Extreme learning‐based non‐linear model predictive controller for an autonomous underwater vehicle: simulation and experimental results

Rath, Biranchi Narayan; Subudhi, Bidyadhar

doi:10.1049/iet-csr.2019.0014

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…Another controller is model-based predictive control (MPC) that is extensively used as an excellent option in the constrained, multivariable control problems. Two strategies are applicable in the non-linear MPC: a direct approach to solve the problem by the non-linear optimisation function [6][7][8][9], or the method using linear MPC for the linear model calculated by the feedback linearisation (FBL) technique, which uses the algebraic transformation of the non-linear system dynamics to gain an equivalent linear system [6,[10][11][12]. Besides employing the FBL technique, other linear controllers such as linear quadratic regulator (LQR) can also be applied to control the system [13].…”

Section: Introductionmentioning

confidence: 99%

Fault tolerant control in an unmanned bicycle robot via sliding mode theory

Alizadeh

Ramezani

Saadatinezhad

2021

IET Cyber-Syst and Robotics

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In this work, a new active fault tolerant control (FTC) is developed for an unmanned bicycle robot based on an integration between a sliding mode control (SMC), fault detection (FD), and fault estimation (FE) via a residual signal. A sliding surface in accordance with the fault tolerant sliding mode control (FTSMC) is designed for the bicycle robot to get multiple exciting features such as fast transient response with finite time convergence, small overshoot and quick stabilisation in the presence of an actuator fault. To obtain an effective performance for the FTSMC, a fault estimation system is employed and in order to attain estimation, an extended Kalman filter (EKF) as an estimator and a change detection algorithm called cumulative sum (CUSUM) as a residual evaluation function are developed. The innovative features of the proposed approach, that is FTSMC, are verified when compared with the other up-to-date control techniques like fault tolerant model-based predictive control with feedback linearisation (FTMPC + FBL) and fault tolerant linear quadratic regulator with feedback linearisation (FTLQR + FBL) on an unmanned bicycle robot. K E Y W O R D S cumulative sum, extended Kalman filter, fault tolerant control, feedback linearisation, sliding mode control, unmanned bicycle robot This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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