International Journal of Adaptive Control and Signal Processing最新文献

A pattern-moving-based probabilistic density evolution analysis and control framework is proposed for non-Newtonian mechanical systems, which are complex nonlinear systems governed by statistical regularities. Due to the high uncertainty in the output of non-Newtonian mechanical systems resulting from statistical regularities, pattern category variables are constructed to describe system changes over time instead of state or output variables. Furthermore, the operational properties of pattern category variables are measured by posterior probability density, and a system dynamic description of pattern motion probability density evolution is proposed. Combining pseudo-partial-derivative (PPD) with pattern-moving probability density evolution, a data-driven method is designed to predict the posterior probability density of system output pattern categories. Based on this, a statistical control strategy called pattern-moving probability density evolution control is further designed. Finally, an extended state observer (ESO) is used to design an estimation algorithm for PPD within the controller. The effectiveness of both the parameter estimation and control algorithms is validated through theoretical analysis, and the performance of the control system is verified through numerical simulation.

This work is focused on the neural adaptive output feedback control of decentralized uncertain nonlinear systems subject to bounded disturbance, unmodeled dynamics, and uncertainties. Specifically, pervasive and general decentralized systems are studied, and auxiliary variables are constructed to account for the unmodeled dynamics and the uncertainties associated with high-order interconnection terms. Neuro-observer is introduced to obtain the unmeasured states online, and output feedback adaptive control is implemented. Then, adaptive dynamic surface controller is designed by Lyapunov method and the problem of “explosion of complexity” in traditional backstepping method is eliminated. By initialization technique, not only the ultimately uniformly boundedness of all closed-loop signals is obtained but also the transient performance of tracking errors can be guaranteed in the sense of $��{�L�}_{�\infty �}�$ norm. Numerical and practical simulation examples are performed to validate the effectiveness of the proposed control scheme.

In this article, a fuzzy finite-time inverse optimal control (IOC) problem is considered for strict-feedback stochastic nonlinear systems (SNSs). Fuzzy logic systems (FLSs) are employed to estimate the nonlinear uncertainties that exist in the considered system. The controller design method proposed in this article not only simplifies the control scheme, but also solves the singularity problem that occurs in traditional control techniques. Especially, an improved lemma about IOC is presented, which enables the controlled system to converge in probability and minimize the cost function within finite time. By utilizing the improved finite-time inverse optimal lemma and the backstepping control strategy, a novel adaptive fuzzy finite-time IOC protocol is obtained. It can be guaranteed that all states are bounded under the proposed controller. Moreover, the tracking error converges to an interval near the origin within finite time, and the given cost function can also be optimized in finite time. Finally, both numerical and practical examples are supplied to verify the efficiency and effectiveness of the proposed algorithm.

In this article, an adaptive tracking control scheme of deep-stall recovery is proposed for the fixed-wing unmanned aerial vehicle (UAV) with external disturbances and system uncertainties. By analyzing the phase plane of the longitudinal static instability UAV system, the causes and the recovery conditions of the deep-stall are given. Considering the system uncertainties, the external disturbances, and input saturation, a practical prescribed-time (PPT) deep-stall recovery controller is designed by using the time-varying multi-constraints, which contain three layers: Prescribed performance functions (PPFs), actual constraints, and virtual constraints. The new PPT saturation disturbance observer (SDO) and neural networks are employed to deal with the external disturbances and the system uncertainties, respectively. Moreover, the problem of “explosion of complexity” and input saturation is solved by introducing a command filter and an auxiliary system. Then, a new PPT time-varying barrier Lyapunov (PPT-TVBLF) is presented to guarantee the closed-loop system stability under the deep-stall recovery process. Furthermore, simulation study results are given to illustrate the validity of the proposed deep-stall recovery control scheme.

This study addresses issues of finite-time control for a class of discrete-time nonlinear systems with previewable reference signals via observer-based control. An observer-based tracking controller with preview actions was designed to realize enhanced finite-time tracking performance when considering unavailable state variables. First, an equivalent model, that is, the augmented error system, was constructed via the error system approach in preview control theory. This transformed the finite-time preview tracking control problem into a finite-time stability problem. Subsequently, novel finite-time stabilization criteria were obtained for the underlying systems over the complete finite-time interval. We further designed the observer-based controller and analyzed two numerical examples to illustrate the effectiveness and merits of the proposed method.

This work proposes a Primal-Dual Neural Network (PDNN) based Robust Model Predictive Control (RMPC) framework to address trajectory tracking challenges for quadrotor unmanned aerial vehicles (UAVs) under unmodeled dynamics, external disturbances, and nonlinearities. Firstly, a polytopic uncertain model capturing system uncertainties and external disturbances is established. A cascade control strategy is then introduced to decouple underactuated dynamics and mitigate nonlinear coupling effects. An RMPC design with a model-uncertainty compensation strategy is introduced to reject the model uncertainties. The compensation-based RMPC problem is formulated as a quadratic programming (QP) problem, and a PDNN optimization method is developed to ensure fast computation solutions for real-time control. The input-to-state stability (ISS) of the closed-loop system under PDNN-RMPC is rigorously proven. Experimental results demonstrate that the proposed PDNN-RMPC framework achieves a $��41�.�28�\%�$ reduction in computation time compared to the interior-point method. Furthermore, under varying external disturbances, compared with existing tube-based MPC and Recurrent Neural Network (RNN)-MPC, the proposed PDNN-RMPC respectively improves the average of 4.23% and 3.25% for position tracking accuracy, by 38.70% and 39.17% for attitude tracking accuracy. These results highlight the ability of the proposed PDNN-RMPC in balancing real-time performance, robustness, and energy efficiency, providing a practical solution for quadrotor UAV flight scenarios.

This article studies the problem of finite impulse response (FIR) system identification with binary sensor and the either-or communication, together with the packet drop for quantized inputs. We propose online identification algorithms for two cases of known and unknown probability of packet drop, and prove strong convergence and asymptotic normality of the algorithms. Additionally, we introduce metrics to describe the speed of the algorithm's convergence when the probability of packet drop is unknown. Finally, the effectiveness of the theoretical results is verified by experiment.

In this article, a fixed-time periodic adaptive event-triggered control for a robotic manipulator is proposed. Combined with the backstepping framework and finite-time control theory, an adaptive fixed-time controller is constructed to achieve the fast convergence performance of the robotic manipulator while maintaining system stability. Besides, a radial basis function neural network (RBFNN) is employed to approximate the uncertainties of the system. Further, Practical robotic applications frequently encounter critical challenges related to limited communication resources, especially during the execution of complex tasks. In contrast to the traditional event-triggered control (ETC) mechanisms that require continuous system monitoring, a novel periodic adaptive event-triggered control (PAETC) is proposed to schedule control signal transmission. The innovative PAETC strategy not only preserves system performance but also substantially reduces both communication burdens and continuous monitoring requirements, thereby enhancing practical implementability. Finally, both theoretical analysis and simulations are conducted to demonstrate the validity of the developed method.

This paper addresses the tracking control problem for a class of constrained parametric nonlinear systems. By employing a constructed nonlinear mapping (NM), the system with time-varying parameters and extended full-state constraints is transformed into an unconstrained nonlinear system. Subsequently, the controller and adaptive law are designed using a modified dynamic surface control (DSC) approach. At each stage of the controller design, a compensating signal is introduced to mitigate the error resulting from the substitution of the linear filter's output for the derivative of the virtual control. This methodology reduces the difficulty of controller design and the complexity of stability analysis. The proposed control algorithm ensures the superior tracking performance while adhering to the extended full-state constraints, and guarantees that all signals are semi-global uniformly ultimately bounded (SGUUB). The effectiveness of the proposed control strategy is validated through stability analysis and numerical simulations.