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]

Learning systems represent a particularly important class of practical data-driven systems that adapt to their environment based on the environment's response to the system's action. Despite the success of learning-based methods, finding suitable control frameworks for learning systems when there is uncertainty in the assumptions related to the system dynamics is still an open problem. They propose a novel constrained ILC design and further develop a decentralized implementation of the resulting ILC algorithm using the alternating direction method of multipliers, allowing the design to scale up to handle large-scale and varying system dynamics. [Extracted from the article]

This paper deals with the high-precision consensus seeking problem of multi-agent systems when they are subject to switching topologies and varying communication time-delays. By combining the iterative learning control (ILC) approach, a distributed consensus seeking algorithm is presented based on only the relative information between every agent and its local (or nearest) neighbors. All agents can be enabled to achieve consensus exactly on a common output trajectory over a finite time interval. Furthermore, conditions are proposed to guarantee both exponential convergence and monotonic convergence for the resulting ILC processes of multi-agent consensus systems. In particular, the linear matrix inequality technique is employed to formulate the established convergence conditions, which can directly give formulas for the gain matrix design. An illustrative example is included to validate the effectiveness of the proposed ILC-motivated consensus seeking algorithm. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Arbitrary high precision is considered one of the most desirable control objectives in the relative formation for many networked industrial applications, such as flying spacecrafts and mobile robots. The main purpose of this paper is to present design guidelines of applying the iterative schemes to develop distributed formation algorithms in order to achieve this control objective. If certain conditions are met, then the control input signals can be learned by the developed algorithms to accomplish the desired formations with arbitrary high precision. The systems under consideration are a class of multi-agent systems under directed networks with switching topologies. The agents have discrete-time affine nonlinear dynamics, but their state functions do not need to be identical. It is shown that the learning processes resulting from the relative output formation of multi-agent systems can converge exponentially fast with the increase of the iteration number. In particular, this work induces a distributed algorithm that can simultaneously achieve the desired relative output formation between agents and regulate the movement of multi-agent formations as desired along the time axis. The illustrative numerical simulations are finally performed to demonstrate the effectiveness and performance of the proposed distributed formation algorithms. [ABSTRACT FROM AUTHOR]

This study considers an iterative learning control approach to achieve accurate coordination performances of the output data sequences for multiple plants that are involved in a networked environment. To realise such a desirable control objective, an update process of the input data sequence is needed to refine its output performance iteratively for each plant, which uses the local or nearest neighbour knowledge. The nominal multi‐agent systems are employed as the plants' description, for which input–output data‐driven consensus problems are addressed in a hybrid networked environment given by signed directed graphs with both cooperative and antagonistic interactions. It is proved that the output data can be guaranteed to achieve bipartite consensus or remain stable for the multi‐agent networks under structurally balanced or structurally unbalanced signed graphs. Moreover, the convergence conditions are derived, which need less knowledge of the agents' plant, and the proposed consensus results can be developed to take into account the plant uncertainties and noises. Simulation tests are performed to verify the effectiveness of the learning approach in refining high input–output data‐driven consensus performances of networked agents. [ABSTRACT FROM AUTHOR]

This study considers the convergence analysis approach to iterative learning control (ILC) which is achieved based on two‐dimensional (2D) Roesser systems. Stability results are proposed for 2D Roesser systems when they are subject to varying parameters with respect to independent time and iteration axes. It is shown that the convergence analysis of ILC for a class of non‐linear systems can be performed based on the established stability results of varying 2D Roesser systems. Moreover, the presented convergence results of ILC can work with sufficient robustness against iteration‐varying initial state shifts. Illustrative simulations are included to verify the established convergence results of ILC for non‐linear systems. [ABSTRACT FROM AUTHOR]

SUMMARY This paper deals with formation control problems for multi-agent systems by using iterative learning control (ILC) design approaches. Distributed formation ILC algorithms are presented to enable all agents in directed graphs to achieve the desired relative formations perfectly over a finite-time interval. It is shown that not only asymptotic stability but also monotonic convergence of multi-agent formation ILC can be accomplished, and the convergence conditions in terms of linear matrix inequalities can be simultaneously established. The derived results are also applicable to multi-agent systems that are subject to stochastic disturbances and model uncertainties. Furthermore, the feasibility of convergence conditions and the effect of communication delays are discussed for the proposed multi-agent formation ILC algorithms. Simulation results are given for uncertain multi-agent systems to verify the theoretical study. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study aims to develop an iterative learning control (ILC) approach to solving finite‐time output consensus problems of multi‐agent systems. The communication topologies among agents are considered to dynamically change in two directions (along both time axis and iteration axis), for which a framework is presented to construct effective distributed protocols. It is shown that a protocol can be derived through ILC to enable multi‐agent systems to accomplish the finite‐time consensus, which moreover can possess an exponentially fast convergence speed. In particular, for any desired terminal output that is available to not all of but only a portion of agents, multi‐agent systems can be guaranteed to achieve the finite‐time consensus at the desired terminal output. Simulation tests are given to demonstrate the performance and effectiveness of the obtained consensus results. [ABSTRACT FROM AUTHOR]