Fixed-order data-driven H∞ controller synthesis for flexible mechanical systems: Two-stage approach

In this paper, a two-stage method is introduced to design fixed-order data-driven [Formula: see text] controller for flexible mechanical systems. In the first stage of the proposed method, unknown parameters of anti-resonance filter that is added to the forward path of the control loop of the system to minimize resonant peaks, are calculated using frequency domain data obtained from open-loop system identification tests. In the second stage, a fixed-order data-driven [Formula: see text] controller is calculated by solving an optimization problem under convex [Formula: see text] constraints obtained based on the Nyquist diagram. With the proposed method, lower order controllers that meets the performance constraints of classical model-based [Formula: see text] problems can be synthesized without need of a parametric plant model. The method developed in this study is tested experimentally on a military stabilized platform and its performance is compared with a model-based [Formula: see text] controller design method.

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Model-Based Control of Combustion Instabilities

Volume 1: Turbo Expo 2005 ◽

10.1115/gt2005-68897 ◽

2005 ◽

Cited By ~ 6

Author(s):

Aimee S. Morgans ◽

Ann P. Dowling

Keyword(s):

Transfer Function ◽

Controller Design ◽

Open Loop ◽

Operating Conditions ◽

Combustion Instabilities ◽

Lean Premixed Combustion ◽

Model Based Control ◽

Model Based ◽

Loop Transfer Function ◽

Mathematical Approximation

Model-based control has been successfully implemented on an atmospheric pressure lean premixed combustion rig. The rig incorporated a pressure transducer in the combustor to provide a sensor measurement, with actuation provided by a fuel valve. Controller design was based on experimental measurement of the open loop transfer function. This was achieved using a valve input signal which was the sum of an identification signal and a control signal from an empirical controller to eliminate the non-linear limit cycle. The transfer function was measured for the main instability occurring at a variety of operating conditions, and was found to be fairly similar in all cases. Using Nyquist and H∞-loop shaping techniques, several robust controllers were designed, based on a mathematical approximation to the measured transfer function. These were implemented experimentally on the rig, and were found to stabilise it under a variety of operating conditions, with a greater reduction in the pressure spectrum than had been achieved by the empirical controller.

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Development of optimal controller design method to compensate for vibrations caused by unbalanced force in rotor system based on Nyquist diagram

Journal of Vibration and Control ◽

10.1177/1077546318797173 ◽

2018 ◽

Vol 25 (4) ◽

pp. 793-805 ◽

Cited By ~ 3

Author(s):

Shota Yabui ◽

Tsuyoshi Inoue

Keyword(s):

Controller Design ◽

Design Method ◽

Active Vibration Control ◽

Open Loop ◽

Rotor System ◽

Optimal Controller ◽

Stable Operation ◽

Nyquist Diagram ◽

Unbalanced Force ◽

Active Vibration

In this paper, an optimal controller design method is proposed to compensate for vibrations caused by unbalanced force in the rotor system. The vibrations caused by unbalanced force are the major root cause of excessive whirling vibration in the rotor system, and it is important to compensate for the vibration to maintain its stable operation. The proposed design method can optimize a performance of the controller based on the vector locus of open loop characteristics on the Nyquist diagram. To verifiy the effectiveness, the proposed design method was employed for three typical active vibration control methods. The experimental results show that the proposed method can design the optimal parameters to compensate for the whirling vibrations of the rotor system.

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Development of an Adaptive Learning Support System based on Model-Based Development (MBD) Approach

Impact ◽

10.21820/23987073.2019.10.99 ◽

2019 ◽

Vol 2019 (10) ◽

pp. 99-101

Author(s):

Shin Wakitani

Keyword(s):

Support System ◽

Adaptive Learning ◽

Controller Design ◽

System Development ◽

Global Scale ◽

Data Driven ◽

Learning Support ◽

Control Engineering ◽

Model Based ◽

Learning Support System

Education is of the highest importance to the world we live in. If ways can be found to elevate the level of education within any given country, the individuals of that country will naturally benefit, but so too will the country as a whole, as it increases its competitiveness on a global scale across a broad range of areas. For example, in Japan there are general guidelines for learning that state the ways in which students are educated should be adjusted according to the needs of each individual. Dr Shin Wakitani is a control engineering specialist with a particular interest in data-driven controller design and model-based development (MBD) education. Wakitani leads a team based at Hiroshima University in Japan, which is investigating a means of establishing an adaptive learning support system development method.

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Iterative Data-Driven Control for Closed Loop with Two Unknown Controllers

Mathematical Problems in Engineering ◽

10.1155/2021/4827900 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Hong Jianwang ◽

Ricardo A. Ramirez-Mendoza ◽

Ruben Morales-Menendez

Keyword(s):

Stability Analysis ◽

Unknown Parameter ◽

Closed Loop ◽

Transfer Functions ◽

Controller Design ◽

Classical Model ◽

Data Driven ◽

Model Matching ◽

Matching Problem ◽

Sensitivity Functions

Iterative idea is combined with data-driven control and is used to design the feedforward controller and feedback controller simultaneously. Consider one closed loop system with two controllers, the classical model-based control holds on the condition of known plant. To alleviate the modeling process for plant, data-driven control is applied to design the two controllers. After these two controllers are parametrized by two unknown parameter vectors, the iterative idea is introduced to identify these two parameter vectors. Furthermore, for more general case of controllers, the closed relations between controllers and expected transfer functions are derived. Then, the iterative idea is also introduced to achieve the controller design. To be of benefit for latter stability analysis, some equities are derived for output-input sensitivity functions with three kinds of disturbances. Generally, after formulating the problem of the controller design as one model-matching problem, the purpose of this paper is threefold. First, we derive that, in case of two parametrized controllers, the iterative idea is performed to identify these two unknown parameter vectors, even when parameters converge to their true values. Second, we show how to design the two controllers iteratively for more general forms and find the closed relations between these controllers and expected closed loop transfer functions. Third, we provide some heuristic considerations on output-input sensitivity functions, which are of benefit for our stability analysis on data-driven control. Finally, one example is given to show the feasibility of our proposed theories.

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Model-based control of the substrate concentration in an aeration basin using the wastewater level

Water Science & Technology ◽

10.2166/wst.1998.0559 ◽

1998 ◽

Vol 37 (12) ◽

pp. 335-342 ◽

Cited By ~ 2

Author(s):

Jacek Czeczot

Keyword(s):

Substrate Concentration ◽

Consumption Rate ◽

Least Square Method ◽

Estimation Procedure ◽

Adaptive Controller ◽

Open Loop ◽

Least Square ◽

Substrate Consumption ◽

Model Based ◽

Effluent Flow Rate

This paper deals with the minimal-cost control of the modified activated sludge process with varying level of wastewater in the aerator tank. The model-based adaptive controller of the effluent substrate concentration, basing on the substrate consumption rate and manipulating the effluent flow rate outcoming from the aerator tank, is proposed and its performance is compared with conventional PI controller and open loop behavior. Since the substrate consumption rate is not measurable on-line, the estimation procedure on the basis of the least-square method is suggested. Finally, it is proved that cooperation of the DO concentration controller with the adaptive controller of the effluent substrate concentration allows the process to be operated at minimum costs (low consumption of aeration energy).

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The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Sensors ◽

10.3390/s21062085 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2085

Author(s):

Xue-Bo Jin ◽

Ruben Jonhson Robert RobertJeremiah ◽

Ting-Li Su ◽

Yu-Ting Bai ◽

Jian-Lei Kong

Keyword(s):

Kalman Filter ◽

State Estimation ◽

Future Development ◽

Data Driven ◽

Estimation Methods ◽

Multiple Model ◽

Development Trends ◽

Research Results ◽

Model Driven ◽

Model Based

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

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