Search

In this article, a new data‐based iterative learning control (ILC) algorithm is proposed via Gaussian process regression (GPR) to accomplish the trajectory tracking objective of aircraft subject to completely unknown dynamics and strong nonlinearities. The nonlinear system input–output relationship of the unknown aircraft is formulated through GPR by leveraging historical data, based on which an optimal ILC framework is established. The monotonic convergence analysis of the GPR‐based ILC is explored such that high‐precision tracking tasks can be accomplished without prior model knowledge. Simulation tests are further conducted on a commercial aircraft performing a continuous climb operation to illustrate the effectiveness of the GPR‐based ILC approach. [ABSTRACT FROM AUTHOR]

This article concentrates on the challenging adaptive tracking problem of multi‐input and multi‐output (MIMO) nonlinear systems with unknown nonlinear dynamics, for which a novel optimization‐based data‐driven adaptive control (ODDAC) equipped with an extended dynamic linearization method is developed. Through considering MIMO nonlinear systems in the fully‐actuated case and over‐actuated case separately, the ODDAC is proposed consisting of a parameter updating algorithm and an adaptive control law. A new design of parameter updating algorithm is presented such that the estimation can be guaranteed to be bounded by a strict contraction process. By leveraging properties of the nonnegative matrix, a grouping‐based contraction mapping (GCM) analysis method is proposed for the convergence of tracking error. Notably, the GCM does not require the contraction mapping condition to hold at all time instants. The proposed ODDAC is data‐based, avoiding reliance on model information, and its validity is verified through simulations. [ABSTRACT FROM AUTHOR]

This work investigates the robust lateral tracking control problem of autonomous vehicles subject to unmodeled system uncertainties, external disturbances as well as input constraints that commonly exist in the vehicle dynamics. The tracking controller design under such uncertain environments is challenging due to the under‐actuated characteristics of the vehicle dynamics. Targeting at this issue, a nonlinear vehicle model is firstly established and transformed into a novel parametric model to facilitate the controller design. A robust adaptive learning control (RALC) approach is then proposed based on the parametric vehicle model, where an input‐dependent auxiliary system is employed to compensate the influence of the input constraints. The convergence of the tracking errors is rigorously analyzed based on the framework of composite energy function. The proposed RALC scheme is proven to be able to achieve an impressive path tracking performance under perturbed and constrained scenarios. Moreover, the proposed technique in tackling the under‐actuated dynamics is novel that can be applied to deal with other generic non‐square systems. The proposed controller is validated with case studies under various conditions. [ABSTRACT FROM AUTHOR]

We report a ligand‐free copper‐catalyzed 1,6‐addition of α‐borylalkyl copper species, catalytically generated from gem‐diborylalkanes to para‐quinone methides. The reaction proceeds with good yield for a variety of para‐quinone methides and substituted gem‐diborylalkanes with a catalytic amount of CuI as catalyst. This catalytic system provides a cost‐effective process with good functional group tolerance under mild conditions. [ABSTRACT FROM AUTHOR]

Herein, we report the catalytic 1,6‐addition of chiral alkyl copper nucleophiles, in‐situ generated from borylalkenes and a copper‐hydride catalyst under mild conditions. Chemo‐ and stereoselective control was crucial in this reductive coupling process of borylalkenes and para‐quinone methides, sequentially forming adjacent stereogenic centers with good diastereo‐ and enantioselectivity. Through the multicomponent and catalytic tandem reactions, asymmetric 1,6‐adducts of p‐quinone methides were efficiently synthesized. [ABSTRACT FROM AUTHOR]

This article is concerned with how the effect of time-varying topologies is overcome and the behaviors of cooperative–antagonistic networks (CANs) are identified. An extended leader–follower (ELF) framework is established for CANs, in which each leader is allowed to evolve dynamically due to the communication with its neighbor leaders. It is shown that a new class of bipartite containment fluctuation behaviors emerges in the presence of the ELF framework, regardless of any structure conditions for CANs. In particular, the leaders are clustered into separate groups, each of which can realize the modulus consensus, whereas the followers may not be enabled to converge but fluctuate within the bounded region spanned by all leaders’ states and their symmetric states. A simulation example is provided to demonstrate the effectiveness of the behavior analysis results developed for CANs. [ABSTRACT FROM AUTHOR]

Mode information is of great significance when investigating the Markov jump systems (MJSs). However, it is common in practical scenarios that the mode information is not completely accessible, which probably induces nonsynchronization problems. Taking this into consideration, in this article, we study nonsynchronous $\mathcal H_{\infty }$ model order reduction for 2-D MJSs with model uncertainty. The considered 2-D system and reduced-order model are characterized by the Roesser model. The nonsynchronization phenomenon between the original system and the reduced-order model is dealt with under the framework of the hidden Markov model. By appropriately selecting the Lyapunov function, the asymptotic mean-square stability and the $\mathcal H_{\infty }$ performance of the error system are analyzed, and sufficient conditions are proposed. Based on this, an efficient design method for nonsynchronous model order reduction is further proposed with the help of a projection lemma. Finally, the correctness and effectiveness of the designed reduced-order model are verified through some simulations. [ABSTRACT FROM AUTHOR]