Search

In this paper, the problem concerned with adaptive approximation-based control is discussed for a class of stochastic pure-feedback nonlinear time-delay systems with unknown direction control gains and full-state constraints. In the controller design process, the approximation capability of neural networks is utilized to identify the unknown nonlinearities, the appropriate Lyapunov–Krasovskii functionals are constructed to compensate the unknown time-delay terms, barrier Lyapunov functions (BLFs) are designed to ensure that the state variables are constrained, and the Nussbaum-type gain function is used to solve the difficulties caused by the unknown virtual control gains. Then, based on adaptive backstepping technique and Lyapunov stability theory, a robust control scheme is presented, and the developed controller decreases the number of learning parameters and thus reduces the computational burden. It is shown that the proposed controller can guarantee that all the signals in the closed-loop system are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a compact set of the origin. Finally, two simulation examples are included to validate the effectiveness of the proposed approach.

This paper investigates an adaptive neural tracking control problem for a class of multi-input multi-output (MIMO) stochastic nonlinear pure-feedback systems with input saturation and full-state constraints. First, the implicit function and mean value theorems are used to overcome the difficulty in the control of pure-feedback systems. Second, the Gaussian error function is employed to represent a continuous differentiable asymmetric saturation model, the neural network is employed to approximate the unknown nonlinearity, and a barrier Lyapunov function is designed to ensure that the full-state variables are restricted. At last, based on Lyapunov stability theory, a robust adaptive neural control method is obtained, which decreases the number of learning parameters and thus reduces the computational burden. It is shown that the designed controller can guarantee that all the signals in the closed-loop system are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. The simulation example is given to further illustrate the effectiveness of the proposed approach.

This paper studies an adaptive neural tracking control problem for a class of strict-feedback stochastic nonlinear systems with guaranteed predefined performance subject to unknown backlash-like hysteresis input. First, utilizing the prescribed performance control, the predefined tracking control performance can be guaranteed via exploiting a new performance function without considering the accurate initial error. Second, by integrating neural network approximation capability into the backstepping technique, a robust adaptive neural control scheme is developed to deal with unknown nonlinear functions, stochastic disturbances and unknown hysteresis input. The designed controller overcomes the problem of the over-parameterization. Under the proposed controller, all the signals in the closed-loop system are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded (SGUUB), and the prespecified transient and steady tracking control performance are guaranteed. Simulation studies are performed to demonstrate and verify the effectiveness of the proposed method.

This paper proposes a novel method for the electrocardiographic (ECG) beat classification via deterministic learning. The dynamics of ECG beats is used as a unique feature for ECG beat classification, which is fundamentally different from the time/frequency domain features used in literature. It is the essential feature of ECG beats, and contains complete information of ECG beats. Precisely, the deterministic learning allows us to model and represent the dynamics of a training beat set as constant radial basis function (RBF) networks. As the classification measure, a set of errors is further obtained through the comparison between the test beat and the estimators constructed by the RBF networks. ECG records taken from the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) arrhythmia database are selected to test the proposed method. With 5% beats used as training beats, the overall accuracies are 97.78% and 97.21% for global and patient-adapting beat classification, respectively. These results indicate the proposed method is reliable and efficient for ECG beat classification.