Accurate temperature regulation of jacketed continuous stirred tank reactors (CSTRs) remains a challenging task due to strong nonlinearities, tight coupling between mass and energy balances, and sensitivity to disturbances and operating-point variations. In this study, a novel augmented proportional–integral–derivative (PID) controller incorporating a hyperbolic tangent nonlinearity (APID-T) is proposed for robust temperature control of an exothermic CSTR. The controller structure extends the classical PID framework by embedding a bounded nonlinear term that enhances transient shaping and robustness while preserving simplicity and practical implementability. The tuning of the APID-T parameters is formulated as a constrained nonlinear optimization problem, where a composite objective function combining normalized overshoot and integral squared error is minimized. To solve this problem efficiently, the recently developed Schrödinger optimizer (SRA) is employed, exploiting its balanced exploration–exploitation mechanism. A detailed nonlinear dynamic model of the jacketed CSTR is considered, and stability characteristics around the nominal operating point are examined to ensure meaningful closed-loop operation. The proposed SRA-based APID-T design is extensively evaluated through comparative simulations against several state-of-the-art metaheuristic optimizers and alternative controller structures, including PI, PID with filter, two-degree-of-freedom PID, and fractional-order PID controllers. Performance is assessed using statistical indicators, convergence behavior, and time-domain response metrics under identical optimization settings. In addition, widely used error performance criteria, including the integral squared error, integral time absolute error, and integral time squared error, are computed to provide a comprehensive quantitative assessment of the tracking performance. The results demonstrate that the SRA-tuned APID-T controller consistently achieves lower objective-function values, faster convergence, reduced settling time, and significantly smaller overshoot compared with the competing approaches. Furthermore, frequency-domain analysis based on the Bode characteristics of the linearized open-loop system confirms favorable stability margins, supporting the robustness of the proposed control structure. Additional stability and robustness evaluations are conducted under practical non-ideal conditions, including feed-temperature disturbances, measurement noise, and multiple setpoint variations, where the controller maintains stable and accurate temperature regulation across the considered operating scenarios.

Due to the nonlinear and multimodal nature of infinite impulse response filter coefficient spaces, achieving stable and accurate identification remains a challenging task in digital signal processing. This study aims to evaluate the capability of the recently developed bio-inspired metaheuristic algorithm, called the enzyme action optimizer, for adaptive identification of infinite impulse response (IIR) filter models with varying structural complexities. The algorithm is tested on four benchmark systems with varying levels of order and structural complexity. For each system, both full-order and reduced-order identification scenarios are examined, resulting in eight independent experiments. In every case, enzyme action optimizer is directly compared with several well-known optimization algorithms including starfish optimization algorithm, hippopotamus optimizer, and grey wolf optimization. The results are evaluated using multiple performance criteria, including mean squared error, mean absolute error, standard deviation, and convergence speed, providing a comprehensive assessment. The results clearly show that enzyme action optimizer provides highly precise identification across all cases. In low-order systems with full-order modelling, the algorithm achieves perfect reconstruction with zero mean squared error, demonstrating its ability to exactly match system dynamics. In reduced-order setups, where structural simplification introduces modelling challenges, enzyme action optimizer consistently delivers the lowest errors and most stable convergence profiles. Even in high-order and asymmetric systems, enzyme action optimizer maintains its advantage, outperforming all comparative methods both in terms of accuracy and convergence speed. The success of enzyme action optimizer stems from its biologically inspired structure, where two control parameters, adaptive factor and enzyme concentration, regulate the balance between exploration and exploitation throughout the optimization process. This dynamic control enables enzyme action optimizer to search broadly in early iterations and focus on fine-tuning in later stages. The study establishes a unified benchmarking framework that uniquely demonstrates how a biologically inspired optimizer can be effectively adapted to complex infinite impulse response identification problems, highlighting its novelty and potential for broader optimization applications.

In this study, it is aimed to tracking a satellite on mobile vehicles for receiving the broadcasting signal of the satellite. Satellite tracking is used in many areas on mobile platforms. Too many problems come with the mobility. A robust tracking algorithm is need for overcome such problems. The robust satellite tracking case is researched and test software is developed for Android based platforms. Satellite tracking can be done with a receiver antenna by adjusting the positions according to the movements of the vehicle. Therefore Kalman Filter and Particle Swarm Optimization algorithm are compared in the use of satellite tracking process. An Android based application was developed to accomplish this and tests were performed using an android - based device on a mobile vehicle. Test data were collected through application from the sensors of the android device. Orientation and location data of the mobile vehicle are collected from the device sensors and they are used in computation of the azimuth and elevation angles of receiver antenna. Two well-known algorithms were implemented and performed on the application for tracking to keep satellite link online. It was systemically investigated the estimation and computational performance of the two algorithm on this subject. The results and test cases were analyzed and discussed. Test cases cover the computation of the azimuth and elevation angles.

This study proposes a new algorithm for air target radar imaging by taking the geometrical and processing advantages of array antenna systems. The backscattered signal is processed with a new technique to benefit the facilities of angular scanning. In order to virtually increase the number of the radar elements of the array system, a powerful optimal estimator is employed, namely Kalman filter.

In this paper a new method to obtain a target density function with variables range and scanning angle, by using theoretical background of the Radon transform that is widely used in tomographic scanning is presented. In order to acquire the backscattered signal from diverse angles, phased array radar system is employed. Effectiveness, validity and the place in the literature of this new approach are considered both in theoretical and practical aspects. The formulation, geometry and implementation results are achieved. The new method is compared with previous methods.