Phase correction for Learning Feedforward Control

Intelligent mechatronics makes it possible to compensate for effects that are difficult to compensate for by construction or by linear control, by including some intelligence into the system. The compensation of state dependent effects, e.g. friction, cogging and mass deviation, can be realised by learning feedforward control. This method identifies these disturbing effects as function of their states and compensates for these, before they introduce an error. Because the effects are learnt as function of their states, this method can be used for non-repetitive motions. The learning of state dependent effects relies on the update signal that is used. In previous work, the feedback control signal was used as an error measure between the approximation and the true state dependent effect. If the effects introduce a signal that contains frequencies near the bandwidth, the phase shift between this signal and the feedback signal might seriously degenerate the performance of the approximation. The use of phase correction overcomes this problem. This is validated by a set of simulations and experiments that show the necessity of the phase corrected scheme

Inferential Feedforward Control of a Distillation Column

An inferential feedforward control strategy is developed and applied to a simulated distillation column. In this control strategy, the effects of disturbances on the primary process variables (top and bottom compositions) are inferred from uncontrolled secondary process variables (tray temperatures) which can be easily measured. The proposed strategies are particularly useful when disturbances cannot be measured easily or economically. Robustness of the inferential feedforward controllers and the selection of appropriate secondary measurements are discussed. Nonlinear dynamic simulation results demonstrate the superior performance of this control strategy and verify the robustness analysis

Adaptive Neural Network Feedforward Control for Dynamically Substructured Systems

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Experimental investigation of feedforward control schemes of a flexible robot manipulator system

This paper presents experimental investigations into the applications of feedforward control schemes for vibration control of a flexible manipulator system. Feedforward control schemes based on input shaping and filtering techniques are to be examined. A constrained planar single-link flexible manipulator is considered in this experimental work. An unshaped bang-bang torque input is used to determine the characteristic parameters of the system for design and evaluation of the input shaping control techniques. The input shapers and filtering techniques are designed based on the properties of the system. Simulation results of the response of the manipulator to the shaped and filtered inputs are presented in time and frequency domains. Performances of the shapers are examined in terms of level of vibration reduction and time response specifications. The effects of derivative order of the input shaper on the performance of the system are investigated. Finally, a comparative assessment of the control strategies is presented and discusse

LIDAR-based wind speed modelling and control system design

Abstract—The main objective of this work is to explore the feasibility of using LIght Detection And Ranging (LIDAR) measurement and develop feedforward control strategy to improve wind turbine operation. Firstly the Pseudo LIDAR measurement data is produced using software package GH Bladed across a distance from the turbine to the wind measurement points. Next the transfer function representing the evolution of wind speed is developed. Based on this wind evolution model, a model-inverse feedforward control strategy is employed for the pitch control at above-rated wind conditions, in which LIDAR measured wind speed is fed into the feedforward. Finally the baseline feedback controller is augmented by the developed feedforward control. This control system is developed based on a Supergen 5MW wind turbine model linearised at the operating point, but tested with the nonlinear model of the same system. The system performances with and without the feedforward control channel are compared. Simulation results suggest that with LIDAR information, the added feedforward control has the potential to reduce blade and tower loads in comparison to a baseline feedback control alone

New control strategies for neuroprosthetic systems

The availability of techniques to artificially excite paralyzed muscles opens enormous potential for restoring both upper and lower extremity movements with\ud neuroprostheses. Neuroprostheses must stimulate muscle, and control and regulate the artificial movements produced. Control methods to accomplish these tasks include feedforward (open-loop), feedback, and adaptive control. Feedforward control requires a great deal of information about the biomechanical behavior of the limb. For the upper extremity, an artificial motor program was developed to provide such movement program input to a neuroprosthesis. In lower extremity control, one group achieved their best results by attempting to meet naturally perceived gait objectives rather than to follow an exact joint angle trajectory. Adaptive feedforward control, as implemented in the cycleto-cycle controller, gave good compensation for the gradual decrease in performance observed with open-loop control. A neural network controller was able to control its system to customize stimulation parameters in order to generate a desired output trajectory in a given individual and to maintain tracking performance in the presence of muscle fatigue. The authors believe that practical FNS control systems must\ud exhibit many of these features of neurophysiological systems

Adaptive feedforward control design for gust loads alleviation and LCO suppression

An adaptive feedforward controller is designed for gust loads alleviation and limit cycle oscillations suppression. Two sets of basis functions, based on the finite impulse response and modified finite impulse response approaches, are investigated to design the controller for gust loads alleviation. Limit cycle oscillations suppression is shown by using the modified finite impulse response controller. Worst case gust search is performed by using a nonlinear technique of model reduction to speed up the costs of calculations. Both the “one–minus–cosine” and Von Kármán continuous turbulence gusts of different intensities were generated to examine the performance of controllers. The responses of these two types of gust can be reduced effectively by finite impulse response controller in the whole process, while the modified finite impulse response controller is found to increase the loads during the initial transient response. The above two types of gust induced limit cycle oscillations were used to test the modified finite impulse response controller. Results show that it can suppress limit cycle oscillations to some exten

Adaptive feedforward control design for gust loads alleviation and LCO suppression

An adaptive feedforward controller is designed for gust loads alleviation and limit cycle oscillations suppression. Two sets of basis functions, based on the finite impulse response and modified finite impulse response approaches, are investigated to design the controller for gust loads alleviation. Limit cycle oscillations suppression is shown by using the modified finite impulse response controller. Worst case gust search is performed by using a nonlinear technique of model reduction to speed up the costs of calculations. Both the “one–minus–cosine” and Von Kármán continuous turbulence gusts of different intensities were generated to examine the performance of controllers. The responses of these two types of gust can be reduced effectively by finite impulse response controller in the whole process, while the modified finite impulse response controller is found to increase the loads during the initial transient response. The above two types of gust induced limit cycle oscillations were used to test the modified finite impulse response controller. Results show that it can suppress limit cycle oscillations to some exten