International Journal of Adaptive Control and Signal Processing最新文献

With the rapid development of the global renewable energy source field, the importance of dynamic index processing technology in distributed energy systems has become more and more obvious. To better improve the real-time dynamic interaction means of microgrids in the energy Internet and optimize the relevant methods for microgrid energy consumption detection, this article proposes to introduce the distributed Hadoop platform into the electrical thermal coupling multivariate data in the form of cluster configuration, and then use the Spark framework to detect and capture real-time data, to complete the tracking and analysis of energy consumption data. At the same time, the Internet of Things and the cloud intelligent monitoring system are combined to further clean and explore the data, to achieve the in-depth detection of the energy consumption problem of the microgrid under the premise of reducing the initial investment, and achieve the purpose of reducing the operating cost. In this case, the outliers are detected according to the photovoltaic indicators of photovoltaic power stations, the filtration and purification functions of photovoltaic indicators are used by the nuclear density curve, and the sustainable solar energy is optimized by combining multiple indicators such as wind direction and temperature. Based on reducing energy consumption, the overfitting phenomenon of the controller is controlled, and an optimized controller-led cloud platform is established. By establishing the objective function model, the robustness of the controller is guaranteed and the detection expectation is satisfied by the experiment of energy consumption data. In addition, when the cloud platform is created, this study uses a genetic algorithm to optimize the controller index and then builds a cloud console detection mechanism that collaborates with the Internet. Through the research, it is found that outliers may lead to the redundancy of energy consumption indicators in the non-processing state. This study adopts the optimization of energy consumption parameters and the help of a distributed data framework to deal with and effectively solve this problem. In terms of interpolation simulation verification combined with experimental data, this paper proposes to use the Internet of Things, wearable devices, sensors, and other means to monitor the cost of energy consumption, to realize the distributed dynamic storage of massive real-time data in the process of parallel processing, as well as the evaluation and detection of real-time data replacement.

Rock drilling jumbo is an important large construction machine used for tunneling construction, and its automation has an urgent demand in engineering. However, the electro-hydraulic system of the rock drilling jumbo has strong parameters uncertainties and some dynamics that are hard to model accurately, which causes certain challenges for designing model-based high-performance control algorithms. To solve these challenges, a parameter adaptive based neural network sliding mode control algorithm is proposed to enhance control performance of the electro-hydraulic system. The parameter adaptive law is developed to estimate unknown parameters of the system, the neural network is applied for compensating unmodeled dynamics, and then the final control law is designed by sliding mode control method, and the stability demonstration of the closed-loop system is given. In the simulations, the effectiveness of the designed parameter adaptive law is verified. Extensive comparison experiments are performed on a real rock drilling jumbo driven by proportional valves, the experimental results demonstrate that the developed control algorithm obviously improves the control precision of hydraulic cylinder of the rock drilling jumbo compared with the traditional sliding mode and PID control algorithm, thus the designed control algorithm can be expanded and applied for general hydraulic servo control mechanism.

In this paper, we investigate the global adaptive stabilization problem via partial-state feedback for a class of uncertain chained-form nonholonomic systems with the dynamic uncertainty and nonlinear parameterization. The notions of Sontag's input-to-state stability (ISS) and ISS-Lyapunov function, together with the changing supply rates technique are used to overcome the dynamic uncertainty. The nonlinear parameterization is well treated with the aid of the parameter separation technique. The discontinuous input-to-state scaling technique is employed in this procedure to derive the global stabilization controllers. Additionally, we develop a switching adaptive control strategy in order to get around the smooth stabilization burden associated with nonholonomic systems. The simulation results illustrate the efficacy of the presented algorithm.

In this paper, an event-triggered adaptive tracking control strategy is proposed for strict-feedback stochastic nonlinear systems with predetermined finite-time performance. Firstly, a finite-time performance function (FTPF) is introduced to describe the predetermined tracking performance. With the help of the error transformation technique, the original constrained tracking error is transformed into an equivalent unconstrained variable. Then, the unknown nonlinear functions are approximated by using the multi-dimensional Taylor networks (MTNs) in the backstepping design process. Meanwhile, an event-triggered mechanism with a relative threshold is introduced to reduce the communication burden between actuators and controllers. Furthermore, the proposed control strategy can ensure that all signals of the closed-loop system are bounded in probability and the tracking error is within a predefined range in a finite time. In the end, the effectiveness of the proposed control strategy is verified by two simulation examples.

The problem of interval parity relations design for systems described by linear and nonlinear models under the external disturbances is considered. The problem is solved based on the reduced-order model of the original system. The relations allowing designing interval parity relations insensitive or having minimal sensitivity to the disturbances are obtained. The obtained interval parity relations are used to solve the problem of fault diagnosis. Theoretical results are illustrated by example.

Virtual coupling is gaining in popularity as a promising development direction to maximize the rail-line efficiency by minimizing the headway distance among trains in the presence of potentially encountered traction engines' faults with unknown amplitude, happening time and probability, which would be huge threat to the safety of trains without proper sensing and handling. This paper considers the fault diagnosis problem for virtually-coupled multiple trains using adaptive observer design. In order to generate false-free and timely fault alarming and relieving signals, an adaptive threshold function design is firstly given using a novel pattern-matched gain technique with explicit consideration of model uncertainty. Then, reinforced regressor-based fault identification algorithm is proposed to generate precise estimation of unknown fault values, activated and powered-off by the fault alarming and relieving signals output by fault detection observer, with globally Lipschitz property and fast convergence performance. Finally, comparative and simulation results are given to demonstrate the effectiveness and advantages of proposed fault diagnosis algorithms.