Control
Ayad Q. Abdulkareem; Abdulrahim Th. Humod; Oday A. Ahmed
Abstract
To perform fault tolerance for Anti-lock Braking System (ABS), This paper proposes a hybrid Fault Detection and Fault Tolerant Control (FD-FTC) for ABS speed sensors. It utilizes a Fault Detection (FD) unit and a Data Construction (DC) unit. The first one, the FD unit, is based on a kNN classifier model ...
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To perform fault tolerance for Anti-lock Braking System (ABS), This paper proposes a hybrid Fault Detection and Fault Tolerant Control (FD-FTC) for ABS speed sensors. It utilizes a Fault Detection (FD) unit and a Data Construction (DC) unit. The first one, the FD unit, is based on a kNN classifier model with 99.9% fault detection accuracy to perform three tasks: early fault detection, fault location diagnosis, and excluding faulty signals from being utilized in further processes. On the other hand, the second one, the DC Unit, is based on two separate neural network models. These models have an MSE of 2.01139e-1 and a R2 of 999880 for the first model and an MSE of 1.12486e-0 and 0.999586 for the second model. They are employed to provide an estimated alternative signal for the ABS speed sensors. These estimated signals are employed to perform two tasks: confirming fault detection declared by the FD model and compensating for the excluded faulty signal to fulfill fault accommodation. Both methods are trained and tested with MATLAB and Simulink. Results demonstrate that the proposed hybrid method has the ability to accurately detect and tolerate sensor faults and fulfill its design purpose, especially during emergency braking.
Mr. Qussay F. Ad; Doory; Dr. Mohammed Yousif Hassan
Volume 7, Issue 1 , June 2007, , Page 87-96
Abstract
Abstract:
A combination of fuzzy logic and neural network can generate a fuzzy neural
controller which in association with a neural network emulator can improve the output
response of the controlled system. This combination uses the neural network training
ability to adjust the membership functions ...
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Abstract:
A combination of fuzzy logic and neural network can generate a fuzzy neural
controller which in association with a neural network emulator can improve the output
response of the controlled system. This combination uses the neural network training
ability to adjust the membership functions of a PID like fuzzy neural controller. Such
controller can be used to adaptively control nonlinear MIMO systems.
The goal of the controller is to force the controlled system to follow a reference
model with required transient specifications of minimum overshoot, minimum rise time
and minimum steady state error. The fuzzy membership functions were tuned using the
propagated error between the plant outputs and the desired ones.
To propagate the error from the plant outputs to the controller, a neural network
is used as a channel to the error. This neural network uses the back propagation
algorithm as a learning technique.
The controller was tested using two inputs / two outputs nonlinear time invariant
model. Different reference (set-point) inputs were applied to the closed loop system.
Also, different values of loads and disturbances were applied to the closed loop system.
Simulation results show that the controller achieves the design requirements.
