Robust Control of a Parallel- Kinematic Nanopositioner

2008 ◽  
Vol 130 (4) ◽  
Author(s):  
Jingyan Dong ◽  
Srinivasa M. Salapaka ◽  
Placid M. Ferreira
Keyword(s):  
Robust Control ◽  
Mimo System ◽  
Model Identification ◽  
Control Design ◽  
Coupled Dynamics ◽  
Strongly Coupled ◽  
Control Approach ◽  
Domain Identification ◽  
Parallel Kinematic ◽  
And Control

This paper presents the design, model identification, and control of a parallel-kinematic XYZ nanopositioning stage for general nanomanipulation and nanomanufacturing applications. The stage has a low degree-of-freedom monolithic parallel-kinematic mechanism featuring single-axis flexure hinges. The stage is driven by piezoelectric actuators, and its displacement is detected by capacitance gauges. The control loop is closed at the end effector instead of at each joint, so as to avoid calibration difficulties and guarantee high positioning accuracy. This design has strongly coupled dynamics with each actuator input producing in multiaxis motions. The nanopositioner is modeled as a multiple input and multiple output (MIMO) system, where the control design forms an important constituent in view of the strongly coupled dynamics. The dynamics that model the MIMO plant is identified by frequency domain and time-domain identification methods. The control design based on modern robust control theory that gives a high bandwidth closed loop nanopositioning system, which is robust to physical model uncertainties arising from flexure-based mechanisms, is presented. The bandwidth, resolution, and repeatability are characterized experimentally, which demonstrate the effectiveness of the robust control approach.

MIMO H∞ Control of a Parallel Kinematic XYZ Nano-Positioner

2007 ◽  
Author(s):  
Jingyan Dong ◽  
Srinivasa M. Salapaka ◽  
Placid M. Ferreira
Keyword(s):  
Robust Control ◽  
Closed Loop ◽  
Mimo System ◽  
Control Design ◽  
Piezoelectric Actuators ◽  
Coupled Dynamics ◽  
Strongly Coupled ◽  
Domain Identification ◽  
Parallel Kinematic ◽  
Control Configuration

This paper presents the design, model identification and control of a parallel-kinematics XYZ nano positioning stage for general nano-manipulation and nano-manufacturing applications. The stage features a low degree of freedom monolithic parallel kinematic mechanism with flexure joints. The stage is driven by piezoelectric actuators and its displacement is detected by capacitance gauges. The control loop is closed at the end-effector instead of the each joint, so as to avoid calibration difficulties and guarantee high positioning accuracy. Instead of a single input and single output (SISO) system with joint space control configuration, this design has strongly coupled dynamics with each actuator input producing along multiple axes. The nano-positioner is modeled as a multiple input and multiple output (MIMO) system, where the control design forms an important constituent that accounts for the strongly coupled dynamics. The dynamics that model the MIMO plant is identified by time-domain identification method. A pseudo-random binary signal is used to excite the system while avoiding violent vibrations at resonant frequencies, which comes from the low damping feature of flexure based structure. The order of the model is reduced to make controller efficient and implementable. The control design based on modern robust control theory that gives a high bandwidth closed loop nanopositioning system which is robust to physical model uncertainties arising from flexure-based mechanisms is presented. The nonlinear effects from piezoelectric actuators, such as hysteresis and creep, are compensated effectively by closed loop robust controller. The bandwidth, resolution and repeatability are characterized experimentally, which demonstrate the effectiveness of the robust control approach.

Design and Control of a Compact and Flexible Pneumatic Artificial Muscle Actuation System: Part Two—Robust Control

2011 ◽  
Author(s):  
Sai-Kit Wu ◽  
Garrett Waycaster ◽  
Tad Driver ◽  
Xiangrong Shen
Keyword(s):  
Robust Control ◽  
Nonlinear Model ◽  
Sliding Mode ◽  
Artificial Muscle ◽  
Control Approach ◽  
Actuation System ◽  
Highly Nonlinear ◽  
Pressure Dynamics ◽  
System Part ◽  
And Control

A robust control approach is presented in this part of the paper, which provides an effective servo control for the novel PAM actuation system presented in Part I. Control of PAM actuation systems is generally considered as a challenging topic, due primarily to the highly nonlinear nature of such system. With the introduction of new design features (variable-radius pulley and spring-return mechanism), the new PAM actuation system involves additional nonlinearities (e.g. the nonlinear relationship between the joint angle and the actuator length), which further increasing the control difficulty. To address this issue, a nonlinear model based approach is developed. The foundation of this approach is a dynamic model of the new actuation system, which covers the major nonlinear processes in the system, including the load dynamics, force generation from internal pressure, pressure dynamics, and mass flow regulation with servo valve. Based on this nonlinear model, a sliding mode control approach is developed, which provides a robust control of the joint motion in the presence of model uncertainties and disturbances. This control was implemented on an experimental setup, and the effectiveness of the controller demonstrated by sinusoidal tracking at different frequencies.

Model Identification and Control Design Kit

1994 ◽  
Vol 27 (8) ◽  
pp. 1617
Author(s):  
S. Carabelli ◽  
C. Greco
Keyword(s):  
Model Identification ◽  
Control Design ◽  
And Control

SISO Model Identification of a Micro Air Vehicle for Robust Control Design

2015 ◽  
Vol 220-221 ◽  
pp. 470-478
Author(s):  
Arkadiusz Mystkowski
Keyword(s):  
Robust Control ◽  
Delta Wing ◽  
Angular Rate ◽  
Model Identification ◽  
Control Design ◽  
Real Data ◽  
Micro Air Vehicle ◽  
Lateral Direction ◽  
Air Vehicle ◽  
Robust Control Design

This paper describes a model identification method of a Micro Air Vehicle (MAV) for a further purpose of the robust control design. The identification procedure is based on logged real data collected from flight tests. For the identification purpose, both frequency and time-domain techniques are used. Identification results are presented for the lateral direction dynamics using the elevon (aileron deflection) as input and the roll angular rate as output. The MAV is a single-delta wing of the 0.84 m wingspan and 1.2 kg of total mass (ready to flight). The main goal of the system identification process is to achieve the best possible fit between the raw data and a dynamic model which is called a nominal model.

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