scholarly journals Mixture Based Outlier Filtration

10.14311/816 ◽  
2006 ◽  
Vol 46 (2) ◽  
Author(s):  
P. Pecherková ◽  
I. Nagy
Keyword(s):  
Model Identification ◽  
Real Data ◽  
Control Algorithms ◽  
Linear Quadratic ◽  
Process Data ◽  
Linear Quadratic Gaussian ◽  
Standard Methods ◽  
Two Component ◽  
Reconstruction Filter

Success/failure of adaptive control algorithms – especially those designed using the Linear Quadratic Gaussian criterion – depends on the quality of the process data used for model identification. One of the most harmful types of process data corruptions are outliers, i.e. ‘wrong data’ lying far away from the range of real data. The presence of outliers in the data negatively affects an estimation of the dynamics of the system. This effect is magnified when the outliers are grouped into blocks. In this paper, we propose an algorithm for outlier detection and removal. It is based on modelling the corrupted data by a two-component probabilistic mixture. The first component of the mixture models uncorrupted process data, while the second models outliers. When the outlier component is detected to be active, a prediction from the uncorrupted data component is computed and used as a reconstruction of the observed data. The resulting reconstruction filter is compared to standard methods on simulated and real data. The filter exhibits excellent properties, especially in the case of blocks of outliers. 

State-Space Dynamic Performance Preview-Predictive Controller

2006 ◽  
Vol 129 (2) ◽  
pp. 144-153 ◽  
Author(s):  
Andrzej W. Ordys ◽  
Masayoshi Tomizuka ◽  
Michael J. Grimble
Keyword(s):  
State Space ◽  
Predictive Control ◽  
Performance Index ◽  
Dynamic Performance ◽  
Optimization Procedure ◽  
Control Algorithms ◽  
Optimization Approach ◽  
Linear Quadratic ◽  
Preview Control ◽  
Predictive Controller

The paper discusses state-space generalized predictive control and the preview control algorithms. The optimization procedure used in the derivation of predictive control algorithms is considered. The performance index associated with the generalized predictive controller (GPC) is examined and compared with the linear quadratic (LQ) optimal control formulation used in preview control. A new performance index and consequently a new algorithm is proposed dynamic performance predictive controller (DPPC) that combines the features of both GPC and preview controller. This algorithm minimizes the performance index through a dynamic optimization. A simple example illustrates the features of the three algorithms and prompts a discussion on what is actually minimized in predictive control. The DPPC algorithm, derived in this paper, provides for a minimum of the predictive performance index. The differences and similarities between the preview control and the predictive control have been discussed and optimization approach of predictive control has been explained.

Compensator Design for a MEMS Gyroscope With Quadratic Optimal Control

2010 ◽  
Author(s):  
Wei Cui ◽  
Wei Xue ◽  
Xiaolin Chen
Keyword(s):  
Dynamic Range ◽  
Linear Quadratic Regulator ◽  
Control Algorithms ◽  
System Response ◽  
Linear Quadratic ◽  
Control Effort ◽  
Mems Gyroscope ◽  
Compensator Design ◽  
Wide Range ◽  
Parameter Perturbations

A number of control algorithms have been reported to adopt force balancing scheme into MEMS vibratory gyroscope systems. In practice, however, many algorithms are difficult to implement with electronic circuits. This paper designs and analyzes a lead compensator for a MEMS gyroscope via the Linear Quadratic Regulator (LQR) technique. LQR optimizes and balances the control effort and system response swiftness. Simulation shows the gyroscope achieves high linearity, wide dynamic range, and high robustness to fabrication uncertainties with this efficient compensator design. The closed-loop scale factor uniformity error is 0.7% under ±10% parameter perturbations. The compensator designed in this paper exhibits comparable outstanding performance compared to other reported control algorithms. The method reported in this paper is proved to be effective and can be used in a wide range of applications.

scholarly journals A Novel Concept for Guidance and Control of Spacecraft Orbital Maneuvers

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Matteo Dentis ◽  
Elisa Capello ◽  
Giorgio Guglieri
Keyword(s):  
Attitude Control ◽  
Linear Quadratic Regulator ◽  
Control Algorithms ◽  
Linear Quadratic ◽  
C Language ◽  
Orbital Maneuvers ◽  
Guidance And Control ◽  
Actuation System ◽  
Novel Concept ◽  
And Control

The purpose of this paper is the design of guidance and control algorithms for orbital space maneuvers. A 6-dof orbital simulator, based on Clohessy-Wiltshire-Hill equations, is developed in C language, considering cold gas reaction thrusters and reaction wheels as actuation system. The computational limitations of on-board computers are also included. A combination of guidance and control algorithms for an orbital maneuver is proposed: (i) a suitably designed Zero-Effort-Miss/Zero-Effort-Velocity (ZEM/ZEV) algorithm is adopted for the guidance and (ii) a linear quadratic regulator (LQR) is used for the attitude control. The proposed approach is verified for different cases, including external environment disturbances and errors on the actuation system.

Robust Observer-Based Load Extenuation Control for Wind Turbines

2019 ◽  
Author(s):  
M. Hung Do ◽  
Dirk Söffker
Keyword(s):  
Wind Turbines ◽  
Large Scale ◽  
Linear Models ◽  
Simulation Software ◽  
Control Algorithms ◽  
Linear Quadratic ◽  
Robust Observer ◽  
Wind Turbine Control ◽  
Speed Regulation ◽  
The Stability

Abstract Wind energy is currently the fastest growing electricity source. To meet the output demand, wind turbines are becoming larger and more flexible leading to the problems of structural load especially in case of offshore turbines. Advanced control algorithms are developed to reduce the load, allowing to build larger turbines, and expand their lifetime. Observer-based control algorithms such as Linear-Quadratic-Gaussian LQG control which uses LQR to calculate the optimal observer and controller gains are commonly applied to wind turbines in literature. However the approach requires to calculate the observer and control gains separately. In addition, linear models used for parameter calculation may have errors with respect to the nonlinearities of wind turbines and induced to unmodeled dynamical properties. These modeling errors need to be considered to to guarantee the stability of the controlled system. Alternatively a robust design assuming bounds and limits of models have to be realized to guarantee stability while ignoring details of modeling. This paper proposes an optimal robust observer-based state feedback controller for large-scale wind turbines to realize multi objectives, including structural load mitigation and rotor speed regulation. The novel contribution is that the observer gain parameters, control gains, and integral action are optimized at the same time within H∞ mixed sensitivity framework to achieve desired performance with respect to power regulation, structural load mitigation, and also robustness for the wind turbine control system. The control performances have been verified by a high fidelity simulation software and are compared to those of a classical baseline controller.

Mixed Skyhook and Power-Driven-Damper: A New Low-Jerk Semi-Active Suspension Control Based on Power Flow Analysis

2016 ◽  
Vol 138 (8) ◽  
Author(s):  
Yilun Liu ◽  
Lei Zuo
Keyword(s):  
Active Control ◽  
Power Flow ◽  
Flow Analysis ◽  
Linear Quadratic Regulator ◽  
Control Algorithms ◽  
Linear Quadratic ◽  
Active Suspensions ◽  
Switching Law ◽  
Suspension Control ◽  
Optimal Linear

In practice, semi-active suspensions provide better tradeoffs between performances and costs than passive or active damping. Many different semi-active control algorithms have been developed, including skyhook (SH), acceleration-driven-damper (ADD), power-driven-damper (PDD), mixed SH and ADD (SH-ADD), and others. Among them, it has been shown that the SH-ADD is quasi-optimal in reducing the sprung mass vibration. In this paper, we analyze the abilities of vehicular suspension components, the shock absorber and the spring, from the perspective of energy transfer between the sprung mass and the unsprung mass, and propose a new sprung mass control algorithm named mixed SH and PDD (SH-PDD). The proposed algorithm defines a switching law that is capable of mixing SH and PDD, and simultaneously carries their advantages to achieve a better suspension performance. As a result, the proposed SH-PDD is effective in reducing the sprung mass vibration across the whole frequency spectrum, similar to SH-ADD and much better than SH, PDD, and ADD, while eliminating the control chattering and high-jerk behaviors as occurred in SH-ADD. The superior characteristics of the SH-PDD are verified in numerical analysis as well as experiments. In addition, the proposed switching law is extended to mix other semi-active control algorithms such as the mixed hard damping and soft damping, and the mixed SH and clipped-optimal linear quadratic regulator (LQR).

scholarly journals A Review of Control Algorithm for Autonomous Guided Vehicle

2020 ◽  
Vol 20 (1) ◽  
pp. 552
Author(s):  
Faiza Gul
Keyword(s):  
System Integration ◽  
Neural Control ◽  
Sliding Mode ◽  
Linear Quadratic Regulator ◽  
Control Algorithms ◽  
Linear Quadratic ◽  
Advantages And Disadvantages ◽  
Autonomous Guided Vehicle ◽  
Back Stepping Control ◽  
Linear Nature

The autonomous guided vehicle is a efficient and<br />effective platform for control system. Their non-linear nature helps<br />in analysing the control algorithms more efficiently and effectively.<br />The main objective of path planning is to find the optimal and<br />shortest path avoiding the time complexity so environment can be<br />modelled completely for vehicle. The paper includes explanation<br />of different systems together with numerous algorithms have been<br />discussed with advantages and disadvantages for example: Fuzzy<br />control, Neural Control, Back-stepping control, Adaptive control,<br />Sliding mode control and PID control and linear quadratic regulator.<br />The conclusion includes the hybrid system integration based<br />on the advantages and disadvantages presented in this paper.

scholarly journals Fractional Order Control Methodology for the Self-Balance Robot.

2020 ◽  
Vol 9 (4) ◽  
pp. 276-381
Keyword(s):  
Fractional Order ◽  
Control Algorithm ◽  
Research Paper ◽  
Fractional Order System ◽  
The Self ◽  
Order System ◽  
Control Algorithms ◽  
Linear Quadratic ◽  
Integer Order ◽  
Fractional Order Controller

In this research paper the control algorithms like LQR and PID has been proposed for the integer and fractional order system. In this research paper the modeling of the selfbalance robot system has been carried out in integer domain and fractional domain. This research paper presents the simulation analysis of control algorithms for two wheel self-balancing robot using Linear Quadratic Regulator, Proportional-IntegralDerivative and Fractional order Proportional-Integral-Derivative control algorithm. These all control algorithm are applied on the integer order system and the fractional order system and comparative analysis has been done. The comparison between integer order PID against the fractional order PID is also been made for the self-balance robot. It has been demonstrated through simulation that fractional order controller gives better response as compared to integer order controller. Further it has been found out that fractional order controller gives better results when applied to fractional order system compared to its integer order counterpart.

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