An adaptive event-triggered H∞ control method is proposed for the uncertain aero-engine distributed control systems with actuator fault,external disturbance,input-output quantization error and network induced time delay.Firstly,the actuator fault,external disturbance,quantization error and system uncertainty are mathematically described,an adaptive event-triggered mechanism is given to improve the utilization of network resources,and a closed-loop system mathematical model considering the above factors is established.Then,the Lyapunov-Krasovskii functional method is utilized to establish a sufficient condition to ensure the asymptotic stability of the system,and a co-design method of adaptive event trigger and H∞controller in the form of linear matrix inequality is given.Under the given simulation conditions,the adaptive event-triggered mechanism saves 94％of the network resources,which is an improvement of 46.4％compared to the ordinary event triggers.The simulation results show that the proposed event trigger and controller can ensure that the system is asymptotically stable despite the undesirable factors considered above.
As the marine environment is complex and changeable,ship navigation is easily affected by factors such as wind,waves,ocean currents and other factors,and ship motion is characterized by nonlinearity and coupling.Aiming at the problem that traditional ship motion prediction methods have insufficient efficiency in extracting time series data and are difficult to achieve high-precision prediction results,a sample convolution and interaction-channel attention(SCI-CA)neural network ship pitch motion prediction model is proposed.The model uses multi-category ship motion attitude data as input,splits the input into two subsequences,utilizes the recursive down sampling.convolution interaction structure of the sample convolution interaction network(SCI),and combines the rich features aggregated from multiple resolutions to improve the utilization of deep features of ship motion data.Then,channel attention mechanism(CA)is used to improve the weight ratio of effective channels,and the residual structure is input to the full connection layer to obtain the final prediction result.The simulation results of real ship data show that the prediction accuracy of the SCI-CA combined model is higher than that of other models,and its mean absolute percentage error(MAPE)and root mean square error(RMSE)are significantly reduced,verifying the effectiveness of the SCI-CA model in predicting ship motion.
When dealing with large-scale multi-objective optimization problem(LSMOP),the MOEA/D algorithm shows poor scalability in the decision space and a tendency to converge to local optima as the dimensionality of decision variables increases.To address this issue,this study proposes a large-scale MOEA/D algorithm with multiple strategies(MSMOEA/D).The MSMOEA/D algorithm introduces a hybrid initialization strategy based on autoencoders in the optimization process to expand the coverage of the initial population,thus promoting global search.Moreover,a neighborhood adjustment strategy based on aggregation functions is proposed,which can more accurately control the search range during the search process by adjusting neighborhood sizes,thereby avoiding low search efficiency caused by excessively large or small neighborhoods.Furthermore,a mutation-selection strategy based on non-dominated sorting is adopted during the optimization process.Different subproblems select their mutation strategies according to the number of individuals in the first level of non-dominated sorting to avoid the population falling into local optima and enhance the overall performance of the algorithm.Finally,the MSMOEA/D algorithm and other existing algorithms are evaluated using LSMOP and DTLZ test problems.Experimental results verify the effectiveness of the proposed algorithm for solving LSMOPs.
To reduce computational loads and save communication resources of multi-missile systems, event-triggered-based distributed differential game guidance laws for the nonlinear cooperative multi-missile system are investigated. First, the cooperative multi-missile interception problem is converted into multi-player zero-sum differential games, where all missiles minimize the cost function while the target maximizes the cost function. Second, event-triggered conditions are designed, for implementing the proposed guidance laws, Adaptive Dynamic Programming technique is adopted to approximate the couple event-triggered Hamilton-Jacobi–Isaacs equation, and adaptive weight-tuning laws are proposed. Furthermore, the proposed guidance laws can guarantee the stability of the nonlinear multi-missile system, which is proved by Lyapunov theory. The estimated weights of neural network and consensus tracking errors are ensured to be Uniformly Ultimately Bounded. Besides, a minimal inter-sample time is established to avoid the Zeno behavior. Finally, experiments show that the number of controller updates can be reduced to 65.36% by the proposed algorithm.
This article considers the distributed economic model predictive control (DEMPC) scheme for addressing the load frequency control problem in a multiarea interconnected power system with wind turbines. The system is divided into multiple dynamically coupled subsystems, each subjected to state and control input constraints due to safety concerns. The overall optimal control problem is decomposed into several local optimal control problems based on the local information of each subsystem, meaning each area designs its own local DEMPC controller. Within this framework, the future state trajectories of neighboring subsystems are estimated from the transmitted information between neighbors. To enhance overall economic benefits, the economic stage cost, including load frequency regulation cost, fuel consumption cost, and wind generation cost, is incorporated into the cost function. Simulation results and analysis under different scenarios demonstrate potential improvements in computational burden, economic performance, and robustness of the designed DEMPC controller.
