Jiunshian Phuah

Neuro-sliding mode control for magnetic levitation systems

It is well known that sliding mode control (SMC) is capable of tackling systems with uncertainties. However, the discontinuous control signal causes the significant problem of chattering. Furthermore, thorough knowledge of the plant dynamics may be unknown or difficult to obtain, which makes it difficult to calculate the control law. A synergistic combination of neural network (NN) and SMC methodology is proposed. The network weights are adjusted using a modified online error backpropagation algorithm. Moreover, a new and simple approach is utilized to construct corrective controls of SMC to overcome the chattering problem. As a result, chattering is eliminated and the error performance of SMC is also improved. Experimental studies carried out on a magnetic levitation system are presented.

The use of NNs in MRAC to control nonlinear magnetic levitation system

This paper investigates the use of neural networks (NNs) in conventional model reference adaptive control (MRAC) to control a nonlinear magnetic levitation system. In the conventional MRAC scheme, the controller is designed to realize plant output convergence to a reference model output based on a plant which is linear. This scheme is effectively for controlling linear plants with unknown parameters. However, using MRAC to control the nonlinear magnetic levitation system in real time is a difficult control problem. In this paper, we incorporate a NN in MRAC to overcome the problem. The control input is given by the sum of the output of the adaptive controller and the output of the NN. The NN is used to compensate the nonlinearity of the plant that is not taken into consideration in the conventional MRAC. From experiment results, it has been shown that the plant output can converge to the reference model output after using NN in MRAC.

A method of simple adaptive control for MIMO nonlinear continuous-time systems using multifraction neural network

This paper presents a method of continuous-time simple-adaptive control (SAC) for multi-input multi-output (MIMO) nonlinear systems using multifraction neural networks. The control input is given by the sum of the output of the simple adaptive controller and the output of the multifraction neural network is used to compensate the nonlinearity of plant dynamics that is not taken into consideration in the usual SAC. The role of the multifraction neural network is to construct a linearized model by minimizing the output error caused by nonlinearities in the control systems.

A method of robust model matching control for nonminimum phase discrete-time systems in the presence of unmodeled dynamics

A method for the design of a robust model matching controller for nonminimum phase discrete-time systems in the presence of unmodeled dynamics is proposed. This controller robustly stabilizes the nominal plant in the presence of unmodeled dynamics and achieves the desired model matching simultaneously. Furthermore, in this method, we introduce the output loop compensator for the unmodeled dynamics. Sufficient condition for stabilizing the nominal plant in the presence of unmodeled dynamics has been established. Finally, the results of computer simulation are presented to illustrate the effectiveness of the proposed method.

Model reference adaptive control for multi-input multi-output nonlinear systems using neural networks

Presents a method of MRAC (model reference adaptive control) for multi-input multi-output (MIMO) nonlinear systems using NNs (neural networks). The control input is given by the sum of the output of a model reference adaptive controller and the output of the NN. The NN is used to compensate the nonlinearity of plant dynamics that is not taken into consideration in the usual MRAC. The role of the NN is to construct a linearized model by minimizing the output error caused by nonlinearities in the control systems.