Dual Design PID Controller for Robotic Manipulator Application

This research introduces a dual design proportional–integral–derivative (PID) controller architecture process that aims to improve system performance by reducing overshoot and conserving electrical energy. The dual design PID controller uses real-time error and one-time step delay to adjust the confidence weights of the controller, leading to improved performance in reducing overshoot and saving electrical energy. To evaluate the effectiveness of the dual design PID controller, experiments were conducted to compare it with the PID controller using least overshoot tuning by Chien–Hrones–Reswick (CHR)  technique. The results showed that the dual design PID controller was more effective at reducing overshoot and saving electrical energy. A case study was also conducted as part of this research, and it demonstrated that the system performed better when using the dual design PID controller. Overshoot and electrical energy consumption are common issues in systems that can impact performance, and the dual design PID controller architecture process provides a solution to these issues by reducing overshoot and saving electrical energy. The dual design PID controller offers a new technique for addressing these issues and improving system performance. In summary, this research presents a new technique for addressing overshoot and electrical energy consumption in systems through the use of a dual design PID controller. The dual design PID controller architecture process was found to be an effective solution for reducing overshoot and saving electrical energy in systems, as demonstrated by the experiments and case study conducted as part of this research. The dual design PID controller presents a promising solution for improving system performance by addressing the issues of overshoot and electrical energy consumption

Adaptive P Control and Adaptive Fuzzy Logic Controller with Expert System Implementation for Robotic Manipulator Application

This study aims to develop an expert system implementation of P controller and fuzzy logic controller to address issues related to improper control input estimation, which can arise from incorrect gain values or unsuitable rule-based designs. The research focuses on improving the control input adaptation by using an expert system to resolve the adjustment issues of the P controller and fuzzy logic controller. The methodology involves designing an expert system that captures error signals within the system and adjusts the gain to enhance the control input estimation from the main controller. In this study, the P controller and fuzzy logic controller were regulated, and the system was tested using step input signals with small values and larger than the saturation limit defined in the design. The PID controller used CHR tuning to least overshoot, determining the system's gain. The tests were conducted using different step input values and saturation limits, providing a comprehensive analysis of the controller's performance. The results demonstrated that the adaptive fuzzy logic controller performed well in terms of %OS and settling time values in system control, followed by the fuzzy logic controller, adaptive P controller, and P controller. The adaptive P controller showed similar control capabilities during input saturation, as long as it did not exceed 100% of the designed rule base. The study emphasizes the importance of incorporating expert systems into control input estimation in the main controller to enhance the system efficiency compared to the original system, and further improvements can be achieved if the main processing system already possesses adequate control ability. This research contributes to the development of more intelligent control systems by integrating expert systems with P controllers and fuzzy logic controllers, addressing the limitations of traditional control systems and improving their overall performance

Evaluation of Single and Dual image Object Detection through Image Segmentation Using ResNet18 in Robotic Vision Applications

This study presents a method for enhancing the accuracy of object detection in industrial automation applications using ResNet18-based image segmentation. The objective is to extract object images from the background image accurately and efficiently. The study includes three experiments, RGB to grayscale conversion, single image processing, and dual image processing. The results of the experiments show that dual image processing is superior to both RGB to grayscale conversion and single image processing techniques in accurately identifying object edges, determining CG values, and cutting background images and gripper heads. The program achieved a 100% success rate for objects located in the workpiece tray, while also identifying the color and shape of the object using ResNet-18. However, single image processing may have advantages in certain scenarios with sufficient image information and favorable lighting conditions. Both methods have limitations, and future research could focus on further improvements and optimization of these methods, including separating objects into boxes of each type and converting image coordinate data into robot working area coordinates. Overall, this study provides valuable insights into the strengths and limitations of different object recognition techniques for industrial automation applications

Design and Develop a Non-Invasive Pulmonary Vibration Device for Secretion Drainage in Pediatric Patients with Pneumonia

The study aimed to develop a non-invasive pulmonary vibration device, specifically tailored for pediatric patients, to address a range of pulmonary conditions. The device employs a PID control system to ensure consistent and precise vibrations. The primary contribution of this research is the successful development, testing, and implementation of this innovative device. Utilizing technical components such as an Arduino, a vibration DC motor, and an ADXL335 accelerometer, the device was engineered to deliver stable and continuous vibrations even when subjected to external pressures or interactions with the patient. Controllers, including P, PI, PD, and PID types, were rigorously compared. The Ziegler-Nichols tuning technique was applied for meticulous evaluation of vibration control specifically within the context of this non-invasive pulmonary vibration device. Our findings revealed that the PID controller displayed superior accuracy in vibration control compared to P, PI, and PD controllers. Clinical trials involving pediatric patients showed that the PID-controlled device achieved treatment outcomes comparable to conventional methods. The device's precise control of vibration strength provides an added benefit, making it a well-tolerated, non-invasive treatment option for various pulmonary conditions in pediatric patients. Future research is necessary to assess the long-term effectiveness of the device and to facilitate its integration into standard clinical practice. In summary, this study represents a significant advancement in pediatric pulmonary care, demonstrating the critical role that PID control systems adapted for non-invasive pulmonary vibration devices can play in enhancing treatment precision and outcomes

Design and Application of PLC-based Speed Control for DC Motor Using PID with Identification System and MATLAB Tuner

Industries use numerous drives and actuators, including DC motors. Due to the wide-ranged and adjustable speed, DC motor is widely used in many industries. However, the DC motor is prone to external disturbance and parameter changes, causing its speed to be unstable. Thus, a DC motor requires an appropriate controller design to obtain a fast and stable speed with a small steady-state error. In this study, a controller was designed based on the PID control method, with the controller gains tuned by trial-and-error and MATLAB Tuner with an identification system. The proposed controller design was implemented using PLC OMRON CP1E NA20DRA in the hardware implementation. Each tuning method was repeated five times so that the system performances could be compared and improved. Based on hardware implementation results, the trial-error method gave acceptable results but had steady-state errors. On the other hand, the use of MATLAB Tuner provided fast system responses with no steady-state error but still had oscillations with high overshoot during the transition. Therefore, the PID controller gains acquired from MATLAB Tuner must be tuned finely to get better system responses

Comparative Study of Takagi-Sugeno-Kang and Madani Algorithms in Type-1 and Interval Type-2 Fuzzy Control for Self-Balancing Wheelchairs

This study examines the effectiveness of four different fuzzy logic controllers in self-balancing wheelchairs. The controllers under consideration are Type-1 Takagi-Sugeno-Kang (TSK) FLC, Interval Type-2 TSK FLC, Type-1 Mamdani FLC, and Interval Type-2 Mamdani FLC. A MATLAB-based simulation environment serves for the evaluation, focusing on key performance indicators like percentage overshoot, rise time, settling time, and displacement. Two testing methodologies were designed to simulate both ideal conditions and real-world hardware limitations. The simulations reveal distinct advantages for each controller type. For example, Type-1 TSK excels in minimizing overshoot but requires higher force. Interval Type-2 TSK shows the quickest settling times but needs the most force. Type-1 Mamdani has the fastest rise time with the lowest force requirement but experiences a higher percentage of overshoot. Interval Type-2 Mamdani offers balanced performance across all metrics. When a 2.7 N control input cap is imposed, Type-2 controllers prove notably more efficient in minimizing overshoot. These results offer valuable insights for future design and real-world application of self-balancing wheelchairs. Further studies are recommended for the empirical testing and refinement of these controllers, especially since the initial findings were limited to four-wheeled self-balancing robotic wheelchairs

Exploring ResNet-18 Estimation Design through Multiple Implementation Iterations and Techniques in Legacy Databases

In a rapidly evolving landscape where automated systems and database applications are increasingly crucial, there is a pressing need for precise and efficient object recognition methods. This study contributes to this burgeoning field by examining the ResNet-18 architecture, a proven deep learning model, in the context of fruit image classification. The research employs an elaborate experimental setup featuring a diverse fruit dataset that includes Rambutan, Mango, Santol, Mangosteen, and Guava. The efficacy of single versus multiple ResNet-18 models is compared, shedding light on their relative classification accuracy. A unique aspect of this study is the establishment of a 90% decision threshold, introduced to mitigate the risk of incorrect classification. Our statistical analysis reveals a significant performance advantage of multiple ResNet-18 models over single models, with an average improvement margin of 15%. This finding substantiates the study’s central hypothesis. The implemented 90% decision threshold is determined to play a pivotal role in augmenting the system’s overall accuracy by minimizing false positives. However, it’s worth noting that the increased computational complexity associated with deploying multiple models necessitates further scrutiny. In sum, this study provides a nuanced evaluation of single and multiple ResNet-18 models in the realm of fruit image classification, emphasizing their utility in practical, real-world applications. The research opens avenues for future exploration by refining these methodologies and investigating their applicability to broader object recognition tasks

Design and Analysis of Solar Car Chassis for WSC 2023

The World Solar Challenger is a biennial global competition that plays a crucial role in testing and advancing the technological limits of solar-powered vehicles, particularly in the context of diverse travel conditions. The competition features three categories: Challenger Class, Cruiser Class, and Adventure Class. In 2023, Siam Technology University participated with the STC-4 model in the Cruiser Class. This research contributed to the vehicle's design, structural integrity, safety, and performance evaluation, essential for passing the scrutineering tests. Researchers were involved in shaping the vehicle's aerodynamics, analyzing structural aspects, and designing safety features compliant with World Solar Challenge 2023 requirements. The goal was to ensure the vehicle's safe participation and successful operation under varied conditions. The designed solar-powered vehicle, STC-4, aimed to address shortcomings from previous competitions. The structural framework, utilizing AISI 4130 steel, met safety standards, with a final weight of 258 kg. Structural testing ensured that the displacement did not exceed 25 mm. Finite Element Analysis confirmed the structural integrity, aligning with the team's objectives. In conclusion, this research significantly contributed to the design and safety aspects of solar-powered vehicles, as demonstrated by the successful participation of STC-4 in the World Solar Challenge 2023 Cruiser Class. The findings highlight advancements in structural design, safety measures, and performance capabilities, emphasizing the importance of continuous innovation in solar vehicle technology