International Journal of Adaptive Control and Signal Processing最新文献

This paper presents a novel neural adaptive sliding mode control (ASMC) for robust quadrotor trajectory tracking under severe nonlinearities, measurement noise, and external disturbances. The proposed strategy integrates a neural feedback-error-learning (FEL) framework that adaptively tunes both the sliding surface and reaching law gains online via a dual-cost optimization scheme, thereby simultaneously reducing tracking errors and control effort. Two key contributions form the cornerstone of this work. First, the neural FEL framework enables the online optimization of control gains by employing distinct cost functions that separately address tracking performance and control effort. Second, the integration of a flexible sigmoid activation function in the ANN's output layer guarantees strictly positive control gains, which enhances system stability. Simulation studies demonstrate that, compared with a similar ANN-based ASMC approach, the proposed controller achieves approximately a 26% reduction in maximum overshoot, nearly a 49% decrease in settling time, and roughly a 64% reduction in steady-state error. In addition, significant improvements in tracking performance are observed, as evidenced by reduced root mean square error (RMSE) and mean absolute error (MAE) while maintaining comparable control effort. These quantitative enhancements underscore the potential of combining adaptive neural tuning with sliding mode control for high-performance and reliable quadrotor operation in complex, uncertain environments.

This paper investigates the problem of fuzzy adaptive predefined-time full-state error constraints for permanent magnet synchronous motor systems. This study employs fuzzy logic systems (FLSs) to handle unknown nonlinear dynamic functions. A state observer based on fuzzy logic systems is utilized to estimate unmeasurable states. By utilizing a general potential Lyapunov function, predefined-time stability theory, combined with the dynamic surface control (DSC) technique and backstepping recursive technique, a predefined-time full-state error constraints control strategy is proposed. By applying predefined-time Lyapunov stability theory, it is proven that all signals in the closed-loop systems remain bounded and the tracking error converges within a predefined time. Finally, the effectiveness and feasibility of the proposed method are verified through simulation results.

This paper discusses the adaptive control problem for a class of stochastic nonlinear systems with uncertain nonlinear functions and uncertain measurement functions. First, a series of neural network functions are used to estimate the unknown nonlinear terms. Then reasonable assumptions about the unknown powers $��{�q�}_{�i�}�$'s are raised based on the notion of the homogeneity with monotone degrees. By recursively constructing twice continuous differential Lyapunov functions, the adaptive feedback controller is designed to deal with the unknown coefficients. Based on the stochastic stability theorem, it is proved that all signals in the closed-loop system are bounded in probability. Furthermore, the effectiveness of the proposed control approach is verified by a practical example and a numerical simulation.

A five-dimensional hyperchaotic system is proposed based on a three-dimensional autonomous chaotic system by introducing two new variables. Based on Lyapunov stability theory and passivity theory, the investigation looks into passivity-based adaptive chaotic synchronization. Through theoretical analysis and numerical simulation, the dissipation, phase diagrams, Lyapunov exponents, and bifurcation diagrams of the system were studied in detail to analyze its dynamic characteristics. The analog circuit simulation of the new five-dimensional system is carried out by Multisim. The simulation results are verified by comparing them with the numerical simulation results in MATLAB. The adaptive control method combines passive and adaptive control theories to provide a new perspective and solution for the synchronization control of chaotic systems. The parameter estimation and updating laws not only improve the flexibility of synchronization control but also provide strong support for subsequent experimental studies and practical applications. The effectiveness of the proposed method is verified by numerical simulation, which proves that the method performs well in coping with the synchronization problem of complex hyperchaotic systems with uncertain parameters.

Accurate and reliable wind power forecasting plays a crucial role in the utilization of wind energy. However, the complexity of the nonlinear process of converting wind energy into power alters the statistical distributions of errors (known as concept drift), making the accomplishment of this task greatly challenging. For this purpose, we devise an innovative approach for predicting wind power based on the “decomposition-prediction-ensemble” framework. First, the raw wind power data is broken down into multiple intrinsic mode functions (IMFs) via improved variational mode decomposition, and the dimensionality of these IMFs is reduced by adopting kernel principal component analysis, thus significantly simplifying the intricacies of the multidimensional IMF data. Then, considering the asymmetric characteristic of modeling error, a probability density function control- based temporal convolutional network is developed for each subseries, where the modeling error PDF is controlled to close to an ideal Gaussian distribution, so that the prediction model parameters are adjusted. Finally, the multiple subseries forecasting models are integrated by a PDF-based differential evolution ensemble strategy. And the convergence of ensemble strategy is analyzed from a mathematical point of view. As a result, the proposed method can break through the limitation of symmetric loss capturing merely the second moment information, alleviating distribution shift of error to make an unbiased estimate. Real-world wind farm data are utilized for empirical analysis, and the simulation outcomes verify the high accuracy and generalization ability of the proposed model.

This paper presents a novel dynamic predictor-based event-triggered control strategy to investigate the mean-square exponential stabilization for a class of stochastic systems with time-varying output delay. First, a dynamic prediction scheme is introduced to deal with the problem of time delay, where the segmentation precision is no longer a constant, but a dynamically changing value. Second, an event-triggering mechanism with time regularization is proposed to avoid the Zeno phenomenon and guarantee the exponential convergence of the segmentation precision in the prediction scheme. Then, a predictor-based event-triggered control is designed to achieve the mean-square exponential stabilization of stochastic systems. Finally, a single-link robot model is given to show the effectiveness of the obtained results.

Occupational active cable-driven shoulder exoskeletons have many advantages compared to the traditional ones, but strong nonlinearity and complexity in modeling prevent the accurate torque transmission through Bowden cable, which is not conducive to its wide application. Besides, there is an urgent need to develop a more intelligent strategy for human-robot interaction. In this paper, to overcome the dependency on the Bowden cable model and achieve the assist-as-needed (AAN) function during the application of the shoulder exoskeleton, a force-velocity evaluation-based model-free sliding mode control (FVE-MFSMC) is proposed. The controller contains a force-velocity evaluation-based fuzzy admittance control (FVEFAC) strategy as the outer loop controller to determine the needed joint angle of the exoskeleton and a model-free super-twisting global terminal sliding mode control (MF-STGTC) as the inner loop controller to realize accurate motion control. In the outer FVEFAC, an admittance-based reference angle is generated to achieve the needed assistance level. Both the interaction force and the angular velocity of the arm are considered to be the evaluation elements of the subject's intention, and then fuzzy logic is applied to adjust the stiffness parameter based on the evaluated subject's intention. In the inner MF-STGTC, the ultra-local model is used to approximate the original complex system, which avoids the accurate modeling of the Bowden cable. Time-delay estimation is adopted to compensate for the lumped uncertainties, and a global fast terminal sliding mode control algorithm with super-twisting design is used to stabilize the system while minimizing the chattering problem in traditional terminal sliding mode control. Co-simulation experiments through MATLAB/Simulink and SolidWorks are carried out to demonstrate the performance of the proposed MF-STGTC and FVE-MFSMC, where the motion control can achieve the reduction percentage in root mean square error (RMSE) of tracking errors from 34% to 89% under fixed and time-varying Bowden cable disturbance.

In this article, the event-triggered adaptive fuzzy fault-tolerant consensus problem for switched nonlinear multiagent systems (SNMASs) with communication faults and state-dependent switchings is considered. To approximate the unknown nonlinear functions in the systems, an adaptive fuzzy control strategy is developed. To reduce the wastage of communication resources, the event-triggered control (ETC) method is used, and a novel ETC strategy independent of the subsystems' mode is designed. Moreover, state-dependent switching laws are designed using the convex combination technology and single Lyapunov function method to ensure the stability of all agents. Finally, to verify the effectiveness of the developed results, the proposed method is applied to a SNMAS with all unstabilizable subsystems.

An adaptive predefined-time optimal control method based on fuzzy logic system (FLS) is presented in this paper for strict-feedback nonlinear systems. The settling time of predefined-time control can be set in advance, which is independent of the design parameters and initial conditions. The optimal control relies on solving the Hamilton-Jacobi-Bellman (HJB) equation, which is difficult to calculate directly due to its inherent nonlinearity. To overcome this difficulty, the reinforcement learning (RL) strategy of actor-critic structure is used, where the actor and critic are used to achieve control behavior and evaluate system performance, respectively. In addition, the RL algorithm is simplified, and the two conditions of persistence of excitation and known dynamics required for the most optimal controls are eliminated. The main objective of this article is to ensure that the tracking error converges to a small neighborhood near the origin within a predefined time, while minimizing energy consumption. Finally, the efficiency of the suggested control strategy is demonstrated by using a practical simulation example.

This work aims to address the optimized formation fault-tolerant control issue by utilizing reinforcement learning (RL) for the single integral dynamic multi-agent system (MAS) having actuator faults. Because actuator faults have a direct effect on system performance and stability, it is essential to take the fault-tolerant mechanism as a design principle of nonlinear system control. Especially in the optimal control of MAS, actuator faults frequently occur due to real-time information exchange and high control performance requirements. To address the problem, the distributed RL and adaptive estimation are combined, where the RL algorithm is used to generate the optimized formation control protocol, and the adaptive learning is used to estimate the time-varying efficiency factor and bias signal in the faulty actuator model. Finally, it is demonstrated through theory and simulation that the proposed optimized control has the fault-tolerant capability and ensures system stability.