Department Publications

Solar photovoltaic (PV) systems have become a trend as a sustainable energy source on a global scale. However, the dirt accumulated on solar panels often reduces their efficiency, considerably. Existing cleaning methods have limitations such as requiring manual labour, low efficiency, limited to single cleaning method or single operational mode. As a solution, this research study describes the design and implementation of a semi-autonomous solar panel cleaning robot for both dry and wet cleaning. The robot can autonomously navigate on solar panels safely, or user can manually control it using a mobile app. The robot is capable of functioning for around 75 minutes on a single charge on solar panels tilted up to 20°, facilitated by image processing algorithms achieving an accuracy surpassing 96%. The orientation estimation is accomplished through the implementation of three parallel Kalman filters running on …

Optimization carries great significance in both human affairs and the laws of nature. It refers to a positive and intrinsically human concept of minimization or maximization to achieve the best or most favorable outcome from a given situation. Besides, as the resources are becoming scarce there is a need to develop methods and techniques which will make the systems extract maximum from minimum use of these resources, ie maximum utilization of available resources with minimum investment or cost of any kind. The resources could be any, such as land, materials, machines, personnel, skills, time, etc. The disciplines such as mechanical, civil, electrical, chemical, computer engineering as well as the interdisciplinary streams such as automobile, structural, biomedical, industrial, environmental engineering, etc. involve in applying scientific approaches and techniques in designing and developing efficient systems to …

Frequency-modulated continuous-wave (FMCW) multiple-input multiple-output (MIMO) long-range radars currently employed for autonomous robotics have limited maximum range. By employing transmit beamforming and beam scanning, the range can be increased. However, the beam scanning time reduces the achievable velocity resolution. In this paper, we propose an FMCW MIMO radar, operating at 77 GHz, with transmit (TX) beamforming with subarrays to increase the range. We employ four TX subarrays, each having three antennas, with analog beamforming. Compared to TX beamforming as one TX array, our approach provides sufficiently wide beams alleviating beam scanning, further, without substantially reducing the virtual elements of the MIMO radar. We implement the radar signal processing pipeline on a ZYNQ Ultrascale+ ZCU106 FPGA to achieve real-time processing. Furthermore, we employ a …

This chapter presents how optimal control is used in the form of feedback so that optimal control can be implemented on real systems. We consider linear plants and quadratic cost functions and develop the optimal regulator, which is known as the linear quadratic regulator (LQR). The nature of the optimality under LQR is analyzed, and the finite-time and infinite-time regulation processes are presented. Finally, the performance of LQR and PID control systems is compared.

This study aims to check the behaviour of various controllers that can successfully control a catamaran vessel on the ocean surface for trajectory tracking. Unlike on the ground, the ocean surface has disturbances from water currents, wind forces and ocean waves. Therefore, trajectory tracking is challenging work. A 3D model of a catamaran vessel was simulated to check the vessel’s behaviour with the controllers: Polynomial recurrent neural network-based adaptive PID controller, first and second-order sliding mode controllers, and first-order adaptive controller are among them. The control system is a combination of two controllers. The primary controller controls the vessel’s surge and yaw according to the velocity error. The secondary controller controls the velocity errors required by the primary controller, according to the displacement error of the catamaran vessel from the trajectory and the required forward …

We present SimuSIL, a software-in-the-loop (SIL) simulation platform using open-source tools to capture the behavior of connected vehicles in intelligent transportation system (ITS) applications. Connected cooperative vehicles use vehicle-to-everything (V2X) communication to enable ITS. Real-world testing of ITS applications involves significant risks. Simulating true vehicle dynamics and real-time behavior is challenging in discrete time step environments. We extend the Veins platform, integrating SUMO and OMNet++, with Webots, a simulator for robots and vehicles. Webots implements engine control unit (ECU) models in software to mimic physical vehicle behavior. We validate SimuSIL with a car-following application using Webots and demonstrate its ability to capture communication anomalies like packet losses. Additionally, we demonstrate fuel consumption and CO 2 emissions in autonomous eco-driving …

This chapter presents how to combine the equality constraints with the objective function in the optimization process. How to deal with inequality constraints using KKT conditions is presented next. Then, the implementation of quadratic optimization with equality constraints and boundary limits is demonstrated using the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathtt quadprog$$\end{document} command in Matlab. In the end, the optimization of an arbitrary function subject to nonlinear constraints, linear constraints, and boundary limits is demonstrated using the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage …

Some control systems need to maximize a certain outcome while minimizing the cost. For example, a rocket propulsion system intends to reach the maximum altitude with the least fuel. In such systems, the cost minimization concept in optimal control is changed to outcome maximization, and the particular optimal control design process can be implemented using Pontryagin’s maximum principle. This chapter presents the maximum principle based on the Hamiltonian and demonstrates its application through several exercises. The bang–bang control and control switching, which are outcomes of the maximum principle, are also presented.

Modern control systems are mostly implemented in discrete time. Hence, the optimal control process also needs discrete-time implementation. This chapter presents how discrete-time optimal control can be developed for discrete state-space systems with quadratic cost functions. Several worked exercises are presented to show how discrete-time optimal control is implemented.

This chapter presents the optimal control design for linear, continuous-time systems. The two main formulations of functional optimization—Lagrange–Euler and Pontryagin–Hamiltonian—are presented, and the conditions for optimality in each formulation are derived. The nature of optimality is also explored. The process of introducing end-point conditions of final time and final state is presented. Several exercises are provided to emphasize the application of the two formulations in optimal control system design.

Optimization aims at finding a unique point, a unique state, or a unique function (a sequence of points or states) that is associated with the minimum cost or maximum benefit with respect to a certain criterion, subject to constraints. Function optimization is largely based on calculus developed by Sir Isaac Newton and Gottfried Wilhelm Leibniz. The most comprehensible optimization process is finding the minima or maxima of a single variable function by setting the first derivative , which can be extended to multi-variable functions by setting all partial derivatives to zero . More often, the functions to be optimized are costs that can be modeled in the quadratic form , which allows wider applicability of mathematical tools in the optimization process, hence quadratic optimization.

This chapter presents how to use the calculus of variations to find the optimum function within a group of functions without considering the constraints. A few examples of functional optimization, including the shortest path and the minimum-time path, are presented. The Matlab implementation of functional optimization is also demonstrated. Figure  shows possible paths f ( x ) of a single variable functional between two fixed points and . It also shows the optimal function .

The optimal control sequence generated offline does not help realize optimal control in actual systems due to state deviations that are not captured in the state-space model. Such deviations are caused by unmodeled dynamics and noise, leaving only the first few control signals in the optimal control sequence valid and practically useful. In this context, the model predictive control (MPC) is presented in which an optimal control sequence is generated for a finite time into the future (the prediction horizon), and only the first few control signals (the control horizon) are practically used. After each control horizon, the process recurs in its actual state at that time.