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In this article, the adaptive neural backstepping control approaches are designed for uncertain stochastic nonlinear systems with full-state constraints. According to the symmetry of constraint boundary, two cases of controlled systems subject to symmetric and asymmetric constraints are studied, respectively. Then, corresponding adaptive neural controllers are developed by virtue of backstepping design procedure and the learning ability of radial basis function neural network (RBFNN). It is worth mentioning that the integral Barrier Lyapunov function (IBLF), as an effective tool, is first applied to solve the above constraint problems. As a result, the state constraints are avoided from being transformed into error constraints via the proposed schemes. In addition, based on Lyapunov stability analysis, it is demonstrated that the errors can converge to a small neighborhood of zero, the full states do not exceed the given constraint bounds, and all signals in the closed-loop systems are semiglobally uniformly ultimately bounded (SGUUB) in probability. Finally, the numerical simulation results are provided to exhibit the effectiveness of the proposed control approaches.

This article investigates the model-free containment control of multiple underactuated unmanned surface vessels (USVs) subject to unknown kinetic models. A novel cooperative control architecture is presented for achieving a containment formation under switching topologies. Specifically, a path-guided distributed containment motion generator (CMG) is first proposed for generating reference points according to the underlying switching topologies. Next, guiding-vector-field-based guidance laws are designed such that each USV can track its reference point, enabling smooth transitions during topology switching. Finally, data-driven neural predictors by utilizing real-time and historical data are developed for estimating total uncertainties and unknown input gains, simultaneously. Based on the learned knowledge from neural predictors, adaptive kinetic control laws are designed and no prior information on kinetic model parameters is required. By using the proposed method, the fleet is able to converge to the convex hull spanned by multiple virtual leaders under switching topologies regardless of fully unknown kinetic models. Through stability analyses, it is proven that the closed-loop control system is input-to-state stable and the tracking errors are uniformly ultimately bounded. Simulation results verify the effectiveness of the proposed cooperative control architecture for multiple underactuated USVs with fully unknown kinetic models.

This paper studies the fuzzy adaptive output feedback control design problem for non-strict feedback multi-input multi-output (MIMO) nonlinear systems with full-states prescribed performance in finite-time. Fuzzy logic systems (FLSs) are introduced to solve the problem of unknown nonlinear dynamics. And on this basis, a fuzzy-based state observer is designed to observe the unmeasurable state. Further, by combining adaptive back-stepping control algorithm and the nonlinear filters, a novel dynamic surface control (DSC) method is proposed, which not only solves the computational complexity explosion the problem inherent in the back-stepping control algorithm, but also improves the control performance, in contrast to traditional DSC methods with linear filters. Besides, to further improve the tracking performance under the structure of full states prescribed performance, a new Lyapunov function is designed considering the transform error constraint. Based on Lyapunov theory, the stability of the closed-loop system is analyzed to ensure that all signals of the closed-loop system are semi-global practical finite-time stability (SGPFS). Finally, to elaborate on the feasibility and effectiveness of the proposed control method, a simulation example is provided.

In this paper, we study the adaptive fuzzy decentralized control problem for the fractional-order nonlinear large-scale systems in strict-feedback form. The controlled systems contain unknown nonlinearities. In the process of recursive design, the global approximation ability of fuzzy logic systems (FLSs) are utilized to cope with the unknown nonlinear continuous functions of the considered systems. In addition, in order to avoid the issue of “explosion of complexity” inherent in the traditional backstepping control design technique, the dynamic surface control (DSC) technique is introduced into the control design. Under the frameworks of the backstepping control design technique, DSC technique and adaptive fuzzy decentralized control theory, an adaptive fuzzy decentralized DSC scheme is put forward. On the basis of the fractional-order Lyapunov stability theory, it is confirmed that all the signals in the controlled systems are bounded and the tracking errors are able to converge to a small neighborhood of the origin.