Kaitwanidvilai, Somyot

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Kaitwanidvilai, Somyot
Alternative Name
Kaitwanidvilai, S.
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Email
somyot.ka@kmitl.ac.th

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    Item type:Publication,
    Experimental realization of PSO-based hybrid adaptive sliding mode control for force impedance control systems
    (2025-06-01)
    Yanyong, Sarucha
    ;
    Kaitwanidvilai, Somyot
    This paper presents a practical solution for an adaptive impedance force controller with online learning capabilities, designed to mitigate the effects of inaccuracies in system identification models. The proposed hybrid algorithm addresses the challenges associated with online learning in real-world machines. Additionally, the system demonstrates the ability to adapt to environmental changes, maintaining high-quality performance despite variations. A sliding surface guarantees system stability, while Particle Swarm Optimization (PSO) optimizes impedance parameters, reducing the risk of local minima. The hybrid algorithm also reduces overshoot and undershoot, resulting in faster system responses. Simulation and experimental results demonstrate that the proposed technique outperforms conventional force control systems in terms of learning ability and overall performance.
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    Adaptive Force Control Using a Standard Deviation-based Hybrid Approach
    (2023-01-01)
    Songthai, Maethinee
    ;
    Yanyong, Sarucha
    ;
    Kaitwanidvilai, Somyot
    A good tracking response is essential for a force control system to achieve high performance in object handling. One of the significant challenges of the force control system is that the plant dynamics are directly dependent on the environment and the mechanical properties of the object. It's well-known that the mechanical properties of an object in manufacturing vary based on the type of product, gripper tools, and environment; hence, a non-adaptive controller may not be efficiently adopted. To address this issue, this paper proposes impedance control with hybrid adaptive algorithms to ensure the actuator system tracks the desired force command accurately. Particle Swarm Optimization (PSO) is adopted to adapt the impedance control parameters, thereby applying the capabilities of the learning system. A hybrid adaptive force control with a fitness function of the standard deviation and summation of error is proposed. The tracking response of the conventional adaptive controller was examined and compared with the proposed controller. The simulation results demonstrate the effectiveness of the proposed system.
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    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)
    Yanyong, Sarucha
    ;
    Parichatprecha, Rattapoohm
    ;
    Chaisiri, Punyavee
    ;
    Kaitwanidvilai, Somyot
    ;
    Konghuayrob, Poom
    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.
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    Energy Prediction of Cleanroom-type Differential Drive Mobile Robot Based on Recurrent Neural Network
    (2023-01-01)
    Yanyong, Sarucha
    ;
    Konghuayrob, Poom
    ;
    Chaisiri, Punyavee
    ;
    Kaitwanidvilai, Somyot
    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.
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    A Novel Robust Adaptive Control for PMDC Servo Motor Incorporating Recursive Least Square and Particle Swarm Optimization
    (2023-01-01)
    Yanyong, Sarucha
    ;
    Kaitwanidvilai, Somyot
    Position control is a crucial control system used extensively in various industrial applications. In many of these applications, system parameters, such as the mass of an object can change unpredictably. Non-adaptive position control proves inadequate in successfully managing these fluctuations. Thus, an online adaptive control system plays a vital role in maintaining high performance despite changing parameters. This paper proposes a novel online adaptive fixed-structure robust controller for a PMDC (Permanent Magnet Direct Current) servo motor. This controller employs Recursive Least-square and Particle Swarm Optimization techniques to form the adaptive system. Simulation results demonstrate the effectiveness of our proposed technique and its potential application.