Sensor Fault-Tolerant Control Design for Magnetic Brake System.

The purpose of the paper is to develop an efficient approach to fault-tolerant control for nonlinear systems of magnetic brakes. The challenging problems of accurate modeling, reliable fault detection and a control design able to compensate for potential sensor faults are addressed. The main idea here is to make use of the repetitive character of the control task and apply iterative learning control based on the observational data to accurately tune the system models for different states of the system.

Neural-network-based iterative learning control of nonlinear systems.

This work reports on a novel approach to effective design of iterative learning control of repetitive nonlinear processes based on artificial neural networks. The essential idea discussed here is to enhance the iterative learning scheme with neural networks applied for controller synthesis as well as for system output prediction. Consequently, an iterative control update rule is developed through efficient data-driven scheme of neural network training.

Two stage neural network modelling for robust model predictive control.

The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming.

Approximation of state-space trajectories by locally recurrent globally feed-forward neural networks.

The paper deals with investigating approximation abilities of a special class of discrete-time dynamic neural networks. The networks considered are called locally recurrent globally feed-forward, because they are designed with dynamic neuron models which contain inner feedbacks, but interconnections between neurons are strict feed-forward ones like in the well-known multi-layer perceptron. The paper presents analytical results showing that a locally recurrent network with two hidden layers is able to approximate a state-space trajectory produced by any Lipschitz continuous function with arbitrary accuracy.

Stability analysis and the stabilization of a class of discrete-time dynamic neural networks.

This paper deals with problems of stability and the stabilization of discrete-time neural networks. Neural structures under consideration belong to the class of the so-called locally recurrent globally feedforward networks. The single processing unit possesses dynamic behavior.