In recent years, the suspension system in modern vehicles has played a key role both as far as driving safety and comfort is concerned. To satisfy these vehicle performance specifications,active suspension is currently studied and implemented in practice in recent decades.In contrast to passive suspensions, by introducing force into system, active suspension can alter the suspension dynamic in real-time. A design of a controller is needed for real-time tuning of the control force in an active suspension system (ASS) to fulfill the challenging control objectives of suspension system comprising road handling, ride convenience, and travel suspension. This research proposed a novel ant colony optimization (ACO) algorithm for solving multi-objective weight optimization problem of the linear quadratic regulator (LQR) for automobiles ASS. The optimization problem of ASS is to design a state-feedback controller (SFC) as a result ACO is used to find optimal LQR weights. Here bothQ and R weight matrix of LQR is tuned. On a quarter-car ASS (QCASS) system, the effectiveness of ACO-tuned LQR is analytically checked with hardware in loop (HIL) analysis for an irregular road surface. Here, forexperiment, ISO road D rough runway, bumpy path, and pulse-type road profile are taken into account. Experimental findings illustrate that the proposed procedure can substantially reduce the acceleration of the Car body due to irregular road profiles compared to classical tuned LQR and model predictive control (MPC). The proposed controller shows the profound impact on the efficiency of the control schemes for three different road profiles.

This paper presents the design and implementation of Modular Multilevel Inverter (MMI) to control the Induction Motor (IM) drive using intelligent techniques towards marine water pumping applications. The proposed inverter is of eleven levels and has the ability to control the speed of an IM drive which is fed from solar photovoltaics. It is estimated that the energy consumed by pumping schemes in an onboard ship is nearly 50% of the total energy. Considering this fact, this paper investigates and validates the proposed control design with reduced complexity intended for marine water pumping system employing an induction motor (IM) drive and MMI. The analysis of inverter is carried out with Proportional-Integral (PI) and Fuzzy Logic (FL) based controllers for improving the performance. A comparative analysis has been made with respect to better robustness in terms of peak overshoot, settling time of the controller and Total Harmonic Distortion (THD) of the inverter. Simulations are undertaken in MATLAB/Simulink and the detailed experimental implementation is conducted with Field Programmable Gate Array (FPGA). The results thus obtained are utilized to analyze the controller performance, improved inverter output voltage, reliable induction motor speed control and power quality improvement by reduction of harmonics. The novelty of the proposed control scheme is the design and integration of MMI, IM drive and intelligent controller exclusively for marine water pumping applications.

Sliding mode control is a promising approach for designing controllers for systems with empirical characteristics. This is a favored nonlinear control strategy that effectively addresses the uncertainties present in derived mathematical models. To further enhance the stability of such systems, an Adaptive Neuro Fuzzy Inference System is employed by adapting to dynamic changes and inconsistent correlations between excitation and response. In this study, Sliding Mode Control was deployed in the feedback loop, effectively serving as a state feedback controller based on a nonlinear control law. As a two-parameter control approach, Sliding Mode Control requires careful tuning to achieve optimal performance. The integration of the Adaptive Neuro-Fuzzy System aims to bestow the two parameters of Sliding Mode Control with the ability to rapidly reduce errors to zero, thereby enhancing overall control efficiency. The research focuses on utilizing an Adaptive Neuro Fuzzy Inference System to implement Sliding Mode Control for a DC servo system while emphasizing state feedback control. The Harmony Search Optimization method is employed to optimize controller parameters effectively. The results of the research demonstrate the achievement of a best-fit value, where the minimal standard error and Best fitness are considered. This highlights the successful integration of the proposed control strategy and validates its effectiveness in providing accurate and reliable control of the real-time DC servo system.

One of the most basic needs of any human being to survive is air. Unfortunately, this basic need is being polluted by many natural factors like volcanic eruptions, forest fires, and man-induced factors like transportation emission. Unpolluted air is now an ideal environment that can never be achieved. So, the pollution levels should be monitored continuously. However, monitoring the levels of pollution will not fix the environment. Forecasting these pollution levels can make the society more aware of the environment and help prepare safety measures. This research paper aims to forecast the air pollutant levels by comparing futuristic machine learning models, which are Long Short-Term Memory (LSTM) and Auto-Regressive Integrated Moving Average (ARIMA) performed on the ground station data from Central Pollution Control Board (CPCB), the data collected from a low-cost IoT hardware setup, and the fused data. The LSTM and ARIMA models have been used to forecast the air pollutant levels in the future. Further, the main novelty of this research is to show that the concept of sensor fusion increases the accuracy of the dataset. The outputs obtained after implementing LSTM and ARIMA models show more accurate results when compared with ground station data and the IoT data from sensors.