International Journal of Adaptive Control and Signal Processing最新文献

This article presents a novel Adaptive Double Integral-Type Second-Order Terminal Sliding Mode (ADITSOTSM) control policy for favorable and swift stabilization of the disturbed uncertain Cyber-Physical Systems (CPSs) in finite time. The offered double integral-type switching surface elides the approaching step and meliorates the system's robustness. Real-time adaptive laws are expanded for coping with unwanted disturbances, parametric uncertainties, and cyber attacks on sensors and actuators, so that their upper bounds designation is not indispensable. The recommended strategy guarantees the disturbed uncertain CPSs robust functionality against rigorous cyber attacks on sensors and actuators. In addition, it provides swift response, smooth and robust control, high carefulness and more flexible operation, finite time convergence, without chattering, and no transient fluctuations. Comparing the simulation results with conventional, adaptive integral-type sliding mode, and state-feedback control depicts the introduced technique's high success and effectiveness.

In this paper, we present a new scheme to design an adaptive neural backstepping tracking controller for a class of stochastic nonlinear systems with unknown input constraints including saturation and dead-zone. The control design is achieved by using certain auxiliary techniques. More specifically, some piecewise injective differential functions are constructed to approximate these nonlinearity constraints with a bounded approximation error. A new dimension reduction inequality related the norm of basis vectors of radial basis functions is established to design the virtual signals. Some negative power exponential functions and the inverse functions of the above injective differentiable functions are introduced to design the adaptive laws and controller, respectively. These techniques can enlarge the nonlinear systems currently studied by backstepping approach and neural networks. Furthermore, it is shown that all the signals in the closed-loop system are semi-globally uniformly, ultimately bounded in probability and the tracking error converges to zero. Meanwhile, the effectiveness of the proposed controller is demonstrated in the simulation study.

This article investigates the problem of event-triggered output feedback for a class of uncertain nonlinear time-delay systems. Essentially different from previous works, the system nonlinearities satisfy certain bounded condition depending on an unknown growth coefficient, the input–output function, the unmeasurable states, and an unknown time-varying delay. The complicated nonlinearities pose a substantial challenge in solving this problem. Employing the dynamic gain technique, we construct a new adaptive event-triggered controller via output feedback, where the event-triggering mechanism is a combination of event-triggered and time-triggered. We prove that all states of the concerned system globally converge to zero, and Zeno phenomenon is excluded. We further extend the results to nonlinear time-delay systems with input matching uncertainty. Numerical examples are given to verify the validity of the theoretical results.

This paper focuses on the parameter estimation problem for pseudo-linear systems with autoregressive moving average noise. In order to reduce the computational complexity of the identification algorithms, the original pseudo-linear system is decomposed into three submodels and a three-stage auxiliary model-based recursive generalized extended least squares (3S-AM-RGELS) algorithm is proposed based on the hierarchical identification principle. The convergence analysis is provided to show that the parameter estimation error can converge to zero under the presented 3S-AM-RGELS algorithm. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithms.

Sensor fault detection and isolation in multi-agent systems (MAS) with uncertain dynamics and undirected, connected communication networks is addressed in this article. The proposed approach involves a two-step process: First, fault detection, and then likelihood-based fault isolation. A novel fault reconstruction technique is introduced by tuning the unscented Kalman filter (UKF) noise covariance matrices within the Q-learning framework. This adjustment helps reconstruct the uncertain states of the MAS and train the internal parameters of a neural network using historical measurements. This innovative method is referred to as Enhanced reinforced UKF (ERUKF). To reduce neural network approximation errors, a robust control term utilizing the hyperbolic tangent function is applied. The stability of ERUKF, when combined with the robust control method, is mathematically proven using the Lyapunov theorem. Simulations illustrate that ERUKF exhibits lower estimation errors compared to adaptive UKF, achieving a 96.67% success rate in fault isolation under Monte Carlo (MC) simulations.

For stochastic nonlinear time-delay systems with full-state constraints and input saturation, the existing control methods cannot be directly applied. To solve the control problem, an adaptive tracking control scheme is proposed here. The barrier Lyapunov function (BLF) and Lyapunov-Krasovskii functional (LKF) are combined in a unified framework. The former is to prevent the violation of state constraints and the latter is to address the impact of time delay. A suitable auxiliary system with the same order as the considered system is constructed to compensate for input saturation. Moreover, the multi-dimensional Taylor network (MTN) is introduced to estimate unknown nonlinear functions in the process of controller design. With the help of the Lyapunov stability theorem and the backstepping technique, an adaptive MTN tracking controller is developed to ensure that all the closed-loop signals remain bounded in probability and that the system state constraints are never violated while the output signal tracks the reference one successfully. Simulation examples with three types of reference signals ($��{�y�}_{�d�}�=�0�.�5�\sin �\left(�2�t�\right)�$, $��{�y�}_{�d�}�=�0�.�9�$ and the square reference signal) are provided to verify the validity of the designed control approach.

This article proposes a fixed-time adaptive tracking control strategy for a class of high-order non-strict feedback nonlinear systems with asymmetric output constraints. The key technique of this strategy is to guarantee that the output of the system does not break the pre-set constraints through the barrier Lyapunov function (BLF) while approximating any unknown nonlinear term through the fuzzy logic systems (FLSs). We design a fixed-time fuzzy adaptive controller to ensure that the closed-loop error signal converges to a tunable neighborhood near zero in a fixed-time instant. Compared with the existing results, the fixed-time adaptive control is realized and the semi-global fixed-time stability of the closed-loop system is proved theoretically. Finally, the effectiveness of the proposed scheme is verified by a numerical simulation.

An adaptive preset performance tracking control scheme is proposed for a class of uncertain strict feedback stochastic nonlinear systems with full state constraints and input dead zones. A new coordinate transformation and obstacle Lyapunov function are adopted to solve the problem of full state constraints and preset performance. The nonlinearity of the input dead zone is solved by splitting the dead zone nonlinear term into a linear term and a bounded disturbance term. A fuzzy logic system is used to approximate the unknown function in the system. A command filter is introduced to solve the “differential explosion” problem, and a compensation signal is used to compensate for the filtering error. An adaptive command filter controller is designed using the adaptive backstepping method and stochastic stability theory, which can ensure that all the closed-loop signals are ultimately bounded with semiglobal consistency in terms of the probability of quarter moments of an appropriate compact set. Meanwhile, the tracking error satisfies the predefined performance and converges to a small neighborhood close to zero. Simulation results show the effectiveness of the method.