In this work, a salp swarm optimization (SSO)-based controller design is proposed for photovoltaic reverse osmosis (RO) desalination unit. The photovoltaic RO model considered in this work is basically an interacting two-input-two-output (TITO) system. The interacting TITO system is first converted into two non-interacting systems with the help of decoupler. For the obtained non-interacting TITO system, two proportional-integral-derivative (PID) controllers are designed by minimizing the integral-of-squared-error (ISE). The ISE is formulated in terms of alpha and beta parameters for ease of simulation. Thus constructed ISE is minimized using recently proposed salp swarm optimization (SSO) algorithm. For showing the efficiency of SSO assisted controllers, other PID controllers are also obtained using some state-of-art optimization techniques. The results prove that SOS assisted PID controllers outperform other controllers.

The focus of present research endeavor was to design a robust fractional-order proportional-integral-derivative (FOPID) controller with specified phase margin (PM) and gain cross over frequency (${\omega }_{gc}$) through the reduced-order model for continuous interval systems. Currently, this investigation is two-fold: In the first part, a modified Routh approximation technique along with the matching Markov parameters (MPs) and time moments (TMs) are utilized to derive a stable reduced-order continuous interval plant (ROCIP) for a stable high-order continuous interval plant (HOCIP). Whereas in the second part, the FOPID controller is designed for ROCIP by considering PM and ${\omega }_{gc}$ as the performance criteria. The FOPID controller parameters are tuned based on the frequency domain specifications using an advanced sine-cosine algorithm (SCA). SCA algorithm is used due to being simple in implementation and effective in performance. The proposed SCA-based FOPID controller is found to be robust and efficient. Thus, the designed FOPID controller is applied to HOCIP. The proposed controller design technique is elaborated by considering a single-input-single-output (SISO) test case. Validity and efficacy of the proposed technique is established based on the simulation results obtained. In addition, the designed FOPID controller retains the desired PM and ${\omega }_{gc}$ when implemented on HOCIP. Further, the results proved the eminence of the proposed technique by showing that the designed controller is working effectively for ROCIP and HOCIP.

In this investigation, self-learning salp swarm optimization (SLSSO) based proportional- integral-derivative (PID) controllers are proposed for a Doha reverse osmosis desalination plant. Since the Doha reverse osmosis plant (DROP) is interacting with a two-input-two-output (TITO) system, a decoupler is designed to nullify the interaction dynamics. Once the decoupler is designed properly, two PID controllers are tuned for two non-interacting loops by minimizing the integral-square-error (ISE). The ISEs for two loops are obtained in terms of alpha and beta parameters to simplify the simulation. Thus designed ISEs are minimized using SLSSO algorithm. In order to show the effectiveness of the proposed algorithm, the controller tuning is also accomplished using some state-of-the-art algorithms. Further, statistical analysis is presented to prove the effectiveness of SLSSO. In addition, the time domain specifications are presented for different test cases. The step responses are also shown for fixed and variable reference inputs for two loops. The quantitative and qualitative results presented show the effectiveness of SLSSO for the DROP system.

This paper investigates frequency regulation of an airport microgrid (AIM) through the application of an integral absolute error (IAE)-assisted control approach. The islanded AIM is initially captured using a linearized transfer function model to accurately reflect its dynamic characteristics. This model is then simplified using a first-order plus dead time (FOPDT) approximation derived via a reaction-curve-based method, which balances between model simplicity and accuracy. Two different proportional–integral–derivative (PID) controllers are designed to meet distinct objectives: one focuses on set-point tracking (SPT) to maintain the target frequency levels, while the other addresses load disturbance rejection (LDR) to reduce the effects of load fluctuations. A thorough comparison of these controllers demonstrates that the SPT-mode PID controller outperforms the LDR-mode controller by providing an improved transient response and notably lower error measures. The results underscore the effectiveness of combining IAE-based control with reaction curve modeling to tune PID controllers for islanded AIM systems, contributing to enhanced and reliable frequency regulation for microgrid operations.

The increasing adoption of Artificial Intelligence (AI) and Machine Learning (ML) technologies has created new possibilities in improving accessibility and communication for people with disabilities. One of the most significant challenges faced by the deaf and mute communities is the communication barrier with non-sign language users. This paper proposes an AI/ML-based real-time sign language converter that enables seamless communication via audio calls for deaf and mute individuals. The proposed system integrates multiple components, including gesture recognition technology and computer vision, to translate sign language gestures into text and audio. The real-time converter uses machine learning models to recognize hand and facial gestures, then converts the recognized gestures into text, which is subsequently converted into speech via a text-to-speech (TTS) engine. The system also ensures minimal latency, enhancing the user experience during live interactions. Experimental results show that the system offers an efficient, real-time solution for overcoming communication barriers, fostering inclusivity and independence for deaf and mute individuals.