Adaptive Fuzzy Output Feedback Fault-Tolerant Control for Active Suspension Systems

Fixed‐time distributed adaptive optimization for third‐order nonlinear fully heterogeneous vehicular platoon systems

“…The system parameters are given as: 𝛾 𝜈,i = (2, 2, 3, 4), i = 1, 2, 3, 4, 𝜉 T 𝜈,i = 0.32, 𝜉 T a,i = 0.25, and set a number of initial system values: x 𝜌,1 (0) = 0, x 𝜈,1 (0) = 1, x a,1 (0) = 0, x 𝜌,2 (0) = 1, x 𝜈,2 (0) = 1, x a,2 (0) = 1, x 𝜌,3 (0) = 3, x 𝜈,3 (0) = 0.1, x a,3 (0) = 3, x 𝜌,4 (0) = 3, x 𝜈,4 (0) = 0, x a,4 (0) = 3. ( η𝜌,1 (0), η𝜌,2 (0), η𝜌,3 (0), η𝜌,4 (0)) = (2, 3, 5, 4), ( η𝜈,1 (0), η𝜈,2 (0), η𝜈,3 (0), η𝜈,4 (0)) = (3,4,8,6), ( ηa,1 (0), ηa,2 (0), ηa,3 (0), ηa,4 (0)) = (3,2,4,5). 𝜔 1 (0) = 1, 𝜔 2 (0) = 0.5, 𝜔 3 (0) = 2, 𝜔 4 (0) = 0.3.…”

“…Plenty of crucial works have been obtained for the vehicular platoon systems. [1][2][3][4][5][6][7][8][9][10][11][12][13] However, these results focused on tracking control. In this article, a new optimization algorithm based two-step design is constructed for three-order CAVs with unknown nonlinearities to enhance the stability performance of the entire system.…”

“…Vehicular platoon control has garnered significant attention among academics as a crucial research topic in the field of cooperative control since it has anti-interference characteristics and string stability. Recently, multitudes of excellent research results have emerged, [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] and various strategies have been employed. For instance, a leader-follower control strategy was presented based on the techniques of neural networks and backstepping, 1 where the formation control problem of autonomous vehicles was addressed.…”

“…For instance, a leader-follower control strategy was presented based on the techniques of neural networks and backstepping, 1 where the formation control problem of autonomous vehicles was addressed. From the aspect of safe driving, the problems of distance constraints and actuator faults [2][3][4][16][17][18] have been taken into account. By combining with sliding-mode control method, a finite-time formation control was studied, which guaranteed the tracking error of the formation converges in a finite time.…”

Fault diagnosis of virtually‐coupled trains by adaptive observer with pattern‐matched detection and reinforced identification

“…It is clearly observed that the virtual coupling in railway control system brought about widespread attention from both theoretical researches and practical applications in an ideal situation, that is, no abnormal sources are considered, such as faults and failures of components or subsystems equipped on complicated VC train control systems, which is actually excessively ideal condition for practical situation, because of that potentially encountered abnormal behaviors, though not large probability in real world, such as speed sensing drift, positioning deviation, partial traction engine failures for powered distributed trains, 19 will bring about serious consequences in turn and may cause collisions among trains with theoretically minimum separation distances in VC mode without proper handling and disposing. Hence, it is quite desired that fault diagnosis method is designed for VC trains using advanced technique and learning‐based identification algorithms 20–27 . For fault tolerant control of trains, some significant efforts have been made.…”