Interior noise cancellation for railway train systems is an important means of enhancing passenger comfort and quality of service. This study proposes a novel active noise control (ANC) approach that uses an finite impulse response (IIR) filter and neural network techniques to effectively reduce interior noise. The authors construct a multiple-channel IIR filter module that is a linearly augmented framework with a generic IIR model to generate a primary control signal. A three-layer perceptron neural network is employed for establishing a secondary-path model to represent air channels among noise fields. Since the IIR module and neural network are connected in series, the output of an IIR filter is transferred forward to the neural model to generate a final ANC signal. A gradient descent optimisation-based learning algorithm is analytically derived for the optimal selection of the ANC parameter vectors. Moreover, re-estimation of partial parameter vectors in the ANC system is proposed for online learning. Sufficient stability conditions are derived for the proposed ANC system. Lastly, the authors present the results of a numerical study to test their ANC methodology with realistic interior noise measurement obtained from Korean railway trains.

Path planning is an important task for optimal motion of a robot in a structured or unstructured environment. The paper shows how to plan the shortest collision-free path in 3D, when a robot is navigated to pick up some tools or to repair some parts from various locations. A path coordinator is proposed having the capabilities of an obstacle avoidance strategy and a traveling salesman problem (TSP) strategy. The obstacle avoidance strategy is to plan the shortest collision-free path between each pair of n locations in 2D or in 3D. The TSP strategy is to compute a minimal system cost of a tour that is defined as a closed path navigating each location exactly once. The TSP strategy can be implemented by the neural network. The obstacle avoidance strategy can be implemented by the optimal edges by the modified genetic algorithm and computes the optimal nodes along the optimal edges by the recursive compensation algorithm.>

The control of the motion of a two-link manipulator with a flexible arm is studied. The first link is rigid, and the second link has a flexible part as an extension of a rigid part. The motion of the manipulator takes place on a horizontal plane. The dynamics of the manipulator are determined in Lagrange's formulation. The positions (output) of specified points on the flexible link are obtained in the Cartesian base coordinate system using strain-gage and joint-variable measurements. The inputs are applied to the actuators of the revolute and prismatic joints. For the controller design, a time-series multivariate model of the autoregressive exogeneous (ARX) type is used to describe the input-output relation. The discounted least-square method is used to estimate parameters of the time-series model. A self-tuning controller (STC) is designed so that the positions of specified points on the flexible link track the given trajectory points. The controller operates on the Cartesian coordinates, specifying the positions of the chosen discrete points on the flexible link. Simulation results as well as laboratory experiments on a Stanford/JPL arm controlled by an STC are presented to illustrate the approach.>