An adaptive neural feedback tracking control scheme is presented for a class of multi-input multi-output nonlinear minimum phase systems with uncertainties and external disturbances. Gaussian basis RBF neural networks are used to approximate the plant unknown nonlinearities, and a high-gain observer is used to estimate the states which can not be measured. The proposed controller can guarantee ...

This paper studies the resilience-based output containment control problem of heterogeneous multi-agent systems in presence simultaneous false data injection attacks on both sensors and actuators injected cyber physical layers. In real-world security problems, malicious adversaries could inject simultaneously, even with unbounded injections, to maximize their damage systems. To address such a p...

This article addresses the crucial requirements in spacecraft attitude control: prescribed performance guarantees under actuator saturation and real-time cost optimization. As an application-oriented study, approximate optimal control scheme is proposed for this objective. To be specific, constraint converted into system dynamics merged adaptive dynamic programming design philosophy. Subsequent...

This article focuses on the trajectory-tracking of an underactuated surface vehicle (USV) considering model uncertainties and nonlinear environmental disturbances. For trajectory tracking in actual USV sailing environment, both inertia damping matrixes are not diagonal, velocities states unmeasurable, error constraints input saturation considered. A robust control strategy is proposed based bac...

In this paper, a direct adaptive fuzzy tracking control scheme is presented for a class of stochastic uncertain nonlinear systems with unknown dead-zone input. A direct adaptive fuzzy tracking controller is developed by using the backstepping approach. It is proved that the design scheme ensures that all the error variables are bounded in probability while the mean square tracking error becomes...

A novel robust adaptive controller is presented for a wide class of strict feedback uncertain nonlinear system with unknown virtual control coefficients under bounded exogenous disturbances. Combined Nussbaum gain with the backstepping technique, the proposed design algorithm, which does not require a priori knowledge of the signs of the unknown virtual control coefficients, is proved to be abl...

In this paper, a direct adaptive fuzzy tracking control scheme is presented for a class of stochastic nonaffine uncertain nonlinear systems with unknown dead-zone input. Based on the first-type fuzzy logic system’s online approximation capability, a direct adaptive fuzzy tracking controller is developed by using the backstepping approach. It is proved that the design scheme ensures that all the...

In this paper, a neural-network-based control scheme is developed for the tracking control problem of a class of switched strict-feedback nonlinear systems with uncertain input delay and external time-varying disturbances. First, the auxiliary signals are obtained by masterly constructing a filter and a virtual observer. Then the adaptive backstepping technique and neural network (NN) are emplo...

In Part I of this paper, [1], we have developed a novel L1 adaptive control architecture that enables fast adaptation and leads to uniformly bounded transient and asymptotic tracking for system’s both signals, input and output, simultaneously. In this paper, we derive the stability margins of L1 adaptive control architecture, including time-delay and gain margins in the presence of time-varying...