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In this paper, the leader-following consensus problem of a multi-Euler-Lagrange system is studied under deception attacks, where attackers can inject the false data into the data being exchanged over the communication network between two Euler-Lagrange systems. A distributed adaptive filter is proposed to compensate the unknown injection false data, where the magnitude of the false data is estimated. Meanwhile, the performance of the multiple Euler-Lagrange system consensus with the proposed filter can be guaranteed to be similar to that with the traditional controller when the communication network is not injected into false data by a malicious attacker. Furthermore, the consensus criteria of a multiple Euler-Lagrange system and the parameter design scheme of the proposed adaptive filter are achieved by using Lyapunov stability theory. Finally, a numerical example is carried out to demonstrate the effectiveness of the proposed consensus controller for a multi-Euler-Lagrange system attacked by using the false data injection.

In this paper, a robust adaptive finite-time (FT) tracking control scheme is proposed for Euler-Lagrange systems (ELSs) subject to nonparametric uncertainties, unknown disturbances and input saturation. In the design procedure, a Gaussian error function is utilized to approximate the input saturation nonlinearity. Following that, by employing the natural property that the upper bound of model parameters uncertainties is linear-in-parameters, the lumped uncertain term caused by uncertain model parameters and external disturbances is formulated by a linear-parametric form with a single parameter. And then, a novel robust adaptive tracking control law is designed to resolve the tracking control problem of uncertain ELSs. The proposed control scheme is featured by FT convergence rate, and robustness against uncertainties and unknown disturbances. Furthermore, the robust adaptive FT tracking control scheme is insensitive to the character of the uncertainties, and is with low computational burden and easy to implement in engineering applications. And its rigorous stability is analyzed with the aid of the Lyapunov stability theory, and its effectiveness is verified by simulation results and comparison.

This paper considers the model-free optimal consensus problem of networked Euler-Lagrange systems without velocity measurements. By employing the position information, a novel neural network-based velocity observer is built for each agent to estimate the unmeasurable velocity vector and unknown system model. Based on the estimated velocity information, we propose distributed optimal control policies depended on the solutions to the coupled Hamilton-Jacobi-Bellman (HJB) equations. Then, a model-free policy iteration (PI) algorithm is provided to learn the coupled HJB equations online. To implement the PI algorithm, the critic-action neural networks are built and their weights are updated based on the gradient descent method. The uniform ultimate boundedness of the integrated observer estimation errors, the integrated consensus errors, and the weight estimation errors for the observer-critic-action neural networks is demonstrated by the Lyapunov technique. Finally, the numerical simulation on a directed network with six nonlinear manipulators is presented to validate the theoretical results.

This paper proposes a control scheme in the operational space for bilateral teleoperation systems composed of heterogeneous robots (kinematically and dynamically different) without velocity sensors and considering variable time-delays in the interconnection. The proposed control scheme use a second order dynamical controller that back-propagates damping to the local and the remote manipulators. Under the assumptions that the human operator and the environment define passive maps from force to velocity, it is proved that velocities and pose (position and orientation) errors between the local and the remote manipulators are bounded. Moreover, in the case that the human and the environment forces are zero, the velocities and pose errors converge asymptotically to zero. The proposed approach employs, the singularity-free, unit-quaternions to represent the orientation of the end-effectors. The performance of the proposed controller is illustrated via simulations with a teleoperation system composed of robots with 3-DoF and 7-DoF.

This paper addresses the problem of leader&ndash, follower synchronization of uncertain Euler&ndash, Lagrange systems under input constraints. The problem is solved in a distributed model reference adaptive control framework that includes positive &mu, modification to address input constraints. The proposed design has the distinguishing features of updating the gains to synchronize the uncertain systems and of providing stable adaptation in the presence of input saturation. By using a matching condition assumption, a distributed inverse dynamics architecture is adopted to guarantee convergence to common dynamics. The design is studied analytically, and its performance is validated in simulation using spacecraft dynamics.

Available control methods for underactuated Euler–Lagrange (EL) systems rely on structure-specific constraints that may be appropriate for some systems, but restrictive for others. A generalized (structure-independent) control framework is to a large extent missing, especially in the presence of uncertainty. This paper introduces an adaptive-robust control framework for a quite general class of uncertain underactuated EL systems. Compared to existing literature, the important attributes of the proposed solution are: (i) avoiding structure-specific restrictions, namely, symmetry condition property of the mass matrix, and a priori bounds on non-actuated states or state derivatives; (ii) considering Coriolis, centripetal, friction and gravity terms to be unknown, while only requiring the knowledge of maximum perturbation around a nominal value of the mass matrix; (iii) handling state-dependent uncertainties irrespective of their linear or nonlinear in parameters structure. These features significantly widen the range of underactuated EL systems the proposed solution can handle in comparison to the available methods. Stability is studied analytically and the performance is verified in simulation using offshore boom crane dynamics.

In this paper we present three distributed control laws for the coordination of networked Euler-Lagrange (EL) systems. We first reformulate the passivity-based control design method in \cite{Arcak} by considering that each edge is associated with an \emph{artificial spring system} instead of the usual diffusive coupling among the communicating agents. With this configuration, the networked EL system possesses a "symmetric" feedback structure which together with the strict passivity of both agents' and edges' dynamics lead to a strictly passive network dynamics. Subsequently we present the networked version of two different passivity-based tracking controllers %local controllers that are particular cases of our method and the one in \cite{Arcak}. Numerical simulation is presented to show the performance of the proposed methods., Comment: Submitted to Necsys2018, 7 pages, 1 figure

This paper discusses the matching conditions as introduced in two recently developed methods for stabilization of underactuated mechanical systems. It is shown that the controlled Lagrangians method is naturally embedded in the IDA-PBC method. The integrability of the latter method is studied in general.

In this short note we deal with the problem of trajectory tracking control of fully actuated Euler–Lagrange systems with unmeasurable velocities and bounded control inputs. In order to avoid the use of velocity measurements we introduce a nonlinear dynamic extension for the plant which renders the closed-loop system semi-globally asymptotically stable, hence, we prove that by increasing some of the dynamic extension parameters it is possible to enlarge the domain of attraction and to maintain the control inputs bounded.

In this brief note we analyse the (local) disturbance attenuation properties of some asymptotically stabilizing nonlinear controllers for Euler-Lagrange systems reported in the literature. Our objective in this study is twofold: first, to compare the performance of these schemes from a perspective different from stabilizability; second, to quantify the basic tradeoff between robust stability and robust performance for these designs. We consider passivity-based and feedback linearization schemes developed for the control of DC-to-DC converters and rigid robots. For the DC-to-DC problem we show that for both controllers there exists a lower bound to the achievable attenuation level, i.e. a lower bound to the L 2-gain of the closed loop operator from disturbance to regulated output, which is independent of the design parameters. Also, for the passivity based scheme we obtain an upper bound for the disturbance attenuation, which is insured provided we sacrifice the convergence rate. For rigid robots we show that both approaches yield arbitrarily good disturbance attenuation without compromising the convergence rate.

This paper presents a separation result for some global stabilization via output feedback of a class of quadratic-like nonlinear systems, under the form of some stabilizability by state feedback on the one hand, and unboundedness observability on the other hand. They allow to design, for any domain of output initial condition, a dynamic output feedback controller achieving global stability. As an example, these conditions are shown to be satisfied by so-called Euler-Lagrange systems, for which a tracking output feedback control law is thus proposed.