Modeling and Control of a 1-D Membrane Strip With an Integrated PZT Bimorph

Aerospace ◽  
2005 ◽  
Author(s):  
Eric J. Ruggiero ◽  
Daniel J. Inman
Keyword(s):  
Thin Strip ◽  
Effective Control ◽  
Fe Model ◽  
Modeling And Control ◽  
Mission Success ◽  
Lqr Controller ◽  
Membrane Sample ◽  
Strip Sample ◽  
Minimal Mass ◽  
Membrane Strip

Ultra-lightweight, ultra-large and deployable satellite technology is at the forefront of research efforts for future on-orbit reconnaissance missions. The minimal mass and stowage volume associated with the technology are attractive traits for getting larger bandwidth satellites on-orbit. One of the key components for such a satellite is the membrane lens or aperture for optical or radar applications, and understanding the membrane’s dynamics is critical for mission success. As either an optical reflector or radar antenna, the vibration levels of the membrane must be minimized and eliminated. This work examines the possibility of integrating a PZT bimorph near the boundary of a strip sample to eliminate detrimental vibration. By starting with a 1-D model, the dominant governing phenomena of the system dynamics can be established and used to build more complex models with confidence. A physics-based finite element (FE) model of a thin strip of Kapton HN material with a monolithic PZT bimorph bonded near a boundary is developed in a MatLab environment and verified experimentally. The membrane strip under tension is modeled as a beam under axial load. In doing so, the FE model is able to capture the relevant transverse dynamics of the experimental setup. Having verified the FE model, an LQR controller is developed and simulated to demonstrate effective control over the transverse dynamics of the membrane sample.

scholarly journals Nonlinear Control Design of a Half-Car Model Using Feedback Linearization and an LQR Controller

2020 ◽  
Vol 10 (9) ◽  
pp. 3075
Author(s):  
Muhammad Aseer Khan ◽  
Muhammad Abid ◽  
Nisar Ahmed ◽  
Abdul Wadood ◽  
Herie Park
Keyword(s):  
Feedback Linearization ◽  
Control Method ◽  
Control Technique ◽  
Effective Control ◽  
Ride Quality ◽  
Linear Quadratic ◽  
Suspension Systems ◽  
Car Model ◽  
Road Disturbance ◽  
Lqr Controller

Effective control of ride quality and handling performance are challenges for active vehicle suspension systems, particularly for off-road applications. The nonlinearities tend to degrade the performance of active suspension systems; these introduce harshness to the ride quality and reduce off-road mobility. Typical control strategies rely on linear models of the suspension dynamics. While these models are convenient, nominally accurate, and controllable due to the abundance of linear control techniques, they neglect the nonlinearities present in real suspension systems. The techniques already implemented and methods used to cope with problem of Half-Car model were studied. Every method and technique had some drawbacks in terms of complexity, cost-effectiveness, and ease of real time implementation. In this paper, an improved control method for Half-Car model was proposed. First, input/output feedback linearization was performed to convert the nonlinear system of Half-Car model into an equivalent linear system. This was followed by a Linear Quadratic Regulator (LQR) controller. This controller had minimized the effects of road disturbances by designing a gain matrix with optimal robustness properties. The proposed control technique was applied in the presence of the deterministic road disturbance. The results were verified using the Matlab/Simulink toolbox.

Real Time Modeling and Control of Three Tank Hybrid System

2018 ◽  
Vol 13 (1) ◽  
Author(s):  
K. Sathishkumar ◽  
V. Kirubakaran ◽  
T. K. Radhakrishnan
Keyword(s):  
Real Time ◽  
Controller Design ◽  
Linear Quadratic Regulator ◽  
Estimation Model ◽  
Linear Quadratic ◽  
Loop Control ◽  
Modeling And Control ◽  
Time Modeling ◽  
Lqr Controller ◽  
Servo Tracking

AbstractThis study discusses the modeling and linear quadratic regulator (LQR) controller based closed loop control of a three tank hybrid (TTH) process. A pseudo random binary signal (PRBS) based excitation data obtained from a real time TTH setup is utilized in validating its first principle model (FPM). Based on top and bottom interactions, various modes prevalent are considered based on steady state physical reachability analysis of the TTH for a given input range for controller design. The FPM is linearized using nominal values of process parameters using Jacobians from each existing mode. LQR controllers are designed for each mode. A supervisory structure is designed for selecting estimation model and controller for each appropriate mode. Results from real time servo tracking and disturbance rejection experiments are discussed.

scholarly journals A Top Side-Pouring Twin-Roll Caster for 3.0 Wt.% Si Steel

2021 ◽  
Author(s):  
Dongpo Xuan ◽  
cheng zhou ◽  
You Zhou ◽  
Tianliang Jiang ◽  
Biji Zhu ◽  
...  
Keyword(s):  
Finite Element ◽  
Three Dimensional ◽  
Solidification Structure ◽  
Thin Strip ◽  
Fe Model ◽  
Melt Pool ◽  
Roll Casting ◽  
Width Direction ◽  
Three Dimensional Finite Element ◽  
Twin Roll

Abstract Three-dimensional finite element modeling of twin-roll strip casting (TRC) and the top side-pouring twin-roll casting (TSTRC) were carried out to predict the temperature, flow, and turbulence kinetic energy of the two processes. The cellular automaton–finite element (CA-FE) model was established to predict the solidification structure of the two processes. By comparing the two processes, it was found that the temperature field distribution in the micro-melt pool during the TSTRC process was uniform. The temperature distribution in the width direction was also more uniform, and the stirring in the micro melt-pool was intense. An equiaxed crystal structure uniformly distributed in the width direction was obtained, and a thin strip of good quality was obtained. Not only the near-final forming of the thin strip was realized, but also the near-final forming of the solidified structure was realized. By comparing the experimental process of TSTRC with the simulation, it was found that the simulation and experimental results were in good agreement, which verified the feasibility and accuracy of the simulation.

Predicting the Peen Forming Effectiveness of Ti-6Al-4V Strips With Different Thicknesses Using Realistic Finite Element Simulations

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Fan Yang ◽  
Yukui Gao
Keyword(s):  
Finite Element ◽  
Numerical Study ◽  
Residual Stress Distribution ◽  
Strip Thickness ◽  
Thin Strip ◽  
Fe Model ◽  
Rate Dependent ◽  
Peen Forming ◽  
Proposed Model ◽  
The Relationship

This paper is intended to quantify the relationship between the peen forming effectiveness and various involved parameters through a realistic numerical study. For this purpose, a new finite element (FE) model is proposed with full geometry representation, random shots generation, and rate-dependent material law of kinematic strain-hardening. The mesh sensitivity and effects of boundary conditions are carefully examined. The FE model is validated by comparing the results with the experimental measurements. The proposed model is then used to investigate the effects of the peening intensity (represented as the shot velocity) and the strip thickness on the peen-formed deflection and the residual stress distribution for strips made of Ti-6Al-4V. Our results indicate the existence of a maximum convex deflection for different strip thicknesses. In addition, a reversed deflection (i.e., concaved curvature) is observed for severe peening conditions (i.e., thin strip under high peening intensity). Our simulations verify the previous proposition that a concaved curvature can be generated only when the whole cross section is plastically deformed.

scholarly journals Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and Control

2019 ◽  
Author(s):  
Jeremy Morton ◽  
Freddie D. Witherden ◽  
Mykel J. Kochenderfer
Keyword(s):  
Nonlinear Dynamical System ◽  
Effective Control ◽  
Dynamics Modeling ◽  
Dynamical Models ◽  
Nonlinear Dynamical ◽  
Modeling And Control ◽  
Koopman Operator ◽  
Control Framework ◽  
And Control ◽  
Long Term Prediction

Koopman theory asserts that a nonlinear dynamical system can be mapped to a linear system, where the Koopman operator advances observations of the state forward in time. However, the observable functions that map states to observations are generally unknown. We introduce the Deep Variational Koopman (DVK) model, a method for inferring distributions over observations that can be propagated linearly in time. By sampling from the inferred distributions, we obtain a distribution over dynamical models, which in turn provides a distribution over possible outcomes as a modeled system advances in time. Experiments show that the DVK model is effective at long-term prediction for a variety of dynamical systems. Furthermore, we describe how to incorporate the learned models into a control framework, and demonstrate that accounting for the uncertainty present in the distribution over dynamical models enables more effective control.

Modeling and Control Design for Active Trailer Steering of Heavy Vehicles

2017 ◽  
Author(s):  
Jesse Brown ◽  
Yuping He ◽  
Haoxiang Lang
Keyword(s):  
Degrees Of Freedom ◽  
Load Transfer ◽  
Linear Quadratic Regulator ◽  
Heavy Vehicles ◽  
Linear Quadratic ◽  
Modeling And Control ◽  
Software Packages ◽  
Lqr Controller ◽  
Control Evaluation ◽  
And Control

This paper presents a linear quadratic regulator (LQR) controller for active trailer steering (ATS) of a tractor-semitrailer. The tractor-semitrailer is modelled as a linear yaw/roll model with 5 Degrees-Of-Freedom (DOF). The linear yaw/roll model is validated with a nonlinear tractor-semitrailer model developed with TruckSim under a simulated single lane-change maneuver. Then, the validated linear yaw/roll model is used to design the LQR controller for ATS. The TruckSim model and the LQR controller are integrated by means of an interface between the software packages of TruckSim and Matlab/Simulink. The LQR controller is assessed using numerical simulation of the TruckSim model with and without the ATS control. Evaluation of the controller is based on the performance measures of the trailer in terms of rearward amplification (RA), peak roll angle, and load transfer ratio (LTR). It is demonstrated that the LQR controller leads to the decrease the peak values of the aforementioned measures by 4.81%, 20.7%, and 33%, respectively.

Dynamic Control of Soft Robots Using Reinforcement Learning

2019 ◽  
Author(s):  
Ben Pawlowski ◽  
Charles W. Anderson ◽  
Jianguo Zhao
Keyword(s):  
Reinforcement Learning ◽  
Dynamic Control ◽  
Soft Materials ◽  
Analytic Solutions ◽  
Effective Control ◽  
Soft Robots ◽  
Modeling And Control ◽  
Infinite Degree ◽  
Computationally Intensive ◽  
And Control

Abstract Soft robots made from soft materials recently attracted tremendous research owing to their unique softness compared with rigid robots, making them suitable for applications such as manipulation and locomotion. However, also due to their softness, the modeling and control of soft robots present a significant challenge because of the infinite degree of freedom. In this case, although analytic solutions can be derived for control, they are too computationally intensive for real-time application. In this paper, we aim to leverage reinforcement learning to approach the control problem. We gradually increase the complexity of the control problems to learn. We also test the effectiveness and efficiency of reinforcement learning techniques to the control of soft robots for different tasks. Simulation results show that the control commands to be computed in milliseconds, allowing effective control of soft manipulators, up to trajectory tracking.

scholarly journals Intelligent Manipulator with Flexible Link and Joint: Modeling and Vibration Control

2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Minqiang Shao ◽  
Yuming Huang ◽  
Vadim V. Silberschmidt
Keyword(s):  
Mass Matrix ◽  
Control Design ◽  
Joint Modeling ◽  
Fe Model ◽  
Output Function ◽  
Flexible Link ◽  
Flexible Joint ◽  
Lqr Controller ◽  
Simulation Results ◽  
Improved Model

This paper presents a finite-element (FE) model of a manipulator with a flexible link and flexible joint as well as embedded PZT actuators and proposes a corrected rebuilt reduced model (CRRM) to make its dynamic characteristics more consistent with reality and facilitate control design. The CRRM considers the holding torque of the manipulator driving motor and eliminates the response divergence induced by a fault of the mass matrix of the FE model. In order to reduce the dimensions and maintain the precision of the model, an iterated improved reduction system (IIRS) method is adopted. Additionally, a LQR controller is designed based on the output function of the improved model. The simulation results demonstrate that the CRRM is consistent with reality and the active controller has good performance in suppressing vibration of the manipulator with both the flexible link and the flexible joint.

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