EHPV® Technology Advanced Control Techniques for Electro-Hydraulic Control Valves by Patrick Opdenbosch Goals Develop a smarter and self-contained valve. Investigate algorithms for off-line and on-line learning of the input-output map. Develop robust trajectory learning controller Improve the valve’s performance Explore algorithms and applications via theory, simulation, and implementation on a Hardware-in-the-Loop simulator. System Description and Modeling System Features: Multidisciplinary: Mechanical, Electromagnetic, and hydraulic. Complex nonlinear behavior Bi-directional capability “Zero” leakage Mechanical pressure compensation On-line Learning Control is based on making the valve’s flow conductance, Kv, follow a desired trajectory. Linearized error dynamics: Control Law: Coil cap Modulating spring Coil Estimation Scheme Control Input Modeling Principle Armature isol Kv Electromagnetic System Mechanical System Hydraulic System Forward Step Responses Pilot Abstract Electro-Hydraulic Poppet Valves are used for flow/pressure control in hydraulic machinery. Currently, the valve control is achieved by changing the valve’s conductance coefficient Kv (output) in an open loop manner via PWM current (input). This input current is computed from an inverse input-output map obtained through individual extensive offline calibration. Without any online correction, the map cannot be adjusted to accurately reflect the behavior of the valve as it undergoes continuous operation. The intention is to develop a control methodology to have the valve learn its own inverse mapping at the same time that the transient performance is improved. Input Output Cage (housing) Main Poppet Forward: Nose to side Estimation can be done via: Modified Broyden Method Recursive Least Squares with Covariance Reset Recursive Least Square with Forgetting Factor Compensation NLPN learning can be accomplished by: Delta-Rule Method Recursive Least Squares with Covariance Reset Recursive Least Square with Forgetting Factor Reverse: Side to nose Experimental Steady State Characteristics: Forward and Reverse Kv as a function of Pressure differential and input current Reverse Step Responses Off-line Learning The input-output map of the EHPV, as well as the inverse of this map, is learned off-line by using a Neural Network structure called Nodal Link Perceptron Network (NLPN). The learning consists of adjusting the “weights” of basis functions (composed of activation functions). Upper Figure: Open-loop Response (nominal map only) Lower Figure: Estimated Parameters J and Q Upper Figure: Closed-loop Response Lower Figure: Estimated Parameters J and Q Project Tasks The main project tasks are summarized as Dynamic modeling of EHPV. Off-line learning of input-output mapping for both forward and reverse flow modes On-line learning of input-output mapping for both forward and reverse flow modes Controller robustness Performance evaluation Triangular Activation Function Test-bed Hydraulic Schematic General NLPN Structure Reverse Input-Output Mapping Forward Input-Output Mapping EHPV Pressure Sensors Least Squares Estimation of Weights: Swing Arm Orifice Flowmeter Calibration Map Reverse Output-Input Mapping Forward Output-Input Mapping Orifice Flowmeter Position Sensor Future Work Controller robustness and performance is to be evaluated when using the EHPV in different combinations for metering modes. Load Needle Valve Hydraulic Piston Collaborators Fixed Arm Temperature Sensor J.D. Huggins Sponsors: HUSCO International and FPMC Center Advisors: Dr. Nader Sadegh, Dr. Wayne Book Email: gte608g@mail.gatech.edu website: http://www.imdl.gatech.edu/opdenbosch Spring 2005