Advanced Control for Applications最新文献

For the needs of vehicle vibration test platform with high precision, large load capacity and fast response, the three-dimensional model design and analysis of vehicle vibration test platform are carried out; in order to improve the motion performance of the platform, a vibration test plat-form control strategy combining hybrid heuristic algorithm and PID control is proposed. Based on the designed 3D model parameters, the single-channel mathematical model of the servo-electric cylinder is derived and a hybrid heuristic algorithm PID optimization model is established to compare and analyze the control performance of the platform with the Ziegler-Nichols method PID. The results show that the step system overshoot is 3.80% and the dynamic performance of the system is significantly improved when the hybrid heuristic algorithm PID control is used. The simulation system model of vehicle vibration test platform control is established, and the operation results show that the platform is closer to the input signal in the spatial position change curve when the hybrid heuristic algorithm PID control is used. Its maximum displacement error is 0.09 mm, and the motion accuracy of the system is improved by 61% compared with the Ziegler-Nichols method PID control.

Underactuation poses a significant challenge to space mission control and performance. This article investigates the non-linear attitude tracking control problem for a remote sensing satellite underactuated by a reaction wheel (RW) actuator fault. First, a timeline close to the in-orbit reality of an underactuation fault is presented. Then, the fault detection and diagnosis strategy is performed in a finite-time decision window. The failed actuator is excluded from the control loop by forming the proposed reconfiguration window to transition from a 3 RWs configuration to 2 RWs. The underactuation fault-tolerant control is designed according to the active method, where the adaptive robust control law employed for fault-free conditions is switched to the underactuated attitude tracking control (UATC). The structure of UATC is based on kinematic and adaptive backstepping dynamic controllers. The effect of unknown bounded external disturbances is considered with an adaptive estimation term. The asymptotic stability of the closed-loop control system is proved via Lyapunov theory in the presence of parametric uncertainty. Due to the underactuation, a new approach proposed in the prescribed performance function is interval error constraints, which include the pointing accuracy and stability requirements in imaging time intervals. Finally, the results of the multidisciplinary simulation and experimental test confirm the applicability of the underactuation fault-tolerant control.

Grey wolf optimization algorithm (GWO) has achieved great results in the optimization of neural network parameters. However, it has some problems such as insufficient precision, poor robustness, weak searching ability and easy to fall into local optimal solution. Therefore, a grey wolf optimization algorithm combining Levy flight and nonlinear inertia weights (LGWO) is proposed in this paper. The combination of Levy flight and nonlinear inertia weight is to improve the search efficiency and solve the problem that the search ability is weak and it is easy to fall into the local optimal solution. In summary, LGWO solves the problems of insufficient precision, poor robustness, weak searching ability and easy to fall into local optimal. This paper uses Congress on Evolutionary Computation benchmark function and combines algorithms with neural network for power line fault classification prediction to verify the effectiveness of each strategy improvement in LGWO and its comparison with other excellent algorithms (sine cosine algorithm, tree seed algorithm, wind driven optimization, and gravitational search algorithm). In the combination of neural networks and optimization algorithms, the accuracy of LGWO has been improved compared to the basic GWO, and LGWO has achieved the best performance in multiple algorithm comparisons.

This paper presents a comprehensive stability analysis of the boundary-based hybrid control (BBHC) algorithm designed for boost converter. The stability assessment is carried out utilizing multiple Lyapunov functions, addressing both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) operation. The boost converter is modeled as a hybrid automaton to capture its dynamic behavior accurately. Through rigorous Lyapunov stability analysis, this study demonstrates the effectiveness of the BBHC algorithm in ensuring stable operation of the boost converter across various operating modes. Additionally, the proposed control algorithm's validation is conducted using the FPGA-in-the-loop (FIL) technique, highlighting its efficiency and robustness in real-world applications. This research contributes valuable insights into the design and implementation of stable control strategies for boost converter, emphasizing the practical utility of the BBHC algorithm with FIL for enhanced performance and reliability in power electronics systems.

At present, there is no standard practice for temporary connection of component installation. How to make temporary connections to components more efficient and accurate is the key to the construction of cast-in-place connection parts in concrete prefabricated buildings. In order to improve the assembly technology of concrete structures, this paper combines three-dimensional intelligent image analysis technology for simulation, analyzes and compares several common electronic image stabilization algorithms, discusses the video image stabilization technology based on gray projection method in detail, and gives experimental results. Moreover, this paper analyzes and compares common moving target tracking algorithms, such as feature-based tracking, region matching-based tracking, dynamic contour-based tracking, and 3D model-based tracking. In addition, this paper studies the target tracking algorithm based on the Camshift algorithm, and constructs the assembly model of the intelligent concrete structure with the support of the algorithm. Through experimental verification, it is known that the performance distribution of the concrete structure assembly model based on 3D intelligent image analysis in experimental evaluation is between [81, 89], the experimental study shows that the concrete structure assembly model based on 3D intelligent image analysis can effectively improve the assembly effect of concrete structure.

Digital technology still has a low level of intelligence in the microgrid mode of teaching behavior analysis, resulting in the traditional manual observation and recording stage still being used for speaker identity classification, and the efficiency of teaching behavior analysis is also low. In response to the above issues, the research is based on the teacher-student analysis method and proposes a dual clustering algorithm based on the general background model Gaussian mixture model for speaker identity classification, thereby realizing the development and design of intelligent behavior analysis software. The research results indicate that the average recall rate of behavior transition points in the classroom teaching discourse corpus of the intelligent behavior analysis software is 89.03%, which is better than traditional analysis methods. Therefore, the intelligent behavior analysis software constructed by the dual clustering algorithm has high effectiveness and practicality. The research proposes a method model and implements intelligent visualization for classroom teaching behavior analysis, improving the efficiency of analyzing current microgrid teaching behavior.

In order to improve the trajectory control effect of multi-degree-of-freedom industrial robots, this paper combines visual image technology to conduct research on trajectory control of multi-degree-of-freedom industrial robots. Aiming at the problem of video segmentation under sudden illumination changes, this paper uses a Gaussian mixture model based on the global illumination function to adopt a variety of illumination invariant features, and proposes a scene segmentation algorithm suitable for sudden illumination changes. Moreover, this paper compares and verifies the algorithm from the subjective and objective perspectives through experiments, which shows that the algorithm in this paper can segment the scene more accurately even in the environment of sudden changes in illumination. In addition, the results of the accuracy test and the trajectory control test show that the research method of the multi-degree-of-freedom industrial robot trajectory control based on the visual image proposed in this paper can effectively improve the trajectory control effect of the robot.

For wireless sensor networks (WSNs), sensor nodes lose a certain amount of energy during the information collection and transmission process, and sensor nodes powered by non-replaceable batteries have limited energy and need to be controlled for energy consumption. In the face of the energy consumption issue in WSN data transmission, research has been conducted to analyze data fusion methods in order to reduce energy consumption. Based on machine learning techniques, a Deep Stacked Auto-Encoder (DSAE) model is constructed and trained using a layer-wise greedy approach. By combining this model with WSN, an algorithm based on the DSAE model, called Deep Stacked Auto-Encoder Data Fusion Algorithm (DSAEDFA), is obtained to do data fusion. The results show that compared to other algorithms, the proposed fusion algorithm has better fusion performance. When the number of iterations is set to 500, the DSAEDFA has 281 surviving nodes, which is 10 more than the Back-Propagation Data Fusion Algorithm (BPDFA) and 144 more than the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm. When the number of failed nodes is 40, the DSAEDFA has a network survival time of 2562 rounds, which is 746 rounds longer than the LEACH algorithm. The research method effectively extends the lifespan of wireless sensor networks and reduces data transmission energy consumption. Compared to previous methods, the proposed method consider the factors of node residual energy and distance on the basis of traditional routing protocols, making the selection of cluster heads more reasonable. The proposed method can organically combine the DSAE model with the clustering model, optimize the data fusion method, and improve the performance of the algorithm. In addition, by combining the DSAE model, a machine learning technique with clustering models has been expanded in terms of the application scope.

In order to improve the precision of earth pressure balance control and anti-interference ability of shield sealing chamber, this paper proposes a nonlinear control strategy for the screw conveyor based on disturbance observer and back-stepping method, so as to ensure the safe and efficient tunneling of the shield machine. According to the hydraulic flow dynamic balance principle of shield machine, the mechanism model of electro-hydraulic screw conveyor system is established, and the system state space model is derived. The nonlinear controller of the screw conveyor is designed by using the inverse step method and the disturbance observer compensation characteristic, so that the system responds quickly and compensates for the flow disturbance and external force disturbance in real time. At last, the system stability is proven by using the Lyapunov function. The experimental results show that the method has high control accuracy with fast response and strong anti-interference ability.

With regard to target tracking in wireless sensor networks, we are faced with problems like deficient occlusion handling and tracking failures during rapid movements due to complex and diverse circumstances. In order to effectively improve the accuracy of particle filter tracking caused by particle degradation, we propose an adaptive particle swarm optimization (APSO) particle filter algorithm. This algorithm uses particle filters to predict the target location in a particular area and introduces the particle swarm optimization (PSO) algorithm, of which both the evolutionary speed and the convergence accuracy are further improved by investigating the particle distribution through an entropy analysis, employing three different inertial weighting strategies and dynamic double mutation strategy, and exploiting the capabilities of the adaptive balancing algorithm in global and local searching. The simulation results show that the improved algorithm has a reduced root mean square error, shorter time consumption, faster speed, reduced target tracking error, and higher average success rate, so this algorithm exhibits sound real-time performance and accuracy in terms of occlusion handling and tracking loss.