High performance fuzzy-Padé controllers: Introduction and comparison to fuzzy controllers

Abedinnasab, Mohammad H.; Yoon, Yong‐Jin; Saeedi-Hosseiny, Marzieh S.

doi:10.1007/s11071-012-0647-0

Cited by 10 publications

(7 citation statements)

References 40 publications

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“…In the INS/GPS system, the measurement vector z k can be expressed as the difference of the measurement between the INS and GPS in equation (25) Figure 2). The GPS device processes the PVT information in standard National Marine Electronics Association (NMEA) signals 19 which will then be processed into geodetic location and velocity-over-ground.…”

Section: Ins/gps Integration Architecturementioning

confidence: 99%

Real-time precision pedestrian navigation solution using Inertial Navigation System and Global Positioning System

Yoon

Lee

et al. 2015

Advances in Mechanical Engineering

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Global Positioning System and Inertial Navigation System can be used to determine position and velocity. A Global Positioning System module is able to accurately determine position without sensor drift, but its usage is limited in heavily urbanized environments and heavy vegetation. While high-cost tactical-grade Inertial Navigation System can determine position accurately, low-cost micro-electro-mechanical system Inertial Navigation System sensors are plagued by significant errors. Global Positioning System is coupled with Inertial Navigation System to correct the errors, while Inertial Navigation System itself can be used to provide navigation solution during a Global Positioning System outage. Data from Global Positioning System and Inertial Navigation System can be integrated by extensive Kalman filtering, using loosely coupled integration architecture to provide navigation solutions. In this study, real-time low-cost loosely coupled micro-electro-mechanical system Inertial Navigation System/Global Positioning System sensors have been used for pedestrian navigation. Trial runs of Global Positioning System outages have been conducted to determine the accuracy of the system described. The micro-electro-mechanical system Inertial Navigation System/Global Positioning System can successfully project a trajectory during a Global Positioning System outage and produces a root mean square error of 9.35 m in latitude direction and 10.8 m in longitude direction. This technology is very suitable for visually impaired pedestrians.

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Section: Ins/gps Integration Architecturementioning

confidence: 99%

Real-time precision pedestrian navigation solution using Inertial Navigation System and Global Positioning System

Yoon

Lee

et al. 2015

Advances in Mechanical Engineering

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“…1 The inequities in (14) are referred to in the sequel as assumption A1. propose v s as a discontinuous injection based on the twisting control algorithm [28,[34][35][36] .That is,…”

Section: Control Of the Cart Pole Systemmentioning

confidence: 99%

“…This system was originally used as a benchmark for educational purposes, see [1,6,15,33,39,43,44,46] .Through the years, this system has attracted attention as an important underactuated mechanical system, because the pendulum angular acceleration cannot be directly controlled [18,42]. As the CPS dynamics resembles that of many underactuated robot systems, it has been studied as a simplified model such systems (see [7,27,30,38,40,43] ).…”

Section: Introductionmentioning

confidence: 99%

Stabilization of the cart pole system: by sliding mode control

Aguilar-Ibáñez¹,

Mendoza-Mendoza²,

Dávila³

2014

Nonlinear Dyn

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This paper presents a control strategy designed as a combination of a PD controller and a twistinglike algorithm to stabilize the damped cart pole system, provided that the pendulum is initially placed within the upper-half plane. To develop the strategy, the original system is transformed into a four-order chain of integrator form, where the damping force is included through an additional nonlinear perturbation. The strategy consists of simultaneously bringing the position and velocity of the pendulum to within a compact region by applying the PD controller. Meanwhile, the system state variables are brought to the origin by the twisting-like algorithm. The corresponding convergence analysis is done using several Lyapunov functions. The control strategy is illustrated with numerical simulations.

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“…To improve the performance of FLCs, they are usually combined with other control approaches. Incorporation of them with linear control methods such as P–integral (PI), PD, or PID controllers leads to a considerable improvement in the performance of the system [22]. PI‐type FLCs are known to possess satisfactory performance for first‐order processes.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, they do not require extensive modelling of the system. Moreover, they are considerably fast and straightforward which can easily be implemented in empirical applications where FLCs are not applicable [22].…”

Section: Introductionmentioning

confidence: 99%