Nur Syazreen Ahmad’s research while affiliated with Universiti Sains Islam Malaysia and other places

p>A two-wheeled self-balancing robot (TWSBR) is an underactuated system that is inherently nonlinear and unstable. While many control methods have been introduced to enhance the performance, there is no unique solution when it comes to hardware implementation as the robot’s stability is highly dependent on accuracy of sensors and robustness of the electronic control systems. In this study, a TWSBR that is controlled by an embedded NI myRIO-1900 board with LabVIEW-based control scheme is developed. We compare the performance between proportional-integral-derivative (PID) and linear quadratic regulator (LQR) schemes which are designed based on the TWSBR’s model that is constructed from Newtonian principles. A hybrid PID-LQR scheme is then proposed to compensate for the individual components’ limitations. Experimental results demonstrate the PID is more effective at regulating the tilt angle of the robot in the presence of external disturbances, but it necessitates a higher velocity to sustain its equilibrium. The LQR on the other hand outperforms PID in terms of maximum initial tilt angle. By combining both schemes, significant improvements can be observed, such as an increase in maximum initial tilt angle and a reduction in settling time.</p

In this paper, we present an internet of things (IoT) powered solution that facilitates effortless monitoring and management of vertical farming operations. Our proposed approach employs cost-effective embedded microcontrollers and sensors to keep a tab on crucial parameters like soil moisture, air humidity, and temperature. The data acquired from these sensors can be accessed through a web page that is compatible with all web browsers and smart gadgets such as mobile phones and tablets. Furthermore, the IoT platform offers users the ability to regulate soil moisture and administer ultraviolet light to plants. The system can bring many benefits such as enabling real-time monitoring and control of environmental conditions, reducing energy consumption, improving scalability and flexibility, and contributing to the sustainable and efficient production of food.

While the recurrent neural network (RNN) architecture has been the go-to model in transient modeling, recently the temporal convolutional network (TCN) has been garnering more attention as it has a longer memory than recurrent architectures with the same capacity. In this paper, we propose the use of the TCN for transient simulation of high-speed channels. The adaptive successive halving algorithm (ASH-HPO) is used to perform automated hyperparameter optimization for the TCN. It has two components, progressive sampling and successive halving. It iteratively expand the size of training dataset and eliminates a certain percentage of bad performing models. The progressive sampling component is modified to preserve the original sequencing of time series data to prevent information leakage. Also, the successive halving component is modified so that each eliminated model must be validated using at least two different validation datasets before it is being removed. The robustness of the proposed method is demonstrated using four high-speed channel examples, and the TCN is compared against existing convolutional neural network long short-term memory (CNN-LSTM) and dilated causal convolution (DCC) models. The TCN outperforms the other models consistently in all four tasks in terms of training speed, amount of training data to converge, and accuracy.

This work demonstrates the design and development of a magnetic levitation (MagLev) system that is able to control both the position and orientation of the levitated object. For the position control, a pole placement method was exploited to estimate parameters of the proportional integral derivative (PID) controller. In addition, the MagLev was constructed using a pair of electromagnets, two infrared (IR) receiver-emitter pairs and a servo motor to allow the orientation of the object to be controlled. The proposed controller was programmed in a LabVIEW environment, which was then compiled and deployed into an embedded NI myRIO board. Experimental results demonstrated that the proposed method was able to achieve a zero steady-state orientation error when the object was rotated from 0 ◦ to ±90◦ , a steady-state position error of 0.3 cm without rotation, and steady-state position errors of no greater than 1.2 cm with rotation.

Recent years have seen a dramatic rise in the popularity of autonomous mobile robots (AMRs) due to their practicality and potential uses in the modern world. Path planning is among the most important tasks in AMR navigation since it demands the robot to identify the best route based on desired performance criteria such as safety margin, shortest time, and energy consumption. The complexity of the problem can however become intractable when challenging scenarios are considered, which include navigation in a dynamic environment and solving multi-objective optimizations. Various classical and heuristic techniques have been proposed by researchers to mitigate such issues. The purpose of this paper is to provide a comprehensive and up-to-date literature review of the path planning strategies that have received a considerable attention over the past decade. A systematic analysis of the strengths, shortcomings, and scope of each method is presented. The trends as well as challenges in practical implementation of the strategies are also discussed at the end of this paper. The outcome of this survey provides useful guidance for future research into creating new strategies that can enhance the autonomy level of AMRs while preserving their robustness against unforeseen circumstances in practice.

The ball and beam (BnB) system serves as a benchmark in control engineering as it provides a foundational concept applicable to addressing stabilization challenges of various underactuated nonlinear systems. This includes tasks like maintaining the balance of goods carried by mobile robots and controlling the attitude of unmanned aerial vehicles. In this study, the focus is on enhancing control optimization strategies for BnB systems that take into account inherent nonlinearities arising from actuator constraints and state measurements. The work introduces a novel intelligent control approach, termed hybrid PSO-WOA, which combines Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) to automate optimal parameter search for proportional-integral-derivative (PID) and state feedback (SF) controllers. The collaborative technique between PSO and WOA is formulated to strike a balance between exploration and exploitation phases, and to mitigate premature convergence risks due to the system’s complexities. Additionally, three control schemes, namely cascade PID-PID, cascade PID-SF, and cascade PID-observer are introduced, each with tailored cost functions for optimization through the hybrid PSO-WOA algorithm, accommodating both measurable and unmeasurable state scenarios. Simulation results consistently demonstrate the superior performance of the hybrid approach compared to individual PSO and WOA methods, as well as conventional PID and linear quadratic regulator approaches. Notable, the hybrid approach exhibits a significant improvement in error metrics, reducing integral-time absolute error by 18.99%, integral squared error by 35.37%, and steady-state error by 92.86%. This substantial enhancement suggests promising directions for future research in automated control parameter tuning for underactuated nonlinear systems.

Fusion of X-band Doppler radar and infrared sensors can offer a great advantage for close proximity time-to-collision (TTC) prediction in the field of autonomous robot navigation due to precise obstacle speed detection and direction sensing. Nevertheless, poor ranging performance from the infrared sensors may result owing to fluctuating reflectivity of the moving obstacle. The TTC prediction accuracy may also be further degraded when the obstacle’s trajectory is non-parallel to the robot’s heading direction due to uncertainty in the radar’s radiation pattern which typically increases at the side lobes of the antenna. Thus, to enhance the performance, we propose an exponential Gaussian Process Regression (E-GPR)-based prediction model which is able to approximate an unknown function in a probabilistic manner. The proposed method is validated via a series of experiments with an obstacle approaching the robot from different viewing angles with a speed ranging between 30 and 63 cm/s. Results demonstrate that the average TTC error based on the sensor fusion is 0.31s; but with the E-GPR method, the error is successfully reduced to 0.0937s, which is the largest error reduction when compared against other competing machine-learning models such as multilayer perceptron neural network, support vector machine and boosted tree.

This article presents a modeling and simulation method for transient thermal analyses of integrated circuits (ICs) using the original and voltage-in-current (VinC) latency insertion method (LIM). LIM-based algorithms are a set of fast transient simulation methods that solve electrical circuits in a leapfrog updating manner without relying on large matrix operations used in conventional Simulation Program with Integrated Circuit Emphasis (SPICE)-based methods which can significantly slow down the solution process. The conversion from the thermal to electrical model is performed first by using the analogy between heat and electrical conduction. Since electrical inductance has no thermal equivalence, a modified VinC LIM formulation is presented which removes the requirement of the insertion of fictitious inductors. Numerical examples are presented which show that the modified VinC LIM formulation outperforms the basic LIM formulation, both in terms of stability and accuracy in the transient thermal simulation of ICs.