International Journal of Adaptive Control and Signal Processing最新文献

This study focuses on the terminal angle constrained interception of maneuvering target by missile in three-dimensional (3D) coupled environment. Considering actuator failures and input saturation, an adaptive non-singular terminal sliding mode guidance law is proposed based on the predefined-time convergence theory. Its design features an adjustable convergence time parameter. First, an adaptive guidance law is developed, the states can converge to zero in the predefined time under ideal conditions. Subsequently, based on this foundation, another anti-saturation fault-tolerant guidance law is developed, which can realize the interception of targets in scenarios involving actuator failure and input saturation. Comprehensive simulation experiments validate the efficacy of the proposed algorithms.

A novel 6-DOF cooperative guidance and control law against a maneuvering target is proposed under full state constraints and non-ideal communication conditions. Taking the impact time and impact angle into account, a comprehensive integrated guidance and control (IGC) model with thrust is formulated. The user-defined flight limitations are well tackled by incorporating a nonlinear mapping function into the IGC dynamic, which brings about a fully state-constraint-free IGC model. Considering a multi-interceptor balanced system under switching topology and time-varying delay, a Lyapunov–Krasovskii function-based sufficient criterion is proposed, based on which an acceleration command in the direction of the line of sight (LOS) is generated to guarantee a simultaneous interception. The backstepping control technique combined with the fixed-time disturbance observer is implemented to formulate a robust fin deflection command in the normal direction of LOS. The overall desired transit and steady guidance performance is greatly enhanced by designing a novel prescribed performance control method with a self-adjusting mechanism. The simulation results show the effectiveness of the proposed cooperative guidance law.

In this article, a distributed secure filtering problem is studied for a class of discrete time-varying multi-rate systems with stochastic nonlinearities subject to false data injection (FDI) attacks over wireless sensor networks (WSNs). The state iteration method is employed to convert the multi-rate system to a single-rate one due to asynchronous state updates and sensor sampling cycles. In order to counter the malicious attacks, a detector featuring an adaptive decision rule is developed for each sensor and a compensation strategy is established based on a memory mechanism. The main objective of this article is to develop a distributed filter for addressing the multi-rate sampling scheme, hostile attacks and stochastic nonlinearities. An upper bound of the filtering error covariance is deduced via mathematical induction and the filter gains are formulated by minimizing this upper bound. Subsequently, a sufficient condition is provided to guarantee the uniform boundedness of the filtering error. Finally, illustrative simulations including comparative experiments are implemented to demonstrate the effectiveness of the proposed distributed filtering algorithm with a memory-based compensation strategy.

This article proposes a fixed-time prescribed performance controller with an adaptive disturbance observer to enhance disturbance rejection and dynamic performance in a permanent magnet synchronous motor (PMSM). Firstly, an adaptive control law designed using a fixed-time prescribed performance function ensures transient behavior and steady-state performance strictly adhere to preset boundary constraints. Secondly, a fixed-time command filter resolves the repeated differentiation issue of virtual control signals in backstepping, while an error compensation signal further improves tracking accuracy. Friction effects and unknown load torque are treated as lumped disturbances, with a novel adaptive disturbance observer developed to suppress these disturbances, thereby improving system robustness. Finally, simulations and experiments validate the algorithm's effectiveness.

Time-varying uncertainties, often with unknown bounds, are ubiquitous in mechanical systems. The lack of a priori knowledge of these uncertainties impedes the evaluation of their effects on system performance, leading to deteriorating control precision and potential instability. To address this challenge, this study presents an adaptive robust model predictive control (ARMPC) strategy and a control switching mechanism to tackle such uncertainties, whether from internal or external origins. First, the nominal system is decoupled from the uncertain components. A feedforward compensation term based on Udwadia-Kalaba approach and an optimal feedback term from the receding horizon control framework are elaborated to achieve rapid convergence, coupled with an uncertainty rejection component to guarantee the uniform boundedness and uniform ultimate boundedness of the closed-loop system. Second, an adaptive robust controller satisfying the input saturation constraint is proposed, serving as a viable alternative when the optimization problem is infeasible. Finally, the efficacy of the ARMPC scheme is demonstrated through simulations of a two-link R-R mechanical manipulator system and a path tracking example, showcasing its superior performance in terms of convergence rate, overshoot, and steady-state error under different levels of uncertainties.

The synchronization control problem of coupled neural networks based on a proportional-integral observer is addressed in this paper. Since the state vector of each node may not be measured directly, a proportional-integral observer is established to estimate the state information of the plant, thus improving the design method of the observer. In addition, considering the limited communication bandwidth, the coding-decoding scheme is adopted to relieve the bandwidth pressure and improve transmission efficiency effectively. The decoding state obtained by the coding-decoding scheme is used as the basis for the designed controller, and it is deduced how the upper bounds of the coding-decoding scheme parameters relate to the exponential ultimate boundedness of the synchronization error system. Finally, an example is offered to demonstrate the validity of the proposed method.

In this paper, an adaptive fuzzy output feedback control method is proposed for stochastic quadrotor unmanned aerial vehicles, which accounts for output constraints and unknown nonlinear functions. In the results of traditional finite-time stability studies, the control strategy often fails to converge faster than the exponential rate when the initial state is far from the origin. To overcome this issue, we present a novel stochastic fast finite-time stable controller for quadrotor unmanned aerial vehicles. During the design process, the output constraint problem is addressed by constructing a barrier Lyapunov function. The design leverages a barrier Lyapunov function to handle output constraints and utilizes the fuzzy logic system to model and approximate nonlinearities. Furthermore, a fuzzy logic system observer is designed based on an adaptive backstepping approach to estimate states that cannot be directly measured. Simulation results demonstrate that the proposed control method ensures fast finite-time stability in probability for the quadrotor system. The validity of the presented scheme is confirmed by simulation results.

This article focuses on an unresolved sampled-data stabilizing control problem pertaining to a concerned nonlinear system with logarithmic quantized input and input unmodeled dynamics. The M-filters are employed to handle the input unmodeled dynamics, while the radial basis function neural networks (RBFNNs) realize the approximation for existing nonlinearities in the backstepping design procedure. Under the appropriate choice of Lyapunov function candidates (LFCs), the analysis demonstrates that the resulting sampled-data stabilizer enables the formulated closed-loop system to have practically asymptotic stability through selecting suitable parameters and an allowable sampling period. Simulation results, which are exported from a power system, reflect the effectiveness of the developed control scheme.