This work is trying to introduce a fractional order floated pole Controller as a fast and robust approach. We designed a robust Variable Structure Control that yields a continuous and constrained Control signal, also a fast response in the presence of model uncertainties and external disturbances. In the proposed Controller, we employed the pole placement algorithm, then by designing proper polynomials gave it robust property, then due to a simple optimization routine, we make it fast and faster within the stability region. Finally, to evaluate the proposed method, numerical examples in different situations of the presence of noise, disturbance, and model uncertainties, also comparative results are presented. This paper proposed an accurate, fast, and robust Controller. This can improve the performance of the perturbed functional systems used in the industrial fields. It is proposed to spread the benefit of fractional calculus in the Control of complex systems in practical situations.

This paper presents a Variable Structure rule-based fuzzy Control for trajectory tracking and vibration Control of a flexible joint manipulator by using chaotic anti-Control. Based on Lyapunov stability theory for Variable Structure Control and fuzzy rules, the nonlinear Controller and some generic sufficient conditions for global asymptotic Control are attained. The fuzzy rules are directly constructed subject to a Lyapunov function obtained from Variable Structure surfaces such that the error dynamics of Control problem satisfy stability in the Lyapunov sense. Also in this study, the anti-Control is applied to reduce the deflection angle of flexible joint system. To achieve this goal, the chaos dynamic must be created in the flexible joint system. So, the flexible joint system has been synchronized to chaotic gyroscope system In this study, Control and anti-Control concepts are applied to achieve the high quality performance of flexible joint system. It is tried to design a Controller which is capable to satisfy the Control and antiControl aims. The performances of the proposed Control are examined in terms of input tracking capability, level of vibration reduction and time response specifications. Finally, the efficacy of the proposed method is validated through experimentation on QUANSER’s flexible-joint manipulator.

In this paper, we present a novel rough neural network Control system based on the Variable Structure Control developed for a class of SISO canonical nonlinear systems with taking the presence of bounded disturbance into account. We assume that the nonlinear functions of the system are completely unknown. The rough neural network presented here is used to approximate the unknown nonlinear functions to a desired appropriate approximation. A fuzzy soft switching Structure is developed to decide the amount of efforts taken by neural network and Variable Structure Control systems based upon the real-time error characteristics. A proper Lyapunov function is defined and used to deduce adaptive laws for tunable parameters of neural network and to achieve the closed loop stability of overall system. The rough family of neural networks have a reputation of better functionality at the presence of noise and disturbance, which comes from their interval characteristic of their parameters. In this study we utilize this property to achieve better performance. To demonstrate the effect of proposed Control Structure, it is applied upon three systems (one exemplary system, a dynamical, and a chaotic) and the simulated results have shown the efficiency of this hybrid Variable Structure Control scheme.