A reliable real-time transport protocol for control systems over wireless networks

Deploying networked control systems (NCSs) over wireless networks is becoming more and more popular. However, the widely-used transport layer protocols, Transmission Control Protocol (TCP) and User Datagram Protocol (UDP), are not designed for real-time applications. Therefore, they may not be suitable for many NCS application scenarios because of their limitations on reliability and/or delay performance, which real-control systems concern. Considering a typical type of NCSs with periodic and sporadic real-time traffic, this paper proposes a highly reliable transport layer protocol featuring a packet loss-sensitive retransmission mechanism and a prioritized transmission mechanism. The packet loss-sensitive retransmission mechanism is designed to improve the reliability of all traffic flows. And the prioritized transmission mechanism offers differentiated services for periodic and sporadic flows. Simulation results show that the proposed protocol has better reliability than UDP and improved delay performance than TCP over wireless networks, particularly when channel errors and congestions occur.

Robots move : bootstrapping the development of object representations using sensorimotor coordination

This paper is concerned with the unsupervised learning of object representations by fusing visual and motor information. The problem is posed for a mobile robot that develops its representations as it incrementally gathers data. The scenario is problematic as the robot only has limited information at each time step with which it must generate and update its representations. Object representations are refined as multiple instances of sensory data are presented; however, it is uncertain whether two data instances are synonymous with the same object. This process can easily diverge from stability. The premise of the presented work is that a robot's motor information instigates successful generation of visual representations. An understanding of self-motion enables a prediction to be made before performing an action, resulting in a stronger belief of data association. The system is implemented as a data-driven partially observable semi-Markov decision process. Object representations are formed as the process's hidden states and are coordinated with motor commands through state transitions. Experiments show the prediction process is essential in enabling the unsupervised learning method to converge to a solution - improving precision and recall over using sensory data alone.

Advanced battery storage control for an autonomous microgrid

A new control method for battery storage to maintain acceptable voltage profile in autonomous microgrids is proposed in this article. The proposed battery control ensures that the bus voltages in the microgrid are maintained during disturbances such as load change, loss of micro-sources, or distributed generations hitting power limit. Unlike the conventional storage control based on local measurements, the proposed method is based on an advanced control technique, where the reference power is determined based on the voltage drop profile at the battery bus. An artificial neural network based controller is used to determine the reference power needed for the battery to hold the microgrid voltage within regulation limits. The pattern of drop in the local bus voltage during power imbalance is used to train the controller off-line. During normal operation, the battery floats with the local bus voltage without any power injection. The battery is charged or discharged during the transients with a high gain feedback loop. Depending on the rate of voltage fall, it is switched to power control mode to inject the reference power determined by the proposed controller. After a defined time period, the battery power injection is reduced to zero using slow reverse-droop characteristics, ensuring a slow rate of increase in power demand from the other distributed generations. The proposed control method is simulated for various operating conditions in a microgrid with both inertial and converter interfaced sources. The proposed battery control provides a quick load pick up and smooth load sharing with the other micro-sources in a disturbance. With various disturbances, maximum voltage drop over 8% with conventional energy storage is reduced within 2.5% with the proposed control method.

Active and reactive power control of synchronous generator for the realization of a virtual power plant

This paper presents a novel power control strategy that decouples the active and reactive power for a synchronous generator connected to a power network. The proposed control paradigm considers the capacitance of the transmission line along with its resistance and reactance as-well. Moreover the proposed controller takes into account all cases of R-X relationships, thus allowing it to function in Virtual Power Plant (VPP) structures which operate at both medium voltage (MV) and low voltage (LV) levels. The independent control of active and reactive power is achieved through rotational transformations of the terminal voltages and currents at the synchronous generator's output. This paper details the control technique by first presenting the mathematical and electrical network analysis of the methodology and then successfully implementing the control using MATLAB-SIMULINK simulation.

Adaptive stepsize based on control theory for stochastic differential equations

The numerical solution of stochastic differential equations (SDEs) has been focused recently on the development of numerical methods with good stability and order properties. These numerical implementations have been made with fixed stepsize, but there are many situations when a fixed stepsize is not appropriate. In the numerical solution of ordinary differential equations, much work has been carried out on developing robust implementation techniques using variable stepsize. It has been necessary, in the deterministic case, to consider the "best" choice for an initial stepsize, as well as developing effective strategies for stepsize control-the same, of course, must be carried out in the stochastic case. In this paper, proportional integral (PI) control is applied to a variable stepsize implementation of an embedded pair of stochastic Runge-Kutta methods used to obtain numerical solutions of nonstiff SDEs. For stiff SDEs, the embedded pair of the balanced Milstein and balanced implicit method is implemented in variable stepsize mode using a predictive controller for the stepsize change. The extension of these stepsize controllers from a digital filter theory point of view via PI with derivative (PID) control will also be implemented. The implementations show the improvement in efficiency that can be attained when using these control theory approaches compared with the regular stepsize change strategy.

100Hz onboard vision for quadrotor state estimation

This paper introduces a high-speed, 100Hz, visionbased state estimator that is suitable for quadrotor control in close quarters manoeuvring applications. We describe the hardware and algorithms for estimating the state of the quadrotor. Experimental results for position, velocity and yaw angle estimators are presented and compared with motion capture data. Quantitative performance comparison with state-of-the-art achievements are also presented.