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This paper studies the distributed optimal coordinated control problem for Euler–Lagrange multi-agent systems with connectivity preservation. The aim is to force agents to achieve the optimal solution minimizing the sum of the local objective functions while guaranteeing the connectivity of the communication graph. For practical purposes, the gradient vector of the local objective function is allowed to use only at the real-time generalized position instead of at the auxiliary system state. To make the control parameters independent of the global information and guarantee the fully distributed manner of controller, the adaptive control is introduced to update the coupling weights of the relative states among neighbors. Moreover, to reduce the resource for control updates, the event-driven communication is employed for the updates of both the relative states and the gradient of the connectivity-preserving potential function. Based on the Lyapunov analysis framework, it is proved that agents can converge to the optimal solution with connectivity preservation and Zeno behavior is excluded for the two event-triggering conditions. Finally, the effectiveness of the proposed method is verified by a numerical simulation example. [ABSTRACT FROM AUTHOR]

This paper considers the robust output regulation and output constraints of the flexible wing system, under an output disturbance, distributed disturbances, boundary disturbances and a reference. The measurable outputs are merely the in-domain tracking error and the in-domain displacement perturbed by an unknown output disturbance that is used to define the uncertain output constraint. All the above disturbances and reference are supposed to be from an exosystem with unknown initial values. Under the guideline of internal model principle, a PDE-ODE coupled observer is designed to exponentially estimate the states of the wing system and exosystem, and meanwhile guarantee the robustness to the unknown coefficients of the disturbances, reference and output constraint. Observer-based nonlinear controllers are then proposed for the flexible wing system so that the in-domain tracking error is regulated toward zero. Furthermore, the approximately known endpoint tracking errors are further restrained by the estimation of the constraint. A numerical simulation is used to indicate the effectiveness of the designed observer-based dynamic controls. This paper also presents the advantages over the adaptive control and active disturbance rejection control. • A PDE-ODE coupled observer is designed to exponentially estimate the states of the wing system and exosystem, and further gives the approximate values of the endpoint tracking errors and output constraint. • Observer-based nonlinear controllers are proposed to guarantee the robust ourpur regulation of the in-domain tracking error of the flexible wing system. • The approximately known endpoint tracking errors are further restrained by the estimation of the unknwon constraint, which is further proved to be robust to unknown disturbances and references. [ABSTRACT FROM AUTHOR]

In this paper, the consensus tracking problem is studied for a group of nonlinear heterogeneous multiagent systems with asymmetric state constraints and input delays. Different from the existing works, both input delays and asymmetric state constraints are assumed to be nonuniform and time-varying. By introducing a nonlinear mapping to handle the problem caused by state constraints, not only the feasibility condition is removed, but also the restriction on the constraint boundary functions is relaxed. The time-varying input delays are compensated by developing an auxiliary system. Furthermore, by utilizing the dynamic surface control method, neural network technology and the designed finite-time observer, the distributed adaptive control scheme is developed, which can achieve the synchronization between the followers' output and the leader without the violation of full-state constraints. Finally, a numerical simulation is provided to verify the effectiveness of the proposed control protocol. [ABSTRACT FROM AUTHOR]

• A tan-type barrier Lyapunov function is introduced to ensure the prescribed performance of formation errors, the effectiveness of communication connections, and the avoidance of collisions. • A high-gain observer is designed to reconstruct the leader's velocity, which makes the control strategy more versatile in engineering applications. • An adaptive self-structuring neural network is proposed to compensate for model uncertainties and external disturbances, which can optimize the structure of the NNs by adjusting the number of neurons online, thus effectively reducing the computational burden. • A finite-time formation tracking controller for underactuated multiple USVs with error constraints is proposed to guarantee all signals of the closed-loop system to be practically finite-time stable. In this paper, a leader-follower formation control scheme of multiple underactuated surface vessels (USVs) is proposed for trajectory tracking, which not only solves the line of sight (LOS) and angle tracking errors within the prescribed performance, but also avoids collisions and maintains the communication connection distance. To achieve the prescribed performance and converge the tracking errors in finite time, a tan-type barrier Lyapunov function (TBLF) is introduced into the designed control strategy. In the process of formation control design, the measured values of the LOS range and angle are available, and the velocity of the leader is estimated using a high-gain observer. Next, a novel self-structuring neural network (SNN) is proposed to estimate the uncertain dynamics induced by the model uncertainties and environmental disturbances, and the computation amount is reduced by optimizing the number of neurons. Combining coordinate transformation and dynamic surface control (DSC), an adaptive NN controller with prescribed performance is proposed. The Lyapunov analysis shows that, although uncertain dynamics exist, the tracking errors can converge to a small region in finite time while achieving the prescribed performance, avoiding collisions, and maintaining the communication distance. In the closed-loop system, all signals are practical finite-time stable (PFS). Finally, the effectiveness of the proposed scheme is illustrated through a numerical simulation. [ABSTRACT FROM AUTHOR]

This paper investigates the neural adaptive tracking control problem of a class of strict-feedback systems considering asymmetric full-state with input magnitude and rate constraint (MRC). By designing a dual-integral-type actual control law, the MRC on system input is transformed to be the magnitude limitations on the extended states of the original system, so the original system with both state and MRC considerations is converted to be a new system with only full-state constraint. Besides, compared with the traditional symmetric integral barrier Lyapunov function, new asymmetric integral barrier Lyapunov function is introduced to the dynamic surface-based controller design process in this paper for dealing with the asymmetric state constraint problem. It is analyzed that the original system is semi-globally uniformly ultimately bounded, and that the desired multiple constraints are never violated. The effectiveness of the control strategy is shown via numerical simulations. [ABSTRACT FROM AUTHOR]

This paper is devoted to adaptive neural network control issue for a class of nonstrict-feedback uncertain systems with input delay and asymmetric time-varying state constraints. State-related external disturbances are involved into the system, and the upper bounds of disturbances are assumed as functions of state variables instead of constants. Additionally, during the approximations of unknown functions by neural networks, the online computation burdens are declined sharply, since the norms of neural network weight vectors are only estimated. In the process of dealing with input delay, an auxiliary function is applied such that the conditions for time delay are more general than the ones in existing literature. A novel adaptive neural network controller is designed by constructing the asymmetric barrier Lyapunov function, which guarantees that the output of system has a good tracking performance and the state variables never violate the asymmetric time-varying constraints. Finally, numerical simulations are presented to verify the proposed adaptive control scheme. [ABSTRACT FROM AUTHOR]

A robust adaptive finite-time controller for satellite fast attitude maneuver is proposed in this paper. The finite-time sliding mode is proposed based on standard sliding mode, hence the terminal convergence rate could be largely improved, and the inherent robustness to some typical perturbations is maintained. A new method is proposed to deal with the singularity issue based on the property of Euler rotation. In order to deal with inertia matrix uncertainty, finite-time adaptive law for inertia matrix estimation variables is proposed. Considering that the variable estimation system has no direct feedback, an auxiliary state converging slower than system is designed to achieve finite-time stability. The overall global finite-time stability is proved by Lyapunov method and the performance of the controller is demonstrated by numerical simulation results. [ABSTRACT FROM AUTHOR]

This paper studies the problem of composite control for a class of uncertain Markovian jump systems (MJSs) with partial known transition rates, multiple disturbances and actuator saturation. Compared with the existing results, a novel robust composite control scheme is put forward by virtue of adaptive neural network technique. For MJSs, the partial unknown information on transition rates and the actuator saturation influence the design of disturbance observer and the robust H ∞ controller. Firstly, without taking account of external disturbances, the network reconstruction error and saturation, a novel robust adaptive control strategy is established to ensure that all the signals of the closed-loop system are asymptotically bounded in mean square. Secondly, the solvability condition for ensuring the robust H ∞ performance is given by using a modified adaptive law, where the saturation is treated as a disturbance-like signal. Finally, the simulations for a numerical example and an application example are performed to validate the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the adaptive synchronization for coupled harmonic oscillators with switching topology. Edge-based adaptive control protocols are proposed for both leaderless and leader-following synchronization for coupled harmonic oscillators with switching topology. Using Lyapunov stability theory, by parting the topology graph into connected components (containing at least two connected vertices) and isolated vertex components (containing a isolated vertex), full distributed adaptive synchronization conditions are obtained, which can guarantee that the synchronization conditions do not require any global information except a mild connection assumption. Finally numerical simulations are presented to illustrate the theoretical findings. [ABSTRACT FROM AUTHOR]

Abstract This paper solves the problem of adaptive neural dynamic surface control (DSC) for a class of full state constrained stochastic nonlinear systems with unmodeled dynamics. The concept of the state constraints in probability is first proposed and applied to the stability analysis of the system. The full state constrained stochastic nonlinear system is transformed to the system without state constraints through a nonlinear mapping. The unmodeled dynamics is dealt with by introducing a dynamic signal and the adaptive neural dynamic surface control method is explored for the transformed system. It is proved that all signals of the closed-loop system are bounded in probability and the error signals are semi-globally uniformly ultimately bounded(SGUUB) in mean square or the sense of four-moment. At the same time, the full state constraints are not violated in probability. The validity of the proposed control scheme is demonstrated through the simulation examples. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a robust adaptive control strategy for a class of state-constrained uncertain nonlinear systems with prescribed transient and steady-state behavior. The prescribed tracking performance can be characterized by constraints on an output tracking error. Both state and output constraints are achieved by bounding integral barrier Lyapunov functions in the backstepping procedure. A robust adaptive term is designed to compress auxiliary system uncertainties without the knowledge of their bounds. The satisfaction of control constraints and tracking error convergence are verified by theoretical analysis and are illustrated by simulation results. [ABSTRACT FROM AUTHOR]

In this paper, a novel adaptive control for dual-arm cooperative manipulators is proposed to accomplish the hybrid position/force tracking in the presence of dynamic and closed-chain kinematic uncertainties. Self-convergent parameter estimation of the grasped object's centre of mass and contact force estimation are incorporated into this systematic scheme. Moreover, internal force and contact force tracking objectives are achieved simultaneously by incorporating into the position tracking formula with proper null-space projection and rotation transformation. Noisy force derivative signals are not required. This adaptive controller is mathematically derived based on Lyapunov stability analysis. Three sets of simulations corresponding to three different situations are presented to verify the effectiveness and superiority of the proposed controller. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an intelligent self-repairing control scheme is proposed for a class of nonlinear MIMO system. A direct self-repairing controller of a nonlinear SISO system is firstly designed, and then the control scheme is promoted to a nonlinear MIMO system. The error signals are replaced by the state variables to deal with the high derivate problems of the desired signals and a nonlinear regulating function is brought in to improve the performances of the sliding mode. The self-repairing controller is made up of four parts: the nonlinear regulator, the equal controller, the compensator I and the compensator II. The control method is applied to a helicopter flight control system with loss-in-effectiveness faults. Some simulation results illustrate the effectiveness and feasibility of the proposed control scheme in the paper. [Copyright &y& Elsevier]

This paper addresses the stability and position synchronization problems for bilateral nonlinear teleoperation system with asymmetric constant time delays under input saturation. Compared with previous work, not only the steady-state performance but also the transient-state performance is considered. In the presence of system uncertainties and external disturbances, the corresponding adaptive neural network (ANN) based prescribed performance control (PPC) scheme is designed. Moreover, the time-dependent stability conditions are derived by applying the linear matrix inequality (LMI). Finally, in simulation comparisons with P+d (proportion plus differential) controller are conducted, simulation results are presented to demonstrate the effectiveness of the proposed PPC approach. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive output feedback fault-tolerant controller is developed for the longitudinal dynamics of a generic hypersonic flight vehicle in the presence of parameter uncertainties, actuator faults and external disturbances. Firstly, the derivatives of the output are calculated repeatedly so that the relative degree of the system is obtained. Then feedback linearization is used to design the nominal controller. Considering the occurrence of actuator faults, a fault-tolerant controller is developed based on the nominal feedback linearization controller to accommodate the effect of actuator fault, ensure system stability and recover desirable tracking performance. Since some of the states are difficult to measure during actual hypersonic flight, the high-gain observer technique is adopted to achieve output feedback fault-tolerant control. Adaptive laws are designed for updating the controller parameters when both the plant parameters and actuator fault parameters are unknown. Closed-loop stability and output tracking performance are analyzed rigorously. Simulation results verify the effectiveness of the proposed adaptive fault-tolerant control scheme. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the adaptive output feedback control problem for a class of nonlinear systems with unknown time delays and output function. The system satisfies linear growth condition with an unknown growth rate. First of all, based on a dynamic gain scaling technique, we present a new dynamic high-gain observer without requiring precise information of the output function. Then, by employing the idea of universal control and the backstepping method, a universal adaptive output feedback control law is designed to globally regulate all the states of the system. A simulation example is presented to illustrate the effectiveness of the proposed design scheme. [Copyright &y& Elsevier]

Abstract: This paper investigates the problem of complete synchronization of chaotic systems with unknown parameters. An adaptive control scheme based on a feedback passivity approach is proposed. The convergence of the synchronization error is guaranteed. The unified chaotic and hyperchaotic Lü systems are taken as illustrative examples. The feasibility and effectiveness of the proposed scheme are demonstrated through numerical simulations. [Copyright &y& Elsevier]