Konghuayrob, Poom
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Item type:Publication, Microprocessor based fuzzy MPPT for PV-AC module DCM-flyback Inverter(2013-01-01); This paper proposes a maximum power point tracking using fuzzy based perturb and observe (P&O) algorithm for a photovoltaic (PV) system (an AC module). In AC module flyback inverter, modulation index (Δma) are adopted as a control variable to track the maximum power point (MPP) of PV array. In the conventional technique, step size of modulation index (Δma) is adopted in the simple P&O technique; Although this technique is easy to be implemented but there is some problems regarding large oscillation around the MPP and slow tracking when improper step size is selected. The proposed technique, fuzzy based P&O technique, is adopted to provide non-equal step size of Δma. The proposed fuzzy system was programmed on microcontroller which is a low cost processing device. Experimental result confirms that the proposed system can effectively track the power from PV system, and is better than the conventional technique. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Maximum power point tracking using hybrid fuzzy based p&o and back propagation (BP) neural network for photovoltaic system(2014-10-01); Photovoltaic system is one of the most popular renewable energy sources to solve the problem of energy crisis. The important problem of solar PV systems is their low efficiency and nonlinear output characteristics in the changing weather that causes the difficulty in tracking of maximum power. To overcome this problem, this paper proposes “Hybrid Fuzzy based P&O and Back Propagation (BP) neural network” for improving the efficiency and reducing the power oscillation of PV system. The proposed system composes of two parts which are fuzzy based P&O and Neural Network. The fuzzy based P&O is used to find the maximum power point (MPP) while the neural network is used to find the appropriate modulation index (ma). The proposed algorithm is adopted in the AC module flyback inverter in which modulation index (Δma) is used as the control variable to track the MPP of the PV array. Simulation results verify that the proposed technique can effectively track the power from PV system, and is better than the conventional P&O techniques - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Sensor Fusion of Light Detection and Ranging and iBeacon to Enhance Accuracy of Autonomous Mobile Robot in Hard Disk Drive Clean Room Production Line(2023-01-01); ; ; In this paper, the adaptive Monte Carlo localization (AMCL) error in terms of similar data detected by light detection and ranging (LiDAR) in different locations is investigated. This localization causes a robot to move to the incorrect location temporarily. We propose the fusion of landmark-based localization using an iBeacon device combined with the AMCL algorithm. This technique can solve the probabilistic localization problem of the conventional techniques applied in mobile robots by fusing the timed elastic band (TEB) and scan-matching algorithms, which reduces the error from 7 cm to less than 3 cm. The proposed technique is implemented on a clean-room-type mobile robot with 100 kg payload certificated by the SOP39 standard. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Decentralised and Centralised Fixed Structure H∞ Robust Loop Shaping for the MIMO Microsurgical Manipulation Based on PSOGSA(2026-01-01); ; Aoyama, HisayukiThis paper presents decentralised and centralised fixed-structure H∞ robust control methods optimised by the Particle Swarm Optimisation and Gravitational Search Algorithm (PSOGSA) for a coupled multi-input multi-output (MIMO) microsurgical manipulator. The design framework explicitly considers uncertainties and disturbance constraints. Conventional H∞ loop-shaping controllers are typically of high order, complex, and difficult to implement in practice. To address this limitation, Proportional-Integral-Derivative (PID)-structured decentralised and centralised H∞ controllers are proposed, providing compact structures while retaining robustness. The novelty of this work lies in embedding H∞ robustness criteria into practical PID-based frameworks, bridging the gap between theoretical robust design and experimental implementation in microsurgical applications. The proposed controllers are evaluated against a reduced-order H∞ controller derived from Hankel norm approximation and a Ziegler–Nichols tuned PID controller, using both simulation and experimental studies. Results demonstrate that the proposed controllers achieve improved stability margins (0.449–0.521 compared with 0.436 for the reduced-order design), maintain low root-mean-square errors (≈0.067–0.089) and remain robust under voltage disturbances where conventional PID control fails. These findings confirm the contribution of a practical and efficient robust control strategy for enhancing the precision and reliability of microsurgical manipulators. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Energy Prediction of Cleanroom-type Differential Drive Mobile Robot Based on Recurrent Neural Network(2023-01-01); ; The battery charger time is a major issue for mobile robots. The study of the power usage of each component is important for optimizing the overall power consumption. Additionally, knowing the total energy consumption before commanding a robot to execute a task is essential for effective queue management and determining which robots are ready to execute tasks or move to the charging station. In this paper, we propose an energy modeling system consisting of an energy sensing technique, logging, and a recurrent neural network prediction model. The model is configured to recognize the dynamic system of the drive unit with the support of the robot operating system. The proposed model has a prediction error of only 3.58%. The simulation and experimental results demonstrate the effectiveness of the proposed system. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Maximum power point tracking using neural network in flyback MPPT inverter for PV systems(2012-12-01); Generally, perturb and observe (P&O) technique is widely adopted in photovoltaic (PV) system to maximize the output power. In flyback inverter, the modulation index needs to be adjusted based on the P&O algorithm. However if the changing step size of modulation index (Δma) is too large, the fast MPP (Maximum Power Point) tracking can be achieved but the power oscillation around the MPP will be large. In contrary, the small changing step size results in long tracking time and small oscillation. Consequently, this paper proposes a technique to adjust the changing step size (Δma) of Flyback inverter to achieve both acceptable tracking time and low power oscillation. In the proposed technique, irradiance is adopted as the input of a neural network which is used to estimate the appropriate modulation step size. Simulation results confirm that the proposed neural network based inverter can find the appropriate changing step size (Δma) which is adequate for any irradiance conditions. © 2012 IEEE. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Remote sensing to minimize energy consumption of six-axis robot arm using particle swarm optimization and artificial neural network to control changes in real time(2020-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Specified order - H∞ loop shaping control for hard disk drive servo using pso(2016-08-01); The demand of data storage density has been increasing continuously, but the data access time and track pitch need to be decreased. The high precision servo control system is required to achieve the predicted goal that will be crossing 10 TB/in<sup>2</sup> area, I density before 2020. Due to the fact that the precision of read/write head is sensitive to disturbance and noise, it is the challenging problem for the practical control. To overcome this problem, this paper proposes a new servo controller designed, specified order - H<inf>∞</inf> loop shaping (SOHLS) which adapts the particle swarm optimization (PSO) for searching the optimal controller parameters. The proposed technique can solve the problem of high order that was caused from the conventional H<inf>∞</inf> loop shaping (HLS), in addition, it can guarantee the robustness of the servo system,. Based on simulation results, the performances of the proposed SOHLS approach are investigated in comparison with that of the conventional H<inf>∞</inf> robust loop shaping, fixed-structure H<inf>∞</inf> control based on nonsmooth algorithm including reduced order controller by Hangkel norm model reduction technique. The proper repeatable runout (RRO) and non-repea table runout (NRRO) were applied, to the system to verify the robustness of all designed, controllers. The results demonstrate the advantages of the proposed SOHLS which gains better performance and more robustness than the other conventional controllers. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Hybrid adaptive notch filter and fixed-structure pid H ∞ robust loop shaping control based pso for hard disk drive servo actuator(2019-02-01); ; Aoyama, HisayukiIn order to achieve the high precision head-positioning of the voice coil motor (VCM) actuator with narrow track pitch, the adaptive notch filter based limited-search-range of particle swarm optimization (PSO), as well as the fixed-structure propor-tional-integral-derivative H <inf>∞</inf> robust loop shaping controller using the concept of four closed loop disturbance norms is proposed. Generally the conventional method, fixed-frequency notch filter (FFNF), is combined with the nominal plant to reduce the effect of the mechanical vibration resonance; however, the resonance mode of servo system can be shifted with various factors such as the ambient temperature change, and the unbalanced disk. In addition, mathematical solving in the H <inf>∞</inf> robust control problems and the suitable notch filter design are very complex and the final results of the conventional controller with notch filters are normally complicated structure and high order which is difficult to implement. Thus, the adding of intelligent system in the proposed design with the careful range of the search space is utilized to suppress the vibration caused by resonance mode shifting and also reduce the order of the final robust controller. Simulation results of six scenarios test demonstrate the effectiveness of the proposed design compared with FFNF in the commercial product. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A convolutional neural network for segmentation of background texture and defect on copper clad lamination surface(2018-08-13); This research interprets the design and test process of copper clad lamination surface defects detection. The system was included four following stages: Image acquisition, image pre-processing and segmentation, convolutional neural network design and image classification. Image processing method and pattern recognition algorithm are utilized in the system. First, the author applies the smoothing filters to eliminate noise from the images and segmenting a defect from background texture. Then, the convolutional neural network architecture is created to learn local feature of defect and background texture. Finally, defect and background images from segmentation step are collected and fed into a convolutional neural network to train and perform the classification task. The classification results demonstrate that the proposed method can re-checked false positive detect from the conventional Sobel edge detection, Hence the accuracy was increased from 78.1% to 98.2%.