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

2019 ◽  
Vol 1 (2) ◽  
pp. 45-54
Author(s):  
Biranchi Narayan Rath ◽  
Bidyadhar Subudhi
Keyword(s):  
Linear Model ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Experimental Results ◽  
Model Predictive Controller ◽  
Vehicle Simulation ◽  
Non Linear ◽  
Predictive Controller

scholarly journals Stabilization of Furuta Pendulum Using Nonlinear MPC

2019 ◽  
Vol 27 (45) ◽  
pp. 42-48
Author(s):  
Matúš Furka ◽  
Martin Klaučo ◽  
Michal Kvasnica
Keyword(s):  
Mathematical Model ◽  
Linear Model ◽  
Model Predictive Controller ◽  
Motion Equations ◽  
Simulation Validation ◽  
Non Linear ◽  
Predictive Controller

Abstract This paper is devoted to design of a non-linear model predictive controller (NMPC), which will swing-up and stabilize an inverse rotary pendulum known as the Furuta Pendulum. This paper presents a simulation validation of the NMPC strategy using a full-fidelity non-linear mathematical model of the Furuta pendulum obtained from the Euler-Lagrange motion equations. The NMPC strategy was implemented in MATLAB using the MATMPC toolbox.

Embedding non-linear filtering in autonomous underwater vehicle dynamics via the Kolmogorov backward equation

2021 ◽  
pp. 014233122110196
Author(s):  
Ravish H. Hirpara ◽  
Shambhu N. Sharma
Keyword(s):  
State Vector ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Linear Filtering ◽  
Homogeneous Markov Process ◽  
Non Linear ◽  
Backward Equation ◽  
Noise Correction ◽  
Ito Process ◽  
Correction Terms

This paper revisits the state vector of an autonomous underwater vehicle (AUV) dynamics coupled with the underwater Markovian stochasticity in the ‘non-linear filtering’ context. The underwater stochasticity is attributed to atmospheric turbulence, planetary interactions, sea surface conditions and astronomical phenomena. In this paper, we adopt the Itô process, a homogeneous Markov process, to describe the AUV state vector evolution equation. This paper accounts for the process noise as well as observation noise correction terms by considering the underwater filtering model. The non-linear filtering of the paper is achieved using the Kolmogorov backward equation and the evolution of the conditional characteristic function. The non-linear filtering equation is the cornerstone formalism of stochastic optimal control systems. Most notably, this paper introduces the non-linear filtering theory into an underwater vehicle stochastic system by constructing a lemma and a theorem for the underwater vehicle stochastic differential equation that were not available in the literature.

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