Piezo-actuated stages have applications in many areas such as aerospace, semiconductor manufacturing, and biotechnology. However, the inherent hysteresis, creep, and vibration of these stages render it challenging to ...
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Piezo-actuated stages have applications in many areas such as aerospace, semiconductor manufacturing, and biotechnology. However, the inherent hysteresis, creep, and vibration of these stages render it challenging to guarantee tracking precision in positioning control. Although various control strategies based on accurate models of piezo-actuated stages have been developed that show remarkable efficacy, the associated complexity in model development and identification, especially when the system exhibits uncertainties, often presents a hurdle to their practical adoption. In this study, we develop a data-driven control method using an adaptive predictive controller that dynamically obtains an equivalent linear model by estimating the pseudo-gradient of the underlying nonlinear dynamics online using only the input/output measurement data. For controller implementation, a radial basis function neural network is adopted to adjust the controller parameters by leveraging its powerful self-learning adaptation. Tracking control experiments illustrate the effectiveness of the proposed method in comparison with the proportional-integral-derivative controller and classical model-free adaptive predictive controller.
In this paper, an improved adaptivepredictive control with robust filter is developed to be applied in an artificial pancreas. Several problems inherent to endocrine systems for diabetic persons have to be tackled su...
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In this paper, an improved adaptivepredictive control with robust filter is developed to be applied in an artificial pancreas. Several problems inherent to endocrine systems for diabetic persons have to be tackled such as nonlinearities, long time delays or daily variations of parameters. Three Finite Impulse Response models for insulin input and the same for meal intake (perturbations) corresponding to normal, hyper-hypoglycaemia levels to implement three zones control are taken into account. The glycaemia reference trajectory is shaped from a healthy person response. A variable weighting factor in the cost function is included to prevent dangerous glycaemia excursions out of the allowed limits. Additionally, a noisy blood glucose subcutaneous sensor model is used. This control strategy is tested on 30 virtual subjects from the UVa - Padova Simulator. Simultaneous meals and physiological disturbances are taken into account and the main conclusions are drawn from Control Variability Grid Analysis. (C) 2013 Elsevier Ltd. All rights reserved.
The present study addresses the adaptive predictive controller with adaptive notch filter for the tip position control of a deployable space structure model. An adaptive notch filter is designed to estimate multiple b...
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The present study addresses the adaptive predictive controller with adaptive notch filter for the tip position control of a deployable space structure model. An adaptive notch filter is designed to estimate multiple bending mode frequencies of the deployable manipulator and to minimize the effect of bending vibration. The results show that the adaptive predictive controller with adaptive notch filter is quite effective controlling the tip position of a deployable space structure under poor modeling information. Crown Copyright (C) 2009 Published by Elsevier Masson SAS. All rights reserved.
This paper presents a trajectory tracking controller for a nonholonomic mobile robot using an optimization algorithm based predictive feedback control and an adaptive posture identifier model while following a continu...
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This paper presents a trajectory tracking controller for a nonholonomic mobile robot using an optimization algorithm based predictive feedback control and an adaptive posture identifier model while following a continuous and a non-continuous path. The posture identifier model is a modified Elman neural network that describes the kinematics and dynamics of the mobile robot model. The feedforward neural controller is trained off-line and its 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 prediction 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 and experimental work show the effectiveness of the proposed adaptive nonlinear predictive control algorithm;this is demonstrated by the minimized tracking error and the smoothness of the torque control signal obtained, especially with regards to the external disturbance attenuation problem. (C) 2012 Elsevier B.V. All rights reserved.
This paper investigated the implementation of an adaptive predictive controller using nonlinear dynamic echo state neural （ESN） model for a rotary crane system by the visual servo method. The control sequences withi...
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This paper investigated the implementation of an adaptive predictive controller using nonlinear dynamic echo state neural （ESN） model for a rotary crane system by the visual servo method. The control sequences within the control horizon were described using cubic spline interpolation to enlarge the predictive horizon. Verification of the proposed scheme in the face of exogenous disturbances and modeling error with inaccurate string length was demonstrated by both simulations and experiments.
As server densities increase to support the rising demand for high density computing, traditional room cooling infrastructure is struggling to keep up. Rack-mounted cooling units are increasingly being deployed in dat...
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As server densities increase to support the rising demand for high density computing, traditional room cooling infrastructure is struggling to keep up. Rack-mounted cooling units are increasingly being deployed in data centers. These cooling units are located closer to the heat sources (the servers) which allow them to cool more efficiently than traditional cooling infrastructure. In this work, a Multi-Input Single-Output (MISO) adaptive predictive controller (APC) for rack-mounted cooling units is investigated and implemented using a low-cost general-purpose microcontroller. The proposed APC is implemented using Weighted Recursive Least Squares (WRLS) and a sub-optimal but fast algorithm based on the General predictivecontroller (GPC) approach. These are combined with a variable forgetting factor and variable prediction horizon algorithms. In addition, methods are proposed to handle stability issues arising due to practical hardware limitations. The controller is implemented on a real single rack system and challenges for practical implementation are addressed and illustrated. The proposed APC is also compared (via simulation) to a standard APC with equivalent complexity and a split-range PI controller. The results show that the proposed controller outperforms both the standard APC and the split-range PI controllers with respect to Mean Squared Error (MSE).
Achieving accurate control of main steam temperature is a very difficult task in Thermal power plants due to the large process lag (8 to 10 minutes) associated with the superheater system. A control oriented boiler mo...
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Achieving accurate control of main steam temperature is a very difficult task in Thermal power plants due to the large process lag (8 to 10 minutes) associated with the superheater system. A control oriented boiler model and an appropriate optimal control strategy are the essential tools for improving the accuracy of this control system. This paper offers a comprehensive integrated 8 th order mathematical model for the boiler and a Kalman Filter based state predictivecontroller for effectively controlling the main steam temperature and to enhance the efficiency of the boiler. In order to demonstrate the effectiveness of the control system, three more advanced control methods are experimented with the boiler model - Pole placement controller, Optimal controller with state observer and Optimal controller with Kalman filter. Simulation results have illustrated that the predictivecontroller method with Kalman filter state estimator and predictor is the most appropriate one for the optimization of main steam temperature control. At present, we are in the process of implementing this control strategy in running Thermal power plants.
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