In this paper, the stabilization is studied for a complex dynamic model which involves nonlinearities, uncertainty, and Levy noises. This paper also discusses the controller discretization and presents a new algorithm to obtain the upper bound for the sample interval through which the exponential stability of the discrete system can still be guaranteed. Firstly, an integral sliding surface is designed to obtain the sliding mode dynamics for the considered stochastic Levy process. By using Lyapunov theory, generalized Ito formula and some inequality techniques, the exponential stability is proved in the sense of mean square for sliding mode dynamics. The reachability of the sliding mode surface is also ensured by designing a sliding mode control law. Secondly, the continuous-time controller is discretized from the point of control cost, and the squared difference is analyzed for the states before and after the discretization. Different from those classical stochastic differential equations driven by Brownian motions, the noise is supposed to be Levy type and the squared difference is analyzed in different cases. Furthermore, we obtain the largest sampling interval through which the discretized controller can still stabilize the Levy process driven stochastic system. Finally, a simulation for a drill bit system is given to demonstrate the results under the algorithms. & COPY; 2023 The Franklin Institute. Published by Elsevier Inc. All rights reserved.
This paper addresses the tracking control problem of intelligent vehicle steering systems with actuator faults and model uncertainty. An adaptive fuzzy logic system is employed to online approximate the unmodeled nonlinear dynamics of the steering system, which is combined with dynamic gain technology to design an adaptive sliding mode controller. This controller effectively compensates for the influences of unknown control gain and actuator faults on control performance. To mitigate the chattering phenomenon inherent in sliding mode control, first-order filtering technology is integrated into the controller design, ensuring smooth system operation. Finally, the designed controller can realize the asymptotic stability of the intelligent vehicle steering system by combining Lyapunov stability theorem analysis. The rationality of the designed control method is verified by numerical simulation and vehicle experiments.
A data-driven model-based microwave heating adaptive dynamic planning temperature tracking algorithm is proposed for the microwave heating process with time-varying nonlinearity and strong coupling. The microwave heating process is modeled using a single hidden-layer feedforward network and the discrimination capability of an extreme learning machine. Based on this, the performance index functions and control laws are approximated using the approximation capability of the single hidden layer feedforward network and the extreme learning machine. Experimental and simulation results show that the proposed algorithm successfully tracks the temperature of high titanium slag during the microwave heating process.
Early fault diagnosis, as an important means to avoid the hidden danger of industrial system, has always been a research hotspot in the field of industry. And the algorithm based on deep learning (DL) with its efficient feature extraction ability has gradually become the mainstream method to solve the problem of fault diagnosis. However, when the fault type is complex and the sample data is insufficient, the existing DL model has the problem of insufficient diagnosis accuracy or the model is too complex to train. Therefore, in order to effectively solve the existing problems, this paper proposes a fault diagnosis method based on support vector machine (SVM) optimized by improved stacked autoencoder (SAE) and differential evolution (DE). Firstly, the back propagation (BP) neural network with a softmax classifier is used to improve SAE, and then the improved SAE is used to extract the features of fault diagnosis data set. The extracted features are input into SVM for fault classification, and the key parameters of SVM are automatically optimized by the DE algorithm. By comparing with the known fault diagnosis methods, the experimental results show that this method can ensure the efficiency of fault diagnosis and improve the diagnosis accuracy when there are too many fault types and few sample data.
In the Internet of Vehicles scenario, the in-vehicle terminal cannot meet the requirements of computing tasks in terms of delay and energy consumption; the introduction of cloud computing and MEC is an effective way to solve the above problem. The in-vehicle terminal requires a high task processing delay, and due to the high delay of cloud computing to upload computing tasks to the cloud, the MEC server has limited computing resources, which will increase the task processing delay when there are more tasks. To solve the above problems, a vehicle computing network based on cloud-edge-end collaborative computing is proposed, in which cloud servers, edge servers, service vehicles, and task vehicles themselves can provide computing services. A model of the cloud-edge-end collaborative computing system for the Internet of Vehicles is constructed, and a computational offloading strategy problem is given. Then, a computational offloading strategy based on the M-TSA algorithm and combined with task prioritization and computational offloading node prediction is proposed. Finally, comparative experiments are conducted under task instances simulating real road vehicle conditions to demonstrate the superiority of our network, where our offloading strategy significantly improves the utility of task offloading and reduces offloading delay and energy consumption.
