21,003 research outputs found
Recommended from our members
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
Extremum Seeking-based Iterative Learning Linear MPC
In this work we study the problem of adaptive MPC for linear time-invariant
uncertain models. We assume linear models with parametric uncertainties, and
propose an iterative multi-variable extremum seeking (MES)-based learning MPC
algorithm to learn on-line the uncertain parameters and update the MPC model.
We show the effectiveness of this algorithm on a DC servo motor control
example.Comment: To appear at the IEEE MSC 201
Adaptive, cautious, predictive control with Gaussian process priors
Nonparametric Gaussian Process models, a Bayesian statistics approach, are used to implement a nonlinear adaptive control law. Predictions, including propagation of the state uncertainty are made over a k-step horizon. The expected value of a quadratic cost function is minimised, over this prediction horizon, without ignoring the variance of the model predictions. The general method and its main features are illustrated on a simulation example
Predictive control of a solar air conditioning plant with simultaneous identification
This paper presents the application of a predictive
controller with simultaneous identification to a solar air conditioning plant. The time varying nature of the process makes
necessary an adjustment of the controller parameters to the
varying operational conditions. The main novelty with respect
to classic adaptive MPC scheme is to penalize the identification
error in the cost function used for control. The behaviour of the
controller is illustrated by simulations and experimental results.
The integration of identification and control avoids the tedious
identification procedure that is necessary before the start-up
of any predictive controller. This new adaptive MPC scheme
shows its effectiveness in controlling the outlet temperature in
the solar thermal plant.Ministerio de Ciencia y TecnologĂa DPI2004-07444-C04-0
Observation of charge ordering signal in monovalent doped Nd0.75Na0.25-xKxMn1O3 (0 †x †0.10) manganites
K doping in the compound of Nd0.75Na0.25-xKxMn1O3 (x = 0, 0.05 and 0.10) manganites have been investigated to study its effect on crystalline phase and surface morphology as well as electrical transport and magnetic properties. The structure properties of the Nd0.75Na0.25- xKxMnO3 manganite have been characterized using X-ray diffraction measurement and it proved that the crystalline phase of samples were essentially single phased and indexed as orthorhombic structure with space group of Pnma. The morphological study from scanning electron microscope showed there was an improvement on the grains boundaries and sizes as well as the compactness with K doping suggestively due to the difference of ionic radius. On the other hand, DC electrical resistivity measurement showed all samples exhibit insulating behavior. However, analysis of dlnÏ/dT-1 vs. T revealed the clearly peaks could be observed at temperature 210K for x = 0 and the peaks were shifted to the lower temperature around 190 K and 165 K for x = 0.05 and x = 0.1 respectively, indicate the existence of charge ordering (CO) state in the compound. Meanwhile, the investigation on magnetic behavior showed all samples exhibit transition from paramagnetic phase to anti-ferromagnetic phase with decreasing temperature and the TN was observed to shift to lower temperature suggestively due to weakening of CO stat
- âŠ