2,910 research outputs found
An internal model approach to (optimal) frequency regulation in power grids with time-varying voltages
This paper studies the problem of frequency regulation in power grids under
unknown and possible time-varying load changes, while minimizing the generation
costs. We formulate this problem as an output agreement problem for
distribution networks and address it using incremental passivity and
distributed internal-model-based controllers. Incremental passivity enables a
systematic approach to study convergence to the steady state with zero
frequency deviation and to design the controller in the presence of
time-varying voltages, whereas the internal-model principle is applied to
tackle the uncertain nature of the loads.Comment: 16 pages. Abridged version appeared in the Proceedings of the 21st
International Symposium on Mathematical Theory of Networks and Systems, MTNS
2014, Groningen, the Netherlands. Submitted in December 201
A family of asymptotically stable control laws for flexible robots based on a passivity approach
A general family of asymptotically stabilizing control laws is introduced for a class of nonlinear Hamiltonian systems. The inherent passivity property of this class of systems and the Passivity Theorem are used to show the closed-loop input/output stability which is then related to the internal state space stability through the stabilizability and detectability condition. Applications of these results include fully actuated robots, flexible joint robots, and robots with link flexibility
Multi-time-horizon Solar Forecasting Using Recurrent Neural Network
The non-stationarity characteristic of the solar power renders traditional
point forecasting methods to be less useful due to large prediction errors.
This results in increased uncertainties in the grid operation, thereby
negatively affecting the reliability and increased cost of operation. This
research paper proposes a unified architecture for multi-time-horizon
predictions for short and long-term solar forecasting using Recurrent Neural
Networks (RNN). The paper describes an end-to-end pipeline to implement the
architecture along with the methods to test and validate the performance of the
prediction model. The results demonstrate that the proposed method based on the
unified architecture is effective for multi-horizon solar forecasting and
achieves a lower root-mean-squared prediction error compared to the previous
best-performing methods which use one model for each time-horizon. The proposed
method enables multi-horizon forecasts with real-time inputs, which have a high
potential for practical applications in the evolving smart grid.Comment: Accepted at: IEEE Energy Conversion Congress and Exposition (ECCE
2018), 7 pages, 5 figures, code available: sakshi-mishra.github.i
Robust Output Regulation for Autonomous Robots:self-learning mechanisms, task-space control and multi-agent systems
This thesis focuses on robust output regulation for autonomous robots. The control objective of output regulation is to design a feedback controller to achieve asymptotic tracking and/or disturbance rejection for a class of exogenous reference and/or disturbance while maintaining closed-loop stability. We investigate three research problems that pertain to the constructive design of robust output regulation for fully actuated Euler-Lagrange systems from centralized to distributed fashions. The first one is the global robust output regulation of second-order affine nonlinear systems with input disturbances that encompass the fully-actuated Euler-Lagrange systems. Based on a certainty equivalence principle method, we proposed a novel class of nonlinear internal models taking a cascade interconnection structure with strictly relaxed conditions than before. The second one is the output regulation for robot manipulators working in task-space. An internal model-based adaptive controller is designed to cope with uncertain manipulator kinematic and dynamic parameters, as well as unknown periodic reference trajectories generated by harmonic oscillators. The last one is the formation control of manipulators’ end-effector subject to external disturbances or parameter uncertainties. We present and analyze gradient descent-based distributed formation controllers for end-effectors. Internal models are used to reject external disturbances. Moreover, by introducing an extra integrator and an adaptive estimator for gravitational compensation and stabilization, respectively, we extend the proposed gradient-based design to the case where the plant parameters are not exactly known
A nonparametric learning framework for nonlinear robust output regulation
This paper proposes a nonparametric learning solution framework for a generic
internal model design of nonlinear robust output regulation. The global robust
output regulation problem for a class of nonlinear systems with output feedback
subject to a nonlinear exosystem can be tackled by constructing a linear
generic internal model, provided that a continuous nonlinear mapping exists. An
explicit continuous nonlinear mapping was constructed recently in [1] under the
assumption that the steady-state generator is linear in the exogenous signal.
We further relax such an assumption to a relaxed assumption that the
steady-state generator is polynomial in the exogenous signal. A nonparametric
learning framework is proposed to solve a linear time-varying equation to make
the nonlinear continuous mapping always exist. With the help of the proposed
framework, the nonlinear robust output regulation problem can be converted into
a robust non-adaptive stabilization problem for the augmented system with
integral Input-to-State Stable (iISS) inverse dynamics. Moreover, a dynamic
gain approach can adaptively raise the gain to a sufficiently large constant to
achieve stabilization without requiring any a priori knowledge of the
uncertainties appearing in the dynamics of the exosystem and the system. We
further apply the nonparametric learning framework to globally reconstruct and
estimate multiple sinusoidal signals with unknown frequencies without using
adaptive techniques. An explicit nonlinear mapping can directly provide the
estimated parameters, which will exponentially converge to the unknown
frequencies. As a result, a feedforward control design is proposed to solve the
output regulation using our nonparametric learning framework.Comment: 15 pages; Nonlinear control; iISS stability; output regulation;
parameter estimation; Non-adaptive contro
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