39,345 research outputs found
Concurrent Learning Adaptive Model Predictive Control with Pseudospectral Implementation
This paper presents a control architecture in which a direct adaptive control
technique is used within the model predictive control framework, using the
concurrent learning based approach, to compensate for model uncertainties. At
each time step, the control sequences and the parameter estimates are both used
as the optimization arguments, thereby undermining the need for switching
between the learning phase and the control phase, as is the case with
hybrid-direct-indirect control architectures. The state derivatives are
approximated using pseudospectral methods, which are vastly used for numerical
optimal control problems. Theoretical results and numerical simulation examples
are used to establish the effectiveness of the architecture.Comment: 21 pages, 13 figure
Experimental Results of Concurrent Learning Adaptive Controllers
Commonly used Proportional-Integral-Derivative based UAV flight controllers are often seen to provide adequate trajectory-tracking performance only after extensive tuning. The gains of these controllers are tuned to particular platforms, which makes transferring controllers from one UAV to other time-intensive. This paper suggests the use of adaptive controllers in speeding up the process of extracting good control performance from new UAVs. In particular, it is shown that a concurrent learning adaptive controller improves the trajectory tracking performance of a quadrotor with baseline linear controller directly imported from another quadrotors whose inertial characteristics and throttle mapping are very di fferent. Concurrent learning adaptive control uses specifi cally selected and online recorded data concurrently with instantaneous data and is capable of guaranteeing tracking error and weight error convergence without requiring persistency of excitation. Flight-test results are presented on indoor quadrotor platforms operated in MIT's RAVEN environment. These results indicate the feasibility of rapidly developing high-performance UAV controllers by using adaptive control to augment a controller transferred from another UAV with similar control assignment structure.United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N000141110688)National Science Foundation (U.S.). Graduate Research Fellowship Program (Grant 0645960)Boeing Scientific Research Laboratorie
Sparse Wide-Area Control of Power Systems using Data-driven Reinforcement Learning
In this paper we present an online wide-area oscillation damping control
(WAC) design for uncertain models of power systems using ideas from
reinforcement learning. We assume that the exact small-signal model of the
power system at the onset of a contingency is not known to the operator and use
the nominal model and online measurements of the generator states and control
inputs to rapidly converge to a state-feedback controller that minimizes a
given quadratic energy cost. However, unlike conventional linear quadratic
regulators (LQR), we intend our controller to be sparse, so its implementation
reduces the communication costs. We, therefore, employ the gradient support
pursuit (GraSP) optimization algorithm to impose sparsity constraints on the
control gain matrix during learning. The sparse controller is thereafter
implemented using distributed communication. Using the IEEE 39-bus power system
model with 1149 unknown parameters, it is demonstrated that the proposed
learning method provides reliable LQR performance while the controller matched
to the nominal model becomes unstable for severely uncertain systems.Comment: Submitted to IEEE ACC 2019. 8 pages, 4 figure
Solar Sailing Adaptive Control Using Integral Concurrent Learning for Solar Flux Estimation
In the interest of exploiting natural forces for propellant-less spacecraft missions, this thesis proposes an adaptive control strategy to account for unknown parameters in the dynamic modeling of a reflectivity-controlled solar sail spacecraft. A Lyapunov-based control law along with integral concurrent learning is suggested to accomplish and prove global exponential tracking of the estimated parameters and states of interest, without satisfying the common persistence of excitation condition, which in most nonlinear systems cannot be guaranteed a priori. This involves estimating the solar flux or irradiance from the Sun to account for uncertainty and variation over time in this value. To illustrate potential applications, two missions are considered: (1) a geostationary debris removal case and (2) an Earth-Mars interplanetary transfer orbit following a logarithmic spiral reference trajectory. The proposed formulation demonstrates the benefit of estimating the solar flux using integral concurrent learning. Results are compared to trajectories with no estimation to illustrate the need to account for solar flux fluctuations
Solar Sailing Adaptive Control Using Integral Concurrent Learning for Solar Flux Estimation
In the interest of exploiting natural forces for propellant-less spacecraft missions, this investigation proposes an adaptive control strategy to account for unknown parameters in the dynamic modeling of a reflectivity-controlled solar sail spacecraft. A Lyapunov-based control law along with integral concurrent learning is suggested to accomplish and prove global exponential tracking of the estimated parameters and states of interest, without satisfying the common persistence of excitation condition, which in most nonlinear systems cannot be guaranteed a priori. This involves estimating the solar flux or irradiance from the Sun to account for uncertainty and variation over time in this value. To illustrate potential applications, two missions are considered: (1) a geostationary debris removal case and (2) an Earth-Mars interplanetary transfer orbit following a logarithmic spiral reference trajectory. The proposed formulation demonstrates the benefit of estimating the solar flux using integral concurrent learning. Results are compared to trajectories with no estimation to illustrate the need to account for the actual solar flux
Concurrent Learning-Based Neuro-Adaptive Robust Tracking Control of Wheeled Mobile Robot: An Event-Triggered Design
In this paper, an event-based neuro-adaptive robust tracking controller for a perturbed and networked differential drive mobile robot (DMR) is designed with concurrent learning. A radial basis function neural network, which approximates an unknown perturbation, is used to design an adaptive sliding mode controller (SMC). The RBFNN weights and SMC parameters are estimated online using an adaptive tuning law to ensure performance with reduced chattering. To improve the convergence of RBFNN weight estimation error, a concurrent learning-based adaptive law is derived, which uses measured online and recorded data. Further, a suitable triggering condition is designed to achieve a reduced number of control computations while minimizing network resources without sacrificing the stability of the sampled data closed-loop control system. A finite sampling frequency is guaranteed for the designed triggering condition by establishing a positive lower bound on the inter-event execution time which is equivalent to the Zeno-free behavior of the system. Finally, the proposed event-based neuro-adaptive robust controller is implemented on a practical system (Q-bot 2e) to show the effectiveness of the proposed design
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