Anastasios Chassiakos

Automated container transport system between inland port and terminals

In this article we propose a new concept called automated container transportation system between inland port and terminals (ACTIPOT) which involves the use of automated trucks to transfer containers between an inland port and container terminals. The inland port is located a few miles away from the terminals and is used for storing and processing import/export containers before distribution to customers or transfer to the terminals. We design and analyze the ACTIPOT system with particular attention paid to the overall supervisory controller that synchronizes all the operations inside the ACTIPOT system. We employ the technique of truck platooning in order to simplify the control of the overall system and to minimize the possibility of deadlocks, congestion, and failures. A microscopic simulation model is developed and used to demonstrate the overall performance of the ACTIPOT system. The contribution of this article is the design, analysis, and evaluation of the new concept ACTIPOT.

Robot identification using dynamical neural networks

The authors solve the identification problem of a robotic manipulator using dynamical neural networks. They propose a dynamical backpropagation scheme that can learn and identify nonlinear systems without needing any prior knowledge about the system to be identified. Simulations show that the proposed algorithm can handle abrupt changes in input data, that the error converges quickly to zero, and that the network can effectively perform after the training stops, even when the input waveforms have not been previously presented.<<ETX>>

Modeling of Robot Dynamics by Neural Networks with Dynamic Neurons

This paper presents a new method of modeling the complex nonlinear dynamics of a robotic system by the use of neural networks. The networks introduced here are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. Networks with dynamic neurons are shown to perform much better than static nets in learning and predicting the nonlinear robot dynamics without any prior knowledge of the robot to be modeled.

Two-area system analysis and control using real-time digital simulator

This paper studies the effects of inter-area oscillations on a practical two-area system using Real-Time Digital Simulator (RTDS). RTDS is a hardware-in-the-loop simulator, which enables the real-time analysis and testing of devices in a controlled environment before they are actually connected to the grid. We study the two-area system stability with and without power system stabilizer (PSS). The system is modeled in RSCAD (Real Simulation Computer Aided Design). We show that by finely tuning the PSS system, the harmful low frequency oscillations can be reduced by 50%.