Hadi Hajieghrary

An information theoretic source seeking strategy for plume tracking in 3D turbulent fields

We present information theoretic search strategies for single and multi-robot teams to find and localize the source of a biochemical or radiological materials in turbulent flows. In our work, robots rely on sporadic and intermittent sensor readings to synthesize information maximizing exploration strategies to find and localize the position of the source. By reasoning about the spatial distribution of these sensory cues, the robots are able to construct a belief distribution over the possible positions of the source. The belief distribution is then employed to synthesize motion strategies that drives the robots to regions in the workspace that results in the largest decrease in the entropy of the belief distribution for the source position. We validate the proposed strategies in 2D and 3D environments and consider the performance of the strategies when robots have limited access to global pose information. In particular, the proposed strategies are validated using a three dimensional (3D) time-varying computational fluid model of the 2010 Deep Water Horizon oil spill.

Experimental Study of a Disturbance Rejection Controller for DFIG Based Wind Energy Conversion Systems

We consider a Double Fed Induction Generator (DFIG) based wind energy conversion system with highly nonlinear dynamics and abrupt changes as a test bed for optimally extracting wind energy. Dynamic backstepping is utilized to implement a sliding mode control that combines high order sliding mode control and Multi-Input/Multi-Output (MIMO) backstepping. A novel adaptive estimator is utilized to obtain the maximum active and reactive output power in the presence of stochastic wind velocity profiles which are fed as the reference signals to the algorithm. The controller developed is tuned and evaluated on a simulator of the DFIG based wind power conversion system; which is subsequently implemented on an experimental setup. Experimental results show that the proposed adaptive method outperforms the traditional control methods in terms of robustness and performance.© 2014 ASME

Small and Adrift with Self-Control: Using the Environment to Improve Autonomy

We present information theoretic search strategies for single and multi-robot teams to localize the source of a chemical spill in turbulent flows. In this work, robots rely on sporadic and intermittent sensor readings to synthesize information maximizing exploration strategies. Using the spatial distribution of the sensor readings, robots construct a belief distribution for the source location. Motion strategies are designed to maximize the change in entropy of this belief distribution. In addition, we show how a geophysical description of the environmental dynamics can improve existing motion control strategies. This is especially true when process and vehicle dynamics are intricately coupled with the environmental dynamics. We conclude with a summary of current efforts in robotic tracking of coherent structures in geophysical flows. Since coherent structures enables the prediction and estimation of the environmental dynamics, we discuss how this geophysical perspective can result in improved control strategies for autonomous systems.

Self-Tuning Adaptive Multiple Model Predictive Control with application to pH Control process

In this paper, an automatic learning and self-tuning multi-variable adaptive Multiple Model Predictive Controller (MMPC) is implemented on a laboratory scale pH pilot plant. The variables of the process is regenerated with an optimized real time soft analyzer, and a self-organizing neural network is trained to model the process. Generalized Predictive Control based on Independent Model (GPCI) and Dynamic Matrix Control (DMC) are used as base controllers. In the presented theme the nonlinear modeling of the plant, model bank generation, model identification for each region, and controller selection/design are done automatically. A new disturbance rejection supervisor is used to improve the performance of the automatic controller in the presence of disturbances. Exhaustive implementation analyses are provided to demonstrate the abilities of the presented control scheme.

Dynamic Adaptive Robust Backstepping Control Design for an Uncertain Linear System

This paper builds on the existing adaptive robust control (ARC) synthesis method introduced by Yao et al. and presents a new method to synthesize ARCs. Based on dynamic backstepping, the approach explicitly addresses the uncertain dynamics which enters into the system via the higher-order channels of the state-space model. As such, the proposed dynamic ARC (D-ARC) method addresses the inherent weakness of the original approach where uncertainty in the higher-order channels is ignored. The proposed approach is illustrated in simulations for controlling a voice coil motor (VCM) actuator that serves as a read/write head for a single-stage hard disk drive (HDD). The effectiveness of the resulting D-ARC controller is validated by considering the transient performance, tracking errors, and disturbance rejection of the VCM operating in both the track-seeking and track-following modes.

Adaptive disturbance rejection control scheme for DFIG-based wind turbine

In this paper, a new control structure is presented to extract maximum power from a wind regime. In this novel approach, a discrete-time higher order sliding-mode controller is designed as an observer to construct the reference value for the extractable power based on the condition that it operates. This is possible by tracking the optimal tip speed ratio with manipulating the voltages of the rotor in the doubly fed induction generator (DFIG) configuration. The presented structure improves the performance under abrupt changes in the wind speed and can be used for any type of optimum active power tracking algorithms. The simulations show the significant improvement in performance of the nonlinear discrete-time backstepping controller utilizing this technique.