Multi-Input–Multi-Output Control of a Utility-Scale, Shaftless Energy Storage Flywheel With a Five-Degrees-of-Freedom Combination Magnetic Bearing

2018 ◽  
Vol 140 (10) ◽  
Author(s):  
Xiaojun Li ◽  
Alan Palazzolo
Keyword(s):  
Energy Storage ◽  
Degrees Of Freedom ◽  
Magnetic Levitation ◽  
High Strength ◽  
Magnetic Bearing ◽  
Detailed Model ◽  
Loop Control ◽  
Modeling And Control ◽  
Power Capability ◽  
Utility Scale

The modeling and control of a recently developed utility-scale, shaftless, hubless, high strength steel energy storage flywheel system (SHFES) are presented. The novel flywheel is designed with an energy/power capability of 100 kWh/100 kW and has the potential of a doubled energy density when compared to conventional technologies. In addition, it includes a unique combination magnetic bearing (CAMB) capable of providing five-degrees-of-freedom (5DOF) magnetic levitation. Initial test results show that the CAMB, which weighs 544 kg, can provide a stable lift-up and levitation control for the 5543 kg flywheel. The object of this paper is to formulate and synthesize a detailed model as well as to design and simulate a closed-loop control system for the proposed flywheel system. To this end, the CAMB supporting structures are considered flexible and modeled by finite element modeling. The magnetic bearing is characterized experimentally by static and frequency-dependent coefficients, the latter of which are caused by eddy current effects and presents a challenge to the levitation control. Sensor-runout disturbances are also measured and included. System nonlinearities in power amplifiers and the controller are considered as well. Even though the flywheel has a large ratio of the primary-to-transversal moment of inertias, multi-input–multi-output (MIMO) feedback control demonstrates its effectiveness in canceling gyroscopic toques at the designed operational spinning speed. Various stages of proportional and derivative (PD) controllers, lead/lag compensators, and notch filters are implemented to suppress the high-frequency sensor disturbances, structural vibrations, and rotor imbalance effects.

Structural optimization and dynamic characteristics of the new type 3-degrees of freedom axial and radial hybrid magnetic bearing

2021 ◽  
pp. 095440622110528
Author(s):  
Xiao Ling ◽  
He Xiwu ◽  
Cheng Wenjie ◽  
Li Ming
Keyword(s):  
Energy Storage ◽  
Dynamic Characteristics ◽  
Eddy Current ◽  
Degrees Of Freedom ◽  
Storage System ◽  
Energy Storage System ◽  
Magnetic Bearing ◽  
Flywheel Energy Storage ◽  
Flywheel Energy Storage System ◽  
New Type

A new type of three degrees of freedom axial-radial hybrid magnetic bearing (3-DOF ARHMB) with compact structure, shorter axial length and smaller volume is proposed for the flywheel energy storage system. The axial direction adopts the permanent magnet biased thrust bearing (PMB) made of soft magnetic composite materials (SMCs). In the radial direction, the laminated structure is used to reduce the eddy current, and the Halbach array is introduced to strengthen the magnetic density of the radial air gap. Firstly, the dynamic magnetic flux distribution of the 3-DOF ARHMB is analyzed by the finite element method (FEM). Based on the equivalent magnetic circuit method, the equivalent reluctance model with comprehensive consideration of eddy current effect and magnetic leakage effect is established, and then the frequency responses are analyzed. Secondly, a constraint model coupled with structural parameters, equivalent reluctance and magnetic leakage coefficient is established, and an adaptive particle swarm optimization algorithm (APSO) is used to optimize the bearing parameters. Finally, based on the equivalent reluctance model, the axial and radial force-current factor and force-displacement factor are derived, and the dynamic characteristics of bearings with different structures and materials are compared and analyzed. The results show that the new 3-DOF ARHMB made of SMCs can provide much larger and more stable magnetic force and larger bandwidth than that made of carbon steel materials, and has better dynamic characteristics under higher-frequency conditions, which can meet the industrial requirements of flywheel energy storage system.

Cycle-to-Cycle Control of HCCI Engines

2003 ◽  
Author(s):  
Gregory M. Shaver ◽  
J. Christian Gerdes
Keyword(s):  
Fuel Injection ◽  
Molar Ratio ◽  
Exhaust Gas ◽  
Cylinder Pressure ◽  
Isentropic Compression ◽  
Detailed Model ◽  
Loop Control ◽  
Modeling And Control ◽  
Control Approach ◽  
Variable Valve Actuation

With stated benefits ranging from increased thermal efficiency to significantly reduced NOx emissions, Homogeneous Charge Compression Ignition (HCCI) represents a promising combustion strategy for future engines. When achieved by reinducting exhaust gas with a variable valve actuation (VVA) system, however, HCCI possesses nonlinear cycle-to-cycle coupling through the exhaust gas and lacks an easily identified trigger comparable to spark or fuel injection. This makes closed-loop control decidedly nontrivial. To develop a controller for HCCI, the engine cycle is partitioned into five stages: adiabatic, constant pressure induction of re-inducted product and reactant charge; isentropic compression to the point just prior to combustion initiation; constant volume combustion; isentropic expansion of product gases; isentropic exhaust of product gases. Using this framework, a nonlinear low-order model of HCCI combustion is formulated, where the input is the molar ratio of reinducted products to fresh reactants and the output is the peak in-cylinder pressure. Comparison with experimental in-cylinder pressure data shows that the model, while simple, offers reasonable fidelity. Using the nonlinear model, a linearized model and an accompanying LQR controller are formulated and implemented on a more detailed model presented in previous work. Results from these simulations show that the modeling and control approach is indeed successful at tracking a varying desired work output while maintaining a constant desired combustion phasing.

An Efficient Approach for Modeling and Control of a Quadrotor

2016 ◽  
Vol 4 (2) ◽  
pp. 1-16
Author(s):  
Ahmed S. Khusheef
Keyword(s):  
Pid Control ◽  
Control Method ◽  
Mechanical Design ◽  
Loop Control ◽  
Modeling And Control ◽  
Loop Control System ◽  
And Control ◽  
Close Loop Control ◽  
Close Loop Control System ◽  
Made In

 A quadrotor is a four-rotor aircraft capable of vertical take-off and landing, hovering, forward flight, and having great maneuverability. Its platform can be made in a small size make it convenient for indoor applications as well as for outdoor uses. In model there are four input forces that are essentially the thrust provided by each propeller attached to each motor with a fixed angle. The quadrotor is basically considered an unstable system because of the aerodynamic effects; consequently, a close-loop control system is required to achieve stability and autonomy. Such system must enable the quadrotor to reach the desired attitude as fast as possible without any steady state error. In this paper, an optimal controller is designed based on a Proportional Integral Derivative (PID) control method to obtain stability in flying the quadrotor. The dynamic model of this vehicle will be also explained by using Euler-Newton method. The mechanical design was performed along with the design of the controlling algorithm. Matlab Simulink was used to test and analyze the performance of the proposed control strategy. The experimental results on the quadrotor demonstrated the effectiveness of the methodology used.

scholarly journals Task space adaptation via the learning of gait controllers of magnetic soft millirobots

2021 ◽  
pp. 027836492110218
Author(s):  
Sinan O. Demir ◽  
Utku Culha ◽  
Alp C. Karacakol ◽  
Abdon Pena-Francesch ◽  
Sebastian Trimpe ◽  
...  
Keyword(s):  
Human Body ◽  
Degrees Of Freedom ◽  
Bayesian Optimization ◽  
Small Scale ◽  
Soft Robots ◽  
Modeling And Control ◽  
Walking Gait ◽  
Physical Experiments ◽  
Efficient Learning ◽  
Task Environments

Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can directly and non-invasively access confined and hard-to-reach spaces in the human body. For such potential biomedical applications, the adaptivity of the robot control is essential to ensure the continuity of the operations, as task environment conditions show dynamic variations that can alter the robot’s motion and task performance. The applicability of the conventional modeling and control methods is further limited for soft robots at the small-scale owing to their kinematics with virtually infinite degrees of freedom, inherent stochastic variability during fabrication, and changing dynamics during real-world interactions. To address the controller adaptation challenge to dynamically changing task environments, we propose using a probabilistic learning approach for a millimeter-scale magnetic walking soft robot using Bayesian optimization (BO) and Gaussian processes (GPs). Our approach provides a data-efficient learning scheme by finding the gait controller parameters while optimizing the stride length of the walking soft millirobot using a small number of physical experiments. To demonstrate the controller adaptation, we test the walking gait of the robot in task environments with different surface adhesion and roughness, and medium viscosity, which aims to represent the possible conditions for future robotic tasks inside the human body. We further utilize the transfer of the learned GP parameters among different task spaces and robots and compare their efficacy on the improvement of data-efficient controller learning.

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