Learning to win process-control games watching game-masters

The present paper focuses on some interesting classes of process-control games, where winning essentially means successfully controlling the process. A master for one of these games is an agent who plays a winning strategy. In this paper we investigate situations in which even a complete model (given by a program) of a particular game does not provide enough information to synthesize—even incrementally—a winning strategy. However, if in addition to getting a program, a machine may also watch masters play winning strategies, then the machine is able to incrementally learn a winning strategy for the given game. Studied are successful learning from arbitrary masters and from pedagogically useful selected masters. It is shown that selected masters are strictly more helpful for learning than are arbitrary masters. Both for learning from arbitrary masters and for learning from selected masters, though, there are cases where one can learn programs for winning strategies from masters but not if one is required to learn a program for the master's strategy itself. Both for learning from arbitrary masters and for learning from selected masters, one can learn strictly more by watching m+1 masters than one can learn by watching only m. Last, a simulation result is presented where the presence of a selected master reduces the complexity from infinitely many semantic mind changes to finitely many syntactic ones.

Robot building for preschoolers

This paper describes Electronic Blocks, a new robot construction element designed to allow children as young as age three to build and program robotic structures. The Electronic Blocks encapsulate input, output and logic concepts in tangible elements that young children can use to create a wide variety of physical agents. The children are able to determine the behavior of these agents by the choice of blocks and the manner in which they are connected. The Electronic Blocks allow children without any knowledge of mechanical design or computer programming to create and control physically embodied robots. They facilitate the development of technological capability by enabling children to design, construct, explore and evaluate dynamic robotics systems. A study of four and five year-old children using the Electronic Blocks has demonstrated that the interface is well suited to young children. The complexity of the implementation is hidden from the children, leaving the children free to autonomously explore the functionality of the blocks. As a consequence, children are free to move their focus beyond the technology. Instead they are free to focus on the construction process, and to work on goals related to the creation of robotic behaviors and interactions. As a resource for robot building, the blocks have proved to be effective in encouraging children to create robot structures, allowing children to design and program robot behaviors.

Operation and control of a DVR in the presence of interharmonics

The paper discusses the operating principles and control characteristics of a dynamic voltage restorer (DVR). It is assumed that the source voltages contain interharmonic components in addition to fundamental components. The main aim of the DVR is to produce a set of clean balanced sinusoidal voltages across the load terminals irrespective of unbalance, distortion and voltage sag/swell in the supply voltage. An algorithm has been discussed for extracting fundamental phasor sequence components from the samples of three-phase voltages or current waveforms having integer harmonics and interharmonics. The DVR operation based on extracted components is demonstrated. The switching signal is generated using a deadbeat controller. It has been shown that the DVR is able to compensate these interharmonic components such that the load voltages are perfectly regulated. The DVR operation under deep voltage sag is also discussed. The proposed DVR operation is verified through the computer simulation studies using the MATLAB software package.

TCSC control design for transient stability improvement of a multi-machine power system using trajectory sensitivity

This paper discusses the effects of thyristor controlled series compensator (TCSC), a series FACTS controller, on the transient stability of a power system. Trajectory sensitivity analysis (TSA) has been used to measure the transient stability condition of the system. The TCSC is modeled by a variable capacitor, the value of which changes with the firing angle. It is shown that TSA can be used in the design of the controller. The optimal locations of the TCSC-controller for different fault conditions can also be identified with the help of TSA. The paper depicts the advantage of the use of TCSC with a suitable controller over fixed capacitor operation.

Control schemes for equalization of capacitor voltages in neutral clamped shunt compensator

Voltage imbalance in capacitors is a well-known problem in compensator topologies which use two or more capacitors. This imbalance may exist even if the load does not contain any DC component, due to practical factors. However, when the load contains a DC part, the voltage imbalance problem becomes critical. In this paper, a two-quadrant chopper has been used to regulate the capacitor voltages in a two-capacitor compensator structure. Two different control strategies for the two-quadrant chopper to equalize the voltage of the capacitors have been proposed. The strategies are validated through detailed simulation studies. Experiments have also been carried out to validate the hysteresis control of chopper.

Performance evaluation of an adaptive travel time prediction model

This paper presents a travel time prediction model and evaluates its performance and transferability. Advanced Travelers Information Systems (ATIS) are gaining more and more importance, increasing the need for accurate, timely and useful information to the travelers. Travel time information quantifies the traffic condition in an easy to understand way for the users. The proposed travel time prediction model is based on an efficient use of nearest neighbor search. The model is calibrated for optimal performance using Genetic Algorithms. Results indicate better performance by using the proposed model than the presently used naïve model.