A Quick-Simulation Tool for Induction Motor Drives Controlled Using Advanced Space-Vector-Based PWM Techniques

Space-vector-based pulse width modulation (PWM) for a voltage source inverter (VSI) offers flexibility in terms of different switching sequences. Numerical simulation is helpful to assess the performance of a PWM method before actual implementation. A quick-simulation tool to simulate a variety of space-vector-based PWM strategies for a two-level VSI-fed squirrel cage induction motor drive is presented. The simulator is developed using C and Python programming languages, and has a graphical user interface (GUI) also. The prime focus being PWM strategies, the simulator developed is 40 times faster than MATLAB in terms of the actual time taken for a simulation. Simulation and experimental results are presented on a 5-hp ac motor drive.

Multi-agent Reinforcement Learning for Traffic Signal Control

Optimal control of traffic lights at junctions or traffic signal control (TSC) is essential for reducing the average delay experienced by the road users amidst the rapid increase in the usage of vehicles. In this paper, we formulate the TSC problem as a discounted cost Markov decision process (MDP) and apply multi-agent reinforcement learning (MARL) algorithms to obtain dynamic TSC policies. We model each traffic signal junction as an independent agent. An agent decides the signal duration of its phases in a round-robin (RR) manner using multi-agent Q-learning with either is an element of-greedy or UCB 3] based exploration strategies. It updates its Q-factors based on the cost feedback signal received from its neighbouring agents. This feedback signal can be easily constructed and is shown to be effective in minimizing the average delay of the vehicles in the network. We show through simulations over VISSIM that our algorithms perform significantly better than both the standard fixed signal timing (FST) algorithm and the saturation balancing (SAT) algorithm 15] over two real road networks.

Simplified implementation of space vector PWM strategies for a three level inverter

Space vector based PWM strategies for three-level inverters have a broader choice of switching sequences to generate the required reference vector than triangle comparison based PWM techniques. However, space vector based PWM involves numerous steps which are computationally intensive. A simplified algorithm is proposed here, which is shown to reduce the computation time significantly. The developed algorithm is used to implement synchronous and asynchronous conventional space vector PWM, synchronized modified space vector PWM and an asynchronous advanced bus-clamping PWM technique on a low-cost dsPIC digital controller. Experimental results are presented for a comparative evaluation of the performance of different PWM methods.

NESTED SPARSE BAYESIAN LEARNING FOR BLOCK-SPARSE SIGNALS WITH INTRA-BLOCK CORRELATION

In this work, we address the recovery of block sparse vectors with intra-block correlation, i.e., the recovery of vectors in which the correlated nonzero entries are constrained to lie in a few clusters, from noisy underdetermined linear measurements. Among Bayesian sparse recovery techniques, the cluster Sparse Bayesian Learning (SBL) is an efficient tool for block-sparse vector recovery, with intra-block correlation. However, this technique uses a heuristic method to estimate the intra-block correlation. In this paper, we propose the Nested SBL (NSBL) algorithm, which we derive using a novel Bayesian formulation that facilitates the use of the monotonically convergent nested Expectation Maximization (EM) and a Kalman filtering based learning framework. Unlike the cluster-SBL algorithm, this formulation leads to closed-form EMupdates for estimating the correlation coefficient. We demonstrate the efficacy of the proposed NSBL algorithm using Monte Carlo simulations.