Journal of Measurement, Control, and Automation

Load adaptive sliding mode control for 6-degree-of-freedom parallel robot system Stewart platform

2024-12-11T10:02:36+07:00

The Gough-Stewart platform, also known as the Stewart platform, is a six-degree-of-freedom parallel robot with a complex interaction structure. It is widely used in applications requiring high precision and fast response, such as vehicle simulation, automobile, ship, and boat, as well as aerospace and medical. As a result, dealing with load changes during operation might lead the system to lose its high accuracy when using dynamic-based control models. To address this issue, this work begins with employing sliding mode control, a commonly utilized method for controlling high-order nonlinear systems. Then, for estimating the moving platform's mass and moment of inertia, an adaptive load management strategy for the system is introduced. To objectively demonstrate the proposed control structure's effectiveness, simulations are performed on the quasi-physical, Simscape simulation platform, provided by MATLAB. The simulation results demonstrate the outweigh performance of the proposed adaptive structure.

Design of controllers for the rotary inverted pendulum

2025-03-26T12:58:58+07:00

The rotary inverted pendulum is a typical nonlinear control object in automatic control research, commonly used to test and evaluate various control algorithms. Due to its inherent instability and strong nonlinearity, controlling this system poses significant challenges, requiring effective control methods to maintain the pendulum’s equilibrium at the upright position. Various control approaches have been applied to this system, including classical control algorithms such as PID, state feedback, and LQR, as well as modern control methods like fuzzy control and model predictive control. This study focuses on the design, simulation, and performance comparison of two primary controllers: LQR and a PID controller with tuned parameters using fuzzy logic. The control algorithms are validated through simulations in MATLAB Simulink and experiments on the Quanser QUBE-Servo2 module. Evaluation criteria include stability, adaptability, and sensitivity to disturbances. The results show that the LQR controller achieves high performance with minimal oscillations and a short settling time, while the PID controller with tuned parameters using fuzzy logic demonstrates better adaptability to changing environmental conditions and unexpected disturbances.

Load rotation control in crane system using hierarchical sliding mode control combined with particle swarm optimization

2025-04-24T13:27:40+07:00

This study focuses on controlling the direction of the load in the bridge crane system, aiming to ensure accurate direction control while simultaneously damping the residual oscillation caused by the cable twist. A hierarchical sliding mode control algorithm combined with swarm optimization was proposed to achieve that goal. The stability of the system was proven based on the Lyapunov criterion. An objective function, including the settling time and residual oscillation components, was constructed to determine the controller parameters, and the swarm optimization algorithm was applied to solve the problem of optimizing the objective function. The simulation results showed that the proposed method ensured accurate load direction control with fast residual oscillation damping. Comparisons with the input signal shaping method were also made to demonstrate the superiority of the proposed method.

Linearly constrained programming with integer variables for conductor size selec-tion in electrical distribution grids considering the impact of electricity price

2025-04-08T13:39:28+07:00

One major subproblem in planning distribution grids is optimally choosing conductor sizes. This subproblem often employs nonlinearly constrained optimization with integer variables as its modeling approach. Attaining optimality on a global scale is not always guaranteed by the nonlinear optimization formulation. As a means of determining the area of the wire’s section in the distribution grid in the most efficient manner possible, this study suggests a model that is based on linearly constrained optimization with integer variables. While adhering to load flow equations, maximum loading restrictions on branches, voltage magnitude bounds, and investment budget constraints, the goal function is minimizing the cost across the lifetime of the distribution system. Piecewise linearization methods and accurately linearized expressions of multiplying a binary variable by a continuous variable are deployed in order to convert the initially nonlinear model into the suggested optimization model. A programming environment called GAMS and an optimizer called CPLEX are used to evaluate the recommended optimization formulation on a 33-node test distribution network. The evaluation is implemented using diverse scenarios about the price of electricity. The calculation outcomes indicate that the size of conductors is significantly influenced by the electricity price. Furthermore, the conductor size based on the suggested methodology is more cost-effective than the one obtained using the method currently utilized in Vietnam

Simulation design of a multilevel MMC converter based on mathematical model of electronic components with NLM modulation algorithm

2025-02-14T11:30:42+07:00

This paper presents the design of mathematical models for the power circuit and control circuit of the MMC multilevel converter. The research focuses on describing all electronic components of the MMC multilevel converter using mathematical formulas that fully ensure the physical nature and function of the electronic components of the MMC system. As a result, the MMC multilevel converter can be described in the form of a mathematical model. This helps develop tools to verify control algorithms for MMC more conveniently due to the fact that the construction of experimental models as well as HIL simulation of the MMC multilevel converter is difficult because the number of component modules of the multilevel converter is large, exceeding the allowable limit of HIL simulation devices. The process of building mathematical formulas for electronic components and the entire power circuit system as well as the control circuit in this paper is carried out and simulated on LabView software when applying the NLM modulation algorithm to create 2N+1 voltage levels (N is the number of SMs in each branch of the MMC). The results are compared with the MMC model operating on Matlab software with the same scenario of technical parameters to verify the operation of the MMC converter.

Constant current charging with dual-phase receiver structure in dynamic wireless charging systems for electric vehicles

2025-03-07T15:53:33+07:00

Wireless dynamic charging is a solution to address the issues of travel distance and charging time for electric vehicles. A constant current charging mode is generally required to maximize energy transfer to the vehicle’s energy storage system in the shortest time. This paper proposes a new method for designing constant current charging with a dual-phase receiver structure in electric vehicles’ wireless dynamic charging system. Specifically, a dual-sided LCC compensation circuit is proposed to achieve constant current charging, while the dual-phase receiver structure reduces current ripple during charging. Simulation and experimental results indicate that the constant current charging mode is achieved with a small deviation of approximately 3.1%.

Adaptive beamforming for uniform circular arrays

2025-03-11T21:39:32+07:00

Beamforming in smart antennas is a highly effective technique that utilizes the flexible adjustment of antenna radiation characteristics, including main beam direction, interference nulling, and sidelobe level control. This paper proposes an adaptive beamforming solution based on the hybrid optimization algorithm combining Particle Swarm Optimization Grey Wolf Optimizer (HPSOGWO) and Bat Algorithm (BA) for Uniform Circular Arrays (UCA). The proposed approach demonstrates the ability to place nulls at interference directions and steer the main beam toward the desired direction. The modeling of circular antenna arrays, objective function, and evaluation scenarios will be presented. Additionally, the cumulative distribution function (CDF) and signal-to-noise ratio (SNR) will be addressed to assess system performance.

Leveraging multi-head attention transformer deep neural network architecture for improved wind speed forecasting

2025-03-11T09:45:53+07:00

Wind energy has great potential for electricity generation, but its variability makes accurate wind speed forecasting essential for efficient integration. This study explores the application of a transformer-based deep learning model for wind speed forecasting. The model features an encoder-decoder architecture with multi-head attention, feed-forward layers, and normalization functions. By leveraging a self-attention mechanism, the transformer model effectively captures temporal dependencies in time series data through weighted relationships among input sequences, leading to improved forecasting accuracy. To evaluate its effectiveness, we collected and pre-processed wind speed data from the Hong Phong 1 wind power plant, cleaned the data by removing outliers and addressed missing values. The processed data was then embedded and added positional encoding to prepare for model input. The model was trained, and its performance was benchmarked against other models, including Long Short-Term Memory, Convolutional Neural Networks, and Artificial Neural Networks. The obtained RMSE is quite low, with 0,26 m/s for single-step forecast, 0,73 m/s for 4-step forecast and 1,70 m/s for 16-step forecast. These results demonstrated that the transformer model achieved superior predictive performance, suggesting it as a powerful alternative to traditional forecasting methods, with significant potential for enhancing the accuracy of wind speed predictions.

An improved YOLOv8 model for fish classification and disease detection

2025-03-26T12:54:22+07:00

Fish classification and disease detection are crucial for sustainable aquaculture, necessitating accurate and efficient vision models. This study introduces FISH-YOLOV8, an enhanced YOLOv8 variant, incorporating: (1) SPD-Conv for optimized feature extraction and reduced computational load; (2) BiFormer Attention for enhanced small object detection and occlusion management; (3) dynamic IoU-threshold NMS to minimize false positives. This Article states that, evaluated on 15,162 images, FISH-YOLOV8 attains a mAP@50 of 0.990 and a mAP@50:95 of 0.859, outperforming baseline YOLOv8 and advanced models such as YOLOv11, at 45 fps, supports effective real-time aquaculture monitoring.

Coverage maximization of sensor networks with connectivity constraints in obstacle-filled environment based on nature-inspired algorithms

2025-04-24T12:56:23+07:00

The application of meta-heuristic algorithms has significant potential in various fields, including wireless sensor networks. In this paper, we utilize two algorithms, the Fruitfly optimization algorithm (FOA) and the Nutcracker optimization algorithm (NOA), to address two critical issues: optimizing coverage and ensuring connectivity in sensor networks. The main contribution of this paper is the application of these algorithms to arbitrary communication radius, independent of predefined connectivity assumptions. Simulation results demonstrate the effectiveness of the proposed methods by comparing with each other and with the two traditional algorithms Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Additionally, this paper simulates the coverage area in an environment with different types of obstacles to showcase the practical flexibility of the algorithms.