28,335 research outputs found
Dynamic Control of Mobile Multirobot Systems: The Cluster Space Formulation
The formation control technique called cluster space control promotes simplified specification and monitoring of the motion of mobile multirobot systems of limited size. Previous paper has established the conceptual foundation of this approach and has experimentally verified and validated its use for various systems implementing kinematic controllers. In this paper, we briefly review the definition of the cluster space framework and introduce a new cluster space dynamic model. This model represents the dynamics of the formation as a whole as a function of the dynamics of the member robots. Given this model, generalized cluster space forces can be applied to the formation, and a Jacobian transpose controller can be implemented to transform cluster space compensation forces into robot-level forces to be applied to the robots in the formation. Then, a nonlinear model-based partition controller is proposed. This controller cancels out the formation dynamics and effectively decouples the cluster space variables. Computer simulations and experimental results using three autonomous surface vessels and four land rovers show the effectiveness of the approach. Finally, sensitivity to errors in the estimation of cluster model parameters is analyzed.Fil: Mas, Ignacio Agustin. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Kitts, Christopher. Santa Clara University; Estados Unido
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Design of an adaptive neural predictive nonlinear controller for nonholonomic mobile robot system based on posture identifier in the presence of disturbance
This paper proposes an adaptive neural predictive nonlinear controller to guide a nonholonomic wheeled mobile robot during continuous and non-continuous gradients trajectory tracking. The structure of the controller consists of two models that describe the kinematics and dynamics of the mobile robot system and a feedforward neural controller. The models are modified Elman neural network and feedforward multi-layer perceptron respectively. The modified Elman neural network model is trained off-line and on-line stages to guarantee the outputs of the model accurately represent the actual outputs of the mobile robot system. The trained neural model acts as the position and orientation identifier. The feedforward neural controller is trained off-line and adaptive weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback neural controller is based on the posture neural identifier and quadratic performance index optimization algorithm to find the optimal torque action in the transient state for N-step-ahead prediction. General back propagation algorithm is used to learn the feedforward neural controller and the posture neural identifier. Simulation results show the effectiveness of the proposed adaptive neural predictive control algorithm; this is demonstrated by the minimised tracking error and the smoothness of the torque control signal obtained with bounded external disturbances
Controlling rigid formations of mobile agents under inconsistent measurements
Despite the great success of using gradient-based controllers to stabilize
rigid formations of autonomous agents in the past years, surprising yet
intriguing undesirable collective motions have been reported recently when
inconsistent measurements are used in the agents' local controllers. To make
the existing gradient control robust against such measurement inconsistency, we
exploit local estimators following the well known internal model principle for
robust output regulation control. The new estimator-based gradient control is
still distributed in nature and can be constructed systematically even when the
number of agents in a rigid formation grows. We prove rigorously that the
proposed control is able to guarantee exponential convergence and then
demonstrate through robotic experiments and computer simulations that the
reported inconsistency-induced orbits of collective movements are effectively
eliminated.Comment: 10 page
Resilient and Decentralized Control of Multi-level Cooperative Mobile Networks to Maintain Connectivity under Adversarial Environment
Network connectivity plays an important role in the information exchange
between different agents in the multi-level networks. In this paper, we
establish a game-theoretic framework to capture the uncoordinated nature of the
decision-making at different layers of the multi-level networks. Specifically,
we design a decentralized algorithm that aims to maximize the algebraic
connectivity of the global network iteratively. In addition, we show that the
designed algorithm converges to a Nash equilibrium asymptotically and yields an
equilibrium network. To study the network resiliency, we introduce three
adversarial attack models and characterize their worst-case impacts on the
network performance. Case studies based on a two-layer mobile robotic network
are used to corroborate the effectiveness and resiliency of the proposed
algorithm and show the interdependency between different layers of the network
during the recovery processes.Comment: 9 pages, 6 figure
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