Saturated observer‐based adaptive neural network leader‐following control of N tractors with n‐trailers with a guaranteed performance

Abstract

This article addresses the leader‐following neural network adaptive observer‐based control of N tractors connected to n trailers with the prescribed performance specifications. To propose the controller, a change of coordinates and a nonlinear error transformation are used to transform the constrained error dynamics to a new second‐order Euler‐Lagrange unconstrained error dynamics which inherits all structural properties of ith vehicle dynamic model. By combining a projection‐type neural network and an adaptive robust technique, a novel leader‐following saturated output‐feedback controller is proposed to force that ith vehicle tracks a virtual leader trajectory with the prescribed transient and steady‐state characteristics while reducing the actuator saturation risk and compensating all unknown dynamic model parameters, external disturbances, unmolded dynamics, and NN approximation errors. A saturated velocity observer is heuristically proposed to obviate the requirement for the velocity measurements of ith vehicle without any unwanted peaking. A Lyapunov‐based stability analysis is utilized to prove that all the tracking and state observation errors are semi‐globally uniformly ultimately bounded (SGUUB) and they converge to small bounds including the origin with a prescribed performance. At the end, computer simulations will be shown to validate the efficacy of the proposed controller in practice.

Multi-machine collaboration realization conditions and precise and efficient production mode of intelligent agricultural machinery

“…Burgard et al [13] proposed a sliding mode control method for the master-slave co-operating tractor, which can be used to asymptotically stabilize the following vehicle to the desired path and realize the stable distance and deflection Angle between the slave and the main engine. Shojad et al [14] designed an adaptive neural network leading tracking control for multiple tractors based on saturation observer.…”

A Novel Voice Feature AVA and its Application to the Pathological Voice Detection Through Machine Learning

“…Among these, the most common include Arabic Voice Pathology Database (AVPD) [7], Saarbruecken Voice Database (SVD) [8] and the Massachusetts Eye and Ear Infirmary Database (MEEI) [9]. Upon surveying the literature about the pathological voice detection, researchers have used varied voice features and diverse machine learning classifiers for discriminating between the pathological and healthy voice signals [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21]. The work [10] wrote a robust voice pathology detection algorithm using the theory of Deep Learning.…”