This study presents a novel meta-heuristic optimization method that combines the Henry Gas Solubility Optimization (HGSO) technique with symmetric chaotic systems. By leveraging the randomness of chaotic systems, the parameters of the HGSO algorithm that require random generation are produced through chaotic processes, allowing the algorithm to exhibit chaotic behavior in its pursuit of optimal values. This innovative approach is termed Chaotic Henry Gas Solubility Optimization (CHGSO), with the primary objective of enhancing the performance of the HGSO method. The randomness of the data obtained from chaotic systems was validated using NIST-800-22 tests. The CHGSO method was applied to both 47 benchmark functions and the optimization of parameters for a PID controller utilized in the speed control of a DC motor. To evaluate the effectiveness of the proposed method, it was compared with several widely recognized algorithms in the literature, including PSO, WOA, GWO, EA, SA, and the original HGSO algorithm. The results demonstrate that the proposed method achieved the best performance in 43 of the benchmark functions, outperforming the other algorithms. In the context of controller design, the PID parameters were optimized using the error-based ITSE objective function. According to the controller responses, the proposed method has achieved the best results in the simulation studies, with a settling time of 0.035 and a rise time of 0.014 without overshooting, and in the experimental studies, with a settling time of 0.15 and a settling time of 1.4%. When the results are examined, it is observed that it has achieved successful results in the controller design problem.

In this study, we present a novel approach for rehabilitation devices through the design of an active elbow joint orthosis, inspired by the fundamental principles of robotic exoskeletons. The device not only enables home-based usage but also facilitates the transmission of exercise data from patients to physiotherapists via the Internet of Things (IoT) device. This approach offers the possibility of increased therapy sessions for each patient while allowing physiotherapists access to data for real-time or subsequent analyses, thereby establishing a database. This permits a single physiotherapist to manage multiple patients more effectively. The developed mobile application within this research incorporates a distinct entry interface for both patients and physiotherapists. Maximum force and position values generated during each exercise period are displayed within the application. The device enables active exercise with a single degree of freedom at the elbow joint and is equipped with force sensors to ensure safety against potential high-shear forces. Furthermore, it can be worn on the upper extremity using adjustable Velcro straps to accommodate users with varying arm circumferences. Specifically, this system amalgamates two primary components: a microcontroller operating control algorithms and IoT technology, and a smartphone application containing interfaces for physiotherapists and users undergoing therapy. The control design of the device employs a P-Type Iterative Learning Control (ILC) due to periodic exercise movements, reducing the error norm by approximately 20% during each exercise period (excluding the initial period). The controller consistently diminishes error values with each iteration, ultimately converging to zero. Throughout an exercise lasting around 3 minutes, the average error norm is recorded as 0.229⁰. In essence, this study presents a pioneering approach that sets itself apart from other research by minimizing shear forces and errors through a specialized controller, all while enabling remote, home-based rehabilitation under expert supervision.

This study presents a novel approach for scanning and obtaining object contours within a workspace using a camera and subsequently following these 2-D contours to another region with the assistance of an end effector. The process begins with the determination of boundary lines of object images through advanced image processing methodologies. Subsequently, a conversion from camera pixel coordinates to robot metric coordinates is performed to facilitate trajectory planning for the robotic arm. The trajectory of the robot arm, and consequently, the path followed by the end effector, is determined based on these metric coordinates. The article provides a detailed exposition of the methodology, including the mathematical background and experimental study, showcasing the efficacy and accuracy of the proposed approach in real-world scenarios. This research contributes to the advancement of robotic systems capable of precise trajectory tracking and manipulation tasks in diverse applications. This study involves an innovative approach that combines theoretical and practical methods, including object contour detection using image processing, conversion of pixel coordinates obtained from the camera to robot metric coordinates, robot kinematics, and programming steps.

This study investigates the impact of chaotic speed-controlled mixing on biogas production efficiency and compares it with conventional fixed-speed mixing. Traditional mixing methods, often operated at fixed speeds or continuous modes, lead to high energy consumption and microbial instability. To address this, a hybrid mixing system combining a helical and propeller shaft was designed to enhance substrate homogenization and biochemical reaction efficiency. A Programmable Logic Controller (PLC) was integrated for automatic process control, while chaotic mixing algorithms, based on Hadley, Halvorsen, Lorenz, and Sprott-A systems, dynamically adjusted the mixing speed to optimize performance. Experiments were conducted at 20°C and 30°C under controlled laboratory conditions. Results showed that chaotic mixing significantly improved methane yield and combustion duration compared to fixed-speed mixing. At 20°C, the Chaotic Sprott-A method produced 18 L/day of methane, compared to 16 L/day with fixed-speed mixing. At 30°C, the Sprott-A method reached 22 L/day, surpassing the 20 L/day of the fixed-speed method. Additionally, combustion duration, an indicator of biogas quality, increased from 740 seconds (fixed-speed) to 829 seconds (Chaotic Sprott-A). These findings confirm that chaotic mixing enhances substrate distribution, improves biochemical reaction efficiency. Chaotic speed-controlled mixing presents a promising alternative for biogas reactors, offering higher methane production.

This study presents a sliding mode controller design for DC motor speed control using optimization algorithms. The design of sliding mode controllers typically requires expert input during the parameter determination phase. Traditionally, these parameters are set through trial-and-error methods based on the experience of specialists. However, this approach can be both time-consuming and costly. The application of optimization methods automates the parameter-tuning process, reducing human intervention and, in turn, minimizing both design time and costs. The goal of this study is to enhance the performance of optimization methods by hybridizing them with chaotic systems. The random structures of chaotic systems allow optimization algorithms to explore a broader solution space, thereby improving their performance. The analyses conducted in this study reveal that hybrid chaotic algorithms outperform their original ones. The data indicate that the use of hybrid algorithms generally leads to a decrease in Steady-State Error. Additionally, it is observed that when all hybrid algorithms are employed, the sliding mode controller does not exhibit any overshoot. The results demonstrate that the sliding mode controller performs effectively, achieving low settling time, rise time, and steady-state error, while also preventing chattering. Among the methods examined, the sliding mode controller optimized with the Chaotic Henry Gas Solubility Optimization algorithm delivers the best performance, ensuring optimal system stability.