Adaptive control: stability, convergence, and robustness
Adaptive control: stability, convergence, and robustness
The Integrated Control of Anti-lock Braking System and Active Suspension in Vehicle
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04
IEEE Transactions on Intelligent Transportation Systems
Approximation capabilities of hierarchical hybrid systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On multistage fuzzy neural network modeling
IEEE Transactions on Fuzzy Systems
Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems
IEEE Transactions on Fuzzy Systems
Neural-network hybrid control for antilock braking systems
IEEE Transactions on Neural Networks
Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems
IEEE Transactions on Neural Networks
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This paper proposes a novel method for identification and robust adaptive control of an anti-lock braking system with an active suspension system by using the hierarchical Takagi-Sugeno (T-S) fuzzy-neural model. The goal of a conventional ABS control system is to rapidly eliminate tracking error between the actual slip ratio and a set reference value in order to bring the vehicle to a stop in the shortest time possible. However, braking time and stopping distance can be reduced even further if the same control system also simultaneously considers the state of the active suspension system. The structure learning capability of the proposed hierarchical T-S fuzzy-neural network is exploited to reduce computational time, and the number of fuzzy rules. Thus, this proposed controller is applied to achieve integrated control over the anti-lock braking system (ABS) with the active suspension system. Our simulation results, presented at the end of this paper, show that the proposed controller is extremely effective in integrated control over the ABS and the active suspension system.