Integrated vehicle control using adaptive control allocation

Abstract

Summary The focus of this paper is an integrated, fault‐tolerant vehicle control algorithm for the overall stability of ground vehicles. The proposed scheme comprises a high‐level controller that creates a virtual control input and a low‐level adaptive control allocator that distributes the virtual control effort among redundant actuators. The proposed control framework distinguishes itself from earlier results in the literature by its ability to blend active suspension, steering and traction control channels, in the presence of uncertainties and time‐varying dynamics, without the need for fault identification. The control structure is validated in the simulation environment using a fourteen‐degree‐of‐freedom non‐linear vehicle model. The integrated controller is compared to the case of a conventional control approach where each control problem is solved separately. Our results show that, compared to the conventional approach, the proposed method ensures that the vehicle follows driver inputs with up to bold33$$ \mathbf{33} $$% higher longitudinal maneuver velocity, despite the presence of actuator failures and slippery road conditions. Furthermore, to demonstrate the benefit of integrating active suspension control to the overall control scheme, we replaced the suspension control of the proposed approach with an independent suspension control system for comparison purposes. We then showed that the integrated case provided bold24$$ \mathbf{24} $$% lower roll angle deviation, and bold20$$ \mathbf{20} $$% lower pitch angle deviation, in the presence of actuator effectiveness loss and adverse road conditions.

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

“…Take a platoon of one leader vehicle with three followers as an example. We aim to establish an fixed-time distributed adaptive optimization strategy which ensures that y i reaches the optimal point in fixed time as  (p) = 0.5(p − i) 2 for i = 1, 2, 4 and  3 (p) = 0.5(p − 3) 2 for i = 3.…”

“…According to (2), it has: p = C for F ∈ R N . By considering p 1 = p = C and p 2 = p * = C * , it can be formulated as:…”

“…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.…”