Design and Evaluation of a Robust Optical Beam-Interruption-Based Vehicle Classifier System

This paper presents the design and development of a novel optical vehicle classifier system, which is based on interruption of laser beams, that is suitable for use in places with poor transportation infrastructure. The system can estimate the speed, axle count, wheelbase, tire diameter, and the lane of motion of a vehicle. The design of the system eliminates the need for careful optical alignment, whereas the proposed estimation strategies render the estimates insensitive to angular mounting errors and to unevenness of the road. Strategies to estimate vehicular parameters are described along with the optimization of the geometry of the system to minimize estimation errors due to quantization. The system is subsequently fabricated, and the proposed features of the system are experimentally demonstrated. The relative errors in the estimation of velocity and tire diameter are shown to be within 0.5% and to change by less than 17% for angular mounting errors up to 30 degrees. In the field, the classifier demonstrates accuracy better than 97.5% and 94%, respectively, in the estimation of the wheelbase and lane of motion and can classify vehicles with an average accuracy of over 89.5%.

Rollover-Preventive Force Synthesis at Active Suspensions in a Vehicle Performing a Severe Maneuver With Wheels Lifted Off

Among the intelligent safety technologies for road vehicles, active suspensions controlled by embedded computing elements for preventing rollover have received a lot of attention. The existing models for synthesizing and allocating forces in such suspensions are conservatively based on the constraints that are valid until no wheels lift off the ground. However, the fault tolerance of the rollover-preventive systems can be enhanced if the smart/active suspensions can intervene in the more severe situation in which the wheels have just lifted off the ground. The difficulty in computing control in the last situation is that the vehicle dynamics then passes into the regime that yields a model involving disjunctive constraints on the dynamics. Simulation of dynamics with disjunctive constraints in this context becomes necessary to estimate, synthesize, and allocate the intended hardware realizable forces in an active suspension. In this paper, we give an algorithm for the previously mentioned problem by solving it as a disjunctive dynamic optimization problem. Based on this, we synthesize and allocate the roll-stabilizing time-dependent active suspension forces in terms of sensor output data. We show that the forces obtained from disjunctive dynamics are comparable with existing force allocations and, hence, are possibly realizable in the existing hardware framework toward enhancing the safety and fault tolerance.

A New Successive Displacement Type Load Flow Algorithm and its Application to Radial Systems

A new successive displacement type load flow method is developed in this paper. This algorithm differs from the conventional Y-Bus based Gauss Seidel load flow in that the voltages at each bus is updated in every iteration based on the exact solution of the power balance equation at that node instead of an approximate solution used by the Gauss Seidel method. It turns out that this modified implementation translates into only a marginal improvement in convergence behaviour for obtaining load flow solutions of interconnected systems. However it is demonstrated that the new approach can be adapted with some additional refinements in order to develop an effective load flow solution technique for radial systems. Numerical results considering a number of systems-both interconnected and radial, are provided to validate the proposed approach.

Reinforcement Learning With Function Approximation for Traffic Signal Control

We propose, for the first time, a reinforcement learning (RL) algorithm with function approximation for traffic signal control. Our algorithm incorporates state-action features and is easily implementable in high-dimensional settings. Prior work, e. g., the work of Abdulhai et al., on the application of RL to traffic signal control requires full-state representations and cannot be implemented, even in moderate-sized road networks, because the computational complexity exponentially grows in the numbers of lanes and junctions. We tackle this problem of the curse of dimensionality by effectively using feature-based state representations that use a broad characterization of the level of congestion as low, medium, or high. One advantage of our algorithm is that, unlike prior work based on RL, it does not require precise information on queue lengths and elapsed times at each lane but instead works with the aforementioned described features. The number of features that our algorithm requires is linear to the number of signaled lanes, thereby leading to several orders of magnitude reduction in the computational complexity. We perform implementations of our algorithm on various settings and show performance comparisons with other algorithms in the literature, including the works of Abdulhai et al. and Cools et al., as well as the fixed-timing and the longest queue algorithms. For comparison, we also develop an RL algorithm that uses full-state representation and incorporates prioritization of traffic, unlike the work of Abdulhai et al. We observe that our algorithm outperforms all the other algorithms on all the road network settings that we consider.

Reinforcement learning with average cost for adaptive control of traffic lights at intersections

We propose for the first time two reinforcement learning algorithms with function approximation for average cost adaptive control of traffic lights. One of these algorithms is a version of Q-learning with function approximation while the other is a policy gradient actor-critic algorithm that incorporates multi-timescale stochastic approximation. We show performance comparisons on various network settings of these algorithms with a range of fixed timing algorithms, as well as a Q-learning algorithm with full state representation that we also implement. We observe that whereas (as expected) on a two-junction corridor, the full state representation algorithm shows the best results, this algorithm is not implementable on larger road networks. The algorithm PG-AC-TLC that we propose is seen to show the best overall performance.

Network pricing for QoS: A 'Regulation' approach

V. S. Borkar’s work was supported in part by grant number III.5(157)/99-ET from the Department of Science and Technology, Government of India. D. Manjunath’s work was supported in part by grant number 1(1)/2004-E-Infra from the Ministry of Information Technology, Government of India.

A neural network based automatic generation control design through reinforcement learning

This paper presents the design and implementation of a learning controller for the Automatic Generation Control (AGC) in power systems based on a reinforcement learning (RL) framework. In contrast to the recent RL scheme for AGC proposed by us, the present method permits handling of power system variables such as Area Control Error (ACE) and deviations from scheduled frequency and tie-line flows as continuous variables. (In the earlier scheme, these variables have to be quantized into finitely many levels). The optimal control law is arrived at in the RL framework by making use of Q-learning strategy. Since the state variables are continuous, we propose the use of Radial Basis Function (RBF) neural networks to compute the Q-values for a given input state. Since, in this application we cannot provide training data appropriate for the standard supervised learning framework, a reinforcement learning algorithm is employed to train the RBF network. We also employ a novel exploration strategy, based on a Learning Automata algorithm,for generating training samples during Q-learning. The proposed scheme, in addition to being simple to implement, inherits all the attractive features of an RL scheme such as model independent design, flexibility in control objective specification, robustness etc. Two implementations of the proposed approach are presented. Through simulation studies the attractiveness of this approach is demonstrated.

Experimental Investigation on Switching Characteristics of High-Current Insulated Gate Bipolar Transistors at Low Currents

Insulated gate bipolar transistors (IGBTs) are used in high-power voltage-source converters rated up to hundreds of kilowatts or even a few megawatts. Knowledge of device switching characteristics is required for reliable design and operation of the converters. Switching characteristics are studied widely at high current levels, and corresponding data are available in datasheets. But the devices in a converter also switch low currents close to the zero crossings of the line currents. Further, the switching behaviour under these conditions could significantly influence the output waveform quality including zero crossover distortion. Hence, the switching characteristics of high-current IGBTs (300-A and 75-A IGBT modules) at low load current magnitudes are investigated experimentally in this paper. The collector current, gate-emitter voltage and collector-emitter voltage are measured at various low values of current (less than 10% of the device rated current). A specially designed in-house constructed coaxial current transformer (CCT) is used for device current measurement without increasing the loop inductance in the power circuit. Experimental results show that the device voltage rise time increases significantly during turn-off transitions at low currents.

Experimental Investigation on Switching Characteristics of High-Current Insulated Gate Bipolar Transistors at Low Currents

Insulated gate bipolar transistors (IGBTs) are used in high-power voltage-source converters rated up to hundreds of kilowatts or even a few megawatts. Knowledge of device switching characteristics is required for reliable design and operation of the converters. Switching characteristics are studied widely at high current levels, and corresponding data are available in datasheets. But the devices in a converter also switch low currents close to the zero crossings of the line currents. Further, the switching behaviour under these conditions could significantly influence the output waveform quality including zero crossover distortion. Hence, the switching characteristics of high-current IGBTs (300-A and 75-A IGBT modules) at low load current magnitudes are investigated experimentally in this paper. The collector current, gate-emitter voltage and collector-emitter voltage are measured at various low values of current (less than 10% of the device rated current). A specially designed in-house constructed coaxial current transformer (CCT) is used for device current measurement without increasing the loop inductance in the power circuit. Experimental results show that the device voltage rise time increases significantly during turn-off transitions at low currents.

Experimental results on low cost microcontroller based DC drive implementation

This paper presents the experimental results for an attractive control scheme implementation using an 8 bit microcontroller. The power converter involved is a 3 phase full controlled bridge rectifier. A single quadrant DC drive has been realized and results have been presented for both open and closed loop implementations.

Experimental Study on Switching Characteristics of an Inverter Leg Consisting of IGBTs of Dissimilar Makes

Usually the top and bottom IGBT devices in an inverter leg are of the same make (i.e. from same manufacturer). At low power level, these two devices even may be contained in the same module. However at high power levels the top and bottom devices are in separate modules. Sometimes, in the event of device failure, device of particular make may be replaced by one of another make, but of same ratings (on account of non-availability of the original make). This paper investigates the effect of such intermixing of two different makes of high power IGBTs in an inverter leg on the switching characteristics. The switching transitions between IGBT and diode of similar make and those of IGBT and diode of dissimilar make are compared experimentally at various DC link voltages and currents. The comparisons are made in terms of, IGBT peak turn-on di/dt, IGBT peak turn-off di/dt, peak diode reverse recovery current (I-rr), peak IGBT voltage overshoot and switching energy losses.

Study of Multilevel Sinusoidal PWM methods for Cascaded H-Bridge Multilevel Inverters

This paper is a study of Multilevel Sinusoidal Pulse Width Modulation (MSPWM) methods; Phase Disposition (PD), Alternate Phase Opposition Disposition (APOD), Phase Opposition Disposition (POD) on a single phase Cascaded H-Bridge Multilevel inverter. Various factors such as amplitude modulation index (Ma), frequency modulation index (M-f), phase angle between carrier and reference modulating wave (phi) have been considered for simulation. Variation in these factors and their effect on inverter performance is evaluated. Factors such as DC bus utilization, output r.m.s voltage, total harmonic distortion (%THD), dominant harmonic order, switching losses are evaluated based on simulation results.

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.

Critical point phenomena in the electrical resistivity of binary liquid systems: Cs2 + CH3CN and CS2 + CH3NO2

Electrical resistance measurements are reported on the binary liquid mixtures CS2 + CH3CN and CS2 + CH3NO2 with special reference to the critical region. Impurity conduction seems to be the dominant mechanism for charge transport. For the liquid mixture filled at the critical composition, the resistance of the system aboveT c follows the relationR=R c−A(T−T c) b withb=0·6±0·1. BelowT c the conductivities of the two phases obey a relation σ2−σ1=B(T c−T)β with β=0·34±0·02, the exponent of the transport coefficient being the same as the exponent of the order parameter, an equilibrium property.

Critical behaviour of electrical resistivity in 5 binary liquid systems

For five binary liquid systems CS2+CH3CN, CS2+CH3NO2, CS2+(CH3CO)2O, C6H12+(CH3CO)2O, n-C7H16+(CH3CO)2O, the electrical resistance has been measured near the critical solution temperatures. The behaviour is universal. Below Tc, the conductivities of the two phases follow σ1−σ2 β, where = T−Tc Tc with β≈0.35. In the one phase region with b≈0.35±0.1 and is positive in some cases and negative in others.