Novel Framework for Navigation using Enhanced Fuzzy Approach with Sliding Mode Controller

The reliability of any embedded navigator in advanced vehicular system depends upon correct and precise information of navigational data captured and processed to offer trustworthy path. After reviewing the existing system, a significant trade-off is explored between the existing navigational system and present state of controller design on various case studies and applications. The existing design of controller system for navigation using error-prone GPS/INS data doesn‟t emphasize on sliding mode controller. Although, there has been good number of studies in sliding mode controller, it is less attempted to optimize the navigational performance of a vehicle. Therefore, this paper presents a novel optimized design of a sliding mode controller that can be effectively deployed on advanced navigational system. The study outcome was found to offer higher speed, optimal control signal, and lower error occurances to prove that proposed system offers reliable and optimized navigational services in contrast to existing system

Adaptive Neural-Fuzzy Sliding-Mode Fault-Tolerant Control for Uncertain Nonlinear Systems

This paper proposes an adaptive neural-fuzzy sliding-mode control method for uncertain nonlinear systems with actuator effectiveness faults and input saturation. The parameter dependence of the control scheme is removed from the bound of actuator faults by updating online. A neural-fuzzy model is developed to approximate the uncertain nonlinear terms and a sliding-mode online-updating controller is developed to estimate the bound of the actuator with no prior knowledge of the fault. The asymptotic stability is verified via the Lyapunov method in the presence of actuator faults and saturation. Furthermore, the adaptive neural-fuzzy control method is extended to the uncertain faulty nonlinear systems with integral sliding-mode manifold as well as other popular sliding-mode surfaces. A numerical example is presented to demonstrate the effectiveness of the derived results

Adaptive neural-fuzzy sliding-mode fault-tolerant control for uncertain nonlinear systems

This paper proposes an adaptive neural-fuzzy sliding-mode control method for uncertain nonlinear systems with actuator effectiveness faults and input saturation. The parameter dependence of the control scheme is removed from the bound of actuator faults by updating online. A neural-fuzzy model is developed to approximate the uncertain nonlinear terms and a sliding-mode online-updating controller is developed to estimate the bound of the actuator with no prior knowledge of the fault. The asymptotic stability is verified via the Lyapunov method in the presence of actuator faults and saturation. Furthermore, the adaptive neural-fuzzy control method is extended to the uncertain faulty nonlinear systems with integral sliding-mode manifold as well as other popular sliding-mode surfaces. A numerical example is presented to demonstrate the effectiveness of the derived results