settling time in robotic arm movement. The genetic algorithms and fuzzy logic is used to meet the objective of optimal control movement of robotic...

Tracking Control of Underactuated Surface Vessels Using Hybrid Control Methods Yu Tian Advisor: University of Guelph, 2016 Prof. Simon X. Yang In this thesis, two intelligent control systems are developed for tracking control of an underactuated surface vessel (USV). The studied USV is controlled with uncertain parameters, such as the disturbances from the ocean current. In the first part of th...

In this project we would try to design a fuzzy feedback control system for stock trading systems. The goal of this project is to tune parameters of the control system with fuzzy logic. This is a famous application of fuzzy logic into control engineering (for example see [5] for examples of tuning PID controllers with fuzzy logic). The output of the controller (control law, control input) is the...

Power system transient stability is one of the most challenging technical areas in electric power industry. Thyristorcontrolled series compensation (TCSC) is expected to improve transient stability and damp power oscillations. TCSC control in power system transients is a nonlinear control problem. This paper presents a T–S-model-based fuzzy control scheme and a systematic design method for the ...

Aneural-learning fuzzy technique is proposed for T–S fuzzy-model identification ofmodel-free physical systems. Further, an algorithm with a defined modelling index is proposed to integrate and to guarantee that the proposed neural-based optimal fuzzy controller can stabilize physical systems; the modelling index is defined to denote the modelling-error evolution, and to ensure that the training...

In this paper, we talk about the application of fuzzy control techniques to the nonlinear systems. We propose a new scheme of fuzzy optimal control techniques in which T-S (Takagi-Sugeno) fuzzy model is used for the modeling. We adopt the PDC (parallel distributed control) as our controller which is also modeled by fuzzy rules. Both the discrete system case and the continuous system case are di...

Abstract A novel adaptive fuzzy controller for a class of uncertain multivariable nonlinear systems with time delays is proposed in this paper. By combining fuzzy Takagi-Sugeno (T-S) models and fuzzy logic systems, the modeling error and the uncertainties are not required to satisfy the constraint conditions. In the meantime, fuzzy T-S models are used to approximate the nonlinear systems and fu...

In this paper, we develop a hybrid state-space fuzzy model-based controller with dual-rate sampling for digital control of chaotic systems. Takagi–Sugeno (TS) fuzzy model is used to model the chaotic dynamic system and the extended paralleldistributed compensation technique is proposed and formulated for designing the fuzzy model-based controller under stability conditions. The optimal regional...