Dingli Yu

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Recursive orthogonal least squares (ROLS) is a numerically robust method for solving for the output layer weights of a radial basis function (RBF) network, and requires less computer memory than the batch alternative. In this paper, the use of ROLS is extended to selecting the centers of an RBF network. It is shown that the information available in an ROLS(More)
Fault tolerant control of dynamic processes is investigated in this paper using an auto-tuning PID controller. A fault tolerant control scheme is proposed composing an auto-tuning PID controller based on an adaptive neural network model. The model is trained online using the extended Kalman filter (EKF) algorithm to learn system post-fault dynamics. Based(More)
In the application of variable structure control to engine air-fuel ratio, the ratio is subjected to chattering due to system uncertainty, such as unknown parameters or time varying dynamics. This paper proposes an adaptive neural network method to estimate two immeasurable physical parameters on-line and to compensate for the model uncertainty and engine(More)
This paper presents modelling of internal combustion (IC) engine with adaptive neural networks. A radial basis function network model with both centres and weights adapted and a model with only weights adapted are compared with a fixed parameter model. The developed models are used in model based predictive control (MPC) to form an adaptive nonlinear MPC(More)