A stealthy cyberattack (SCA) aims to gradually drive the system toward instability. Although research is continuing in attack detection algorithms, a resilient control strategy is vital if SCAs go undetected for a considerable time. Especially for a controller involved in automatic generation control (AGC), resiliency becomes more crucial as the controller has to sometimes work under abnormal conditions considering that shutdown of operation is not an option. This paper introduces an innovative approach to support dual-area thermal power systems in the face of network-related challenges such as transference latency (TL) and SCAs. We propose a new tri-parametric fractional controller (TFC) that merges the advantages of proportional-integral and proportional-derivative operations, eliminating the need for additional control loops and enhancing system efficiency. The TFC's efficacy in mitigating random and step load disturbances is established, outperforming existing controllers. We employ the complex root boundary method to define the optimal parameter search space, with an improved equilibrium optimizer algorithm determining the best controller settings. The inclusion of renewable energy sources, including solar and wind, is considered, and the robustness of the system is evaluated using uncertainty norms and complementary sensitivity functions. Our approach is validated through hardware-in-loop real-time verification on the OPAL-RT platform, demonstrating superior performance in maintaining frequency stability under SCAs and TL challenges.

Farm machinery like water sprinklers (WS) and pesticide sprayers (PS) are becoming quite popular in the agricultural sector. The WS and PS are two distinct types of machinery, mostly powered using conventional energy sources. In recent times, the battery and solar-powered WS and PS have also emerged. With the current WS and PS, the main drawback is the lack of intelligence on water and pesticide use decisions and autonomous control. This paper proposes a novel multi-purpose smart farming robot (MpSFR) that handles both water sprinkling and pesticide spraying. The MpSFR is a photovoltaic (PV) powered battery-operated internet of things (IoT) and computer vision (CV) based robot that helps in automating the watering and spraying process. Firstly, the PV-powered battery-operated autonomous MpSFR equipped with a storage tank for water and pesticide drove with a programmed pumping device is engineered. The sprinkling and spraying mechanisms are made fully automatic with a programmed pattern that utilizes IoT sensors and CV to continuously monitor the soil moisture and the plant’s health based on pests. Two servo motors accomplish the horizontal and vertical orientation of the spraying nozzle. We provided an option to remotely switch the sprayer to spray either water or pesticide using an infrared device, i.e., within a 5-m range. Secondly, the operation of the developed MpSFR is experimentally verified in the test farm. The field test’s observed results include the solar power profile, battery charging, and discharging conditions. The results show that the MpSFR operates effectively, and decisions on water use and pesticide are automated.

This brief introduces a new fractional-order integral derivative ( ${\mathrm {FOI}}^{\lambda }\text{D}^{1-\lambda }$ ) controller for a class of integrating systems involving time delays. The stability region is explored through the complex root boundary (CRB) analysis which explicitly provides the search space of ${\mathrm {FOI}}^{\lambda }\text{D}^{1-\lambda }$ parameters for integrating time delay systems of any order. The optimal settings are obtained through a three-step optimization algorithm based on the CRB knowledge and the desired performance measures. Comparisons with other recent sophisticated tuning methods are also provided to demonstrate the effectiveness of the suggested control scheme through numerical study. Finally, a case study on a non-linear continuously stirred reactor under model uncertainties is presented to check the efficacy of the new method.

Industrial processes of time delayed integrating type require sophisticated control methods because of their non-self-regulating nature. Literature presents fractional order controllers as possible solution to this problem and there is scope for further enhancing the performance of the existing solutions. Though fractional order controllers are capable of outperforming their integer order counterparts, it is evident in literature that the former controller designs are complex and hence analytical tuning procedures for the same are scarce. Hence, this paper presents a novel predictive strategy for time-delayed integrating processes based on two fractional-order tilt-derivative (FOT $^{\alpha} \text{D}^{1-\alpha }$ ) controllers. These controllers are analytically designed with only two tunable parameters. These tuneables are computed using gain- and phase-margin specifications. In contrast to the previous methods, the presented scheme is more capable of eliminating input-load disturbances without adding complexity in terms of tunable parameters. In addition to the investigations carried out using three difficult benchmark plant models, the present design is also experimentally validated using a two-tank level control system to vindicate its efficacy. Through robust stability analysis, it is shown that the suggested strategy is capable of achieving stable closed-loop responses amid upto 50% perturbation in plant parameters.