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Analysis of output response of nonlinear systems using nonlinear output frequency response functions

Analysis of output response of nonlinear systems using nonli...

引用

UKACC International Conference on control (control)

作者： Yunpeng Zhu Z Q Lang The Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

In the present study, the relationship between the Harmonic Balance Method (HBM) and the Nonlinear Output Frequency Response Functions (NOFRFs) approach when analysing the output response of nonlinear systems is investigated, showing that the output response described by the NOFRFs is the minimum solution of that from the HBM. After that, the convergent range of the NOFRFs is determined based on the proposed relationship, indicating that, outside this range, the validity of the NOFRFs method should be taken into account when used to represent the output response of nonlinear systems. Moreover, the effect of system damping on the convergence of the NOFRF approach is studied. The results imply that a nonlinear system with appropriate linear or nonlinear damping may have no convergence issue when being analysed using a NOFRFs-based method.

关键词： Nonlinear systems Oscillators Harmonic analysis Frequency-domain analysis Frequency response Time-domain analysis Damping

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Handling missing data in multivariate time series using a vector autoregressive model based imputation (VAR-IM) algorithm: Part I: VAR-IM algorithm versus traditional methods

Handling missing data in multivariate time series using a ve...

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Mediterranean Conference on control and Automation (MED)

作者： Faraj Bashir Hua-Liang Wei Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

ISBN: (纸本)9781467383479

Given an observed data set, there are different methods that can be used to impute missing data. While excellent work has been done in this field, most available approaches are focused on some particular applications, such as static data and univariate time series. The primary aim of the two papers Part I VAR-IM algorithm v.s. traditional methods and Part II VAR-IM algorithm v.s. modern algorithms - is to introduce an algorithm for handling missing data in multivariate time series based on vector autoregressive (VAR) model by combining an expectation and minimization (EM) algorithm with the prediction error minimization (PEM) method. In the first part, we conduct two cases studies (one for simulation data and another for real ECG data) to compare the proposed algorithm with three traditional methods for imputing missing data: Mean substitution, list-wise deletion and linear regression substitution. In the second part, the proposed method is compared with more powerful modern techniques: MARRS Package, nearest neighbour, and the full information maximum likelihood (FIML) method. Furthermore, we demonstrate the use of the proposed method together with an empirical example of multivariate time series to ECG data and discuss its advantages and limitations.

关键词： Time series analysis Data models Mathematical model Prediction algorithms Brain modeling Electrocardiography

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Model-based feature selection based on Radial Basis Functions and information measures

Model-based feature selection based on Radial Basis Function...

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： Georgios N. Tzagarakis George Panoutsos Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

ISBN: (纸本)9781509006274

In this paper the development of a new embedded feature selection method is presented, based on a Radial-Basis-Function Neural-Fuzzy modelling structure. The proposed method is created to find the relative importance of features in a given dataset (or process in general), with special focus on manufacturing processes. The proposed approach evaluates the impact/importance of processes features by using information theoretic measures to measure the correlation between the process features and the modelling performance. Crucially, the proposed method acts during the training of the process model; hence it is an embedded method, achieving the modelling/classification task in parallel to the feature selection task. The latter is achieved by taking advantage of the information in the output layer of the Neural Fuzzy structure; in the presented case this is a TSK-type polynomial function. Two information measures are evaluated in this work, both based on information entropy: mutual information, and cross-sample entropy. The proposed methodology is tested against two popular datasets in the literature (IRIS - plant data, AirFoil - manufacturing/design data), and one more case study relevant to manufacturing - the heat treatment of steel. Results show the good and reliable performance of the developed modelling structure, on par with existing published work, as well as the good performance of the feature selection task in terms of correctly identifying important process features.

关键词： Fuzzy logic Training Entropy Modeling Optimization Mutual information Noise measurement

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Fuzzy sliding control with non-linear observer for magnetic levitation systems

Fuzzy sliding control with non-linear observer for magnetic ...

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Mediterranean Conference on control and Automation (MED)

作者： A. M. Benomair A. R. Firdaus M. O. Tokhi Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield UK

Magnetic levitation (Maglev) systems make significant contribution to industrial applications due their reduced power consumption, increased power efficiency and reduced cost of maintenance. Common applications include Maglev power generation (e.g. wind turbine), Maglev trains and medical devices (e.g. magnetically suspended artificial heart pump). This paper proposes fuzzy sliding-mode controller `FSMC' with a nonlinear observer been used to estimate the unmeasured states. Simulations are performed with nonlinear mathematical model of the Maglev system, and the results show that the proposed observer and control strategy perform well.

关键词： Observers Magnetic levitation Sliding mode control Nonlinear dynamical systems Uncertainty

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Complexity certifications of inexact projection primal gradient method for convex problems: Application to embedded MPC

Complexity certifications of inexact projection primal gradi...

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Mediterranean Conference on control and Automation (MED)

作者： Andrei Patrascu Ion Necoara Automatic Control and Systems Engineering Department University Politehnica Bucharest Romania

ISBN: (纸本)9781467383479

In this paper we introduce a primal projected gradient method based on inexact projections for solving constrained convex problems. For this algorithm we prove sublinear rate of convergence when applied to problems with objective function being convex and having Lipschitz gradient. At each iteration, our method computes a gradient step towards the solution of the unconstrained problem and then projecting approximately this step onto the feasible set. We recast the inexact projection as approximately solving a best approximation problem for the gradient step until a certain stopping criterion holds. Finally, we show that there are available powerful algorithms, with linear convergence, for computing the inexact projection, such as Dykstra algorithm and alternating direction method of multipliers. Our algorithm is especially useful in embedded model predictive control on hardware with limited computational power, where tight bounds on the computational complexity of the numerical algorithm, used for solving the control problem, are required.

关键词： Approximation algorithms Gradient methods Linear programming Prediction algorithms Convergence Computational complexity

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Invasive weed optimization algorithm optimized fuzzy logic scaling parameters in controlling a lower limb exoskeleton

Invasive weed optimization algorithm optimized fuzzy logic s...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Ghasaq Al Rezage Hyreil A Kasdirin Siti Khadijah Ali M. O. Tokhi Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

This paper describes a new modified versions of invasive weed optimization algorithm with exponential seeds-spread factor. The modified invasive weed optimization algorithm (MIWO) is employed to optimize the fuzzy input-output scaling factors of lower limb exoskeleton. A fuzzy logic control (FLC) system with the (MIWO) are evolved for reference tracking control. The exoskeleton is developed to enhance and upgrade the lower limb capability and augment the torque of knee and hip of elderly people during the walking cycle. Invasive weed optimization is a bio-inspired search algorithm that mimics how weeds colonize a certain area in nature. The algorithm is modified by applying local knowledge during distribution of seeds that depends on their cost function value in each generation to narrow the accuracy and improve the local search ability. The obtained results from the modified invasive weed optimization algorithm are compared with heuristic gain values to improve the performance of the exoskeleton system. The Visual Nastran 4D software is used to develop a simulation model of the humanoid and an exoskeleton for testing and verification of the developed control mechanism. Simulation results demonstrating the performance of the adopted approach are presented and discussed.

关键词： Exoskeletons Mathematical model Optimization Solid modeling Fuzzy logic Legged locomotion Knee

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Dealing with massive data with a distributed expectation propagation particle filter for object tracking

Dealing with massive data with a distributed expectation pro...

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International Conference on Information Fusion

作者： Allan De Freitas Lyudmila Mihaylova Department of Automatic Control and Systems Engineering University of Sheffield United Kingdom

Target tracking in distributed networks faces the challenge in coping with large volumes of distributed data which requires efficient methods for real time applications with minimal communication overhead. The complexity considered in this paper is when each sensor in a distributed network observes a large number of measurements which are all required to be processed at each time step. The particle filter has been widely used for localisation and tracking in distributed networks with a small number of measurements [1]. This paper goes beyond the current state-of-the-art and presents a novel particle filter approach, combined with the expectation propagation framework, that is capable of dealing with the challenges presented by a large volume of measurements in a distributed network. In the proposed algorithm, the measurements are processed in parallel at each sensor node in the network and the communication overhead is minimised substantially. We show results with large improvements in communication overhead, with a negligible loss in tracking performance, compared with the standard centralised particle filter.

关键词： Atmospheric measurements Particle measurements Time measurement Current measurement Distributed databases Particle filters Estimation

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Lateral control of vehicle platoons with on-board sensing and Inter-Vehicle Communication

Lateral control of vehicle platoons with on-board sensing an...

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European control Conference

作者： Owen McAree Sandor M. Veres Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

ISBN: (纸本)9781509025923

This paper presents a lateral control strategy for a platoon of vehicles which utilises only data which can realistically be measured by each vehicle, augmented with Inter-Vehicle Communication (IVC). The control problem resembles those which exist for longitudinal control and this introduces the challenge of estimating a vehicles lateral position and velocity when direct measurement is not possible (due to lane markings being obscured by a preceding vehicle). It is shown that the associated robust controller, which we propose, exhibits string stability in the presence of sensor and actuation delays and a high fidelity simulation is conducted to verify this.

关键词： Lateral control vehicle platoons on-board sensing

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Efficient robust feed forward model predictive control with tracking

Efficient robust feed forward model predictive control with ...

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UKACC International Conference on control (control)

作者： S. S. Dughman J. A. Rossiter Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

This paper extends efficient, robust model predictive control (MPC) approaches for linear parameter varying (LPV) systems to tracking scenarios. A dual mode approach is used and future information about target changes is included in the optimisation tracking problem. The controller guarantees recursive feasibility by adding an artificial target as an extra degree of freedom. Convergence to admissible targets is ensured by constructing a robustly invariant set to track any admissible target. The efficacy of the proposed algorithm is demonstrated by MATLAB simulations.

关键词： Robustness Target tracking Optimization Steady-state Predictive control Prediction algorithms Uncertainty

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Output feedback quasi-distributed MPC for linear systems coupled via dynamics and constraints

Output feedback quasi-distributed MPC for linear systems cou...

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UKACC International Conference on control (control)

作者： P. R. Baldivieso Monasterios P. A. Trodden Department of Automatic Control & Systems Engineering University of Sheffield Sheffield UK

In this paper, we present a non-centralized approach to the output-feedback variant of tube-based model predictive control of dynamically coupled linear time-invariant systems with shared constraints. The proposed algorithm achieves guaranteed recursive feasibility and closed-loop asymptotic stability of the overall system. The approach combines decentralized and distributed MPC, hence the name quasi-distributed MPC. Local controllers, which are deployed among subsystems of the system, solve model predictive control problems in a decentralized fashion (i.e. without communication) in order to regulate their respective subsystems; the output-feedback variant of tube-basedPC is employed in order that controllers may reject the mutual disturbances induced by the coupled dynamics and the state estimation errors arising from having only noisy output measurements. On the other hand, shared constraints are handled in a distributed fashion: in order to satisfy shared constraints between systems, the controllers share predicted trajectories and take remedial action (by adjusting the optimized control inputs) only when a constraint violation is predicted.

关键词： Couplings Output feedback Observers Electron tubes Noise measurement Trajectory

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