Identification of linear parameter varying models

Abstract:System identification is the art and science of building mathematical models of dynamic systems from observed input-output data. It can be seen as the interface between the real world of applications and the mathematical world of control theory and model abstractions. As such, it is an ubiquitous necessity for successful applications. System identification is a very large topic, with different techniques that depend on the character of the models to be estimated: linear, nonlinear, hybrid, nonparametric etc. At the same time, the area can be characterized by a small number of leading principles, e.g. to look for sustainable descriptions by proper decisions in the triangle of model complexity, information contents in the data, and effective validation. The area has many facets and there are many approaches and methods. A tutorial or a survey in a few pages is not quite possible. Instead, this presentation aims at giving an overview of the "science" side, i.e. basic principles and results and at pointing to open problem areas in the practical, "art", side of how to approach and solve a real problem.

show abstract

“…Identification of such models has been the subject of recent interest. See, e.g., Lee and Poolla (1999) and Bamieh and Giarré (2002).…”

Section: Lpv Modelsmentioning

confidence: 99%

Perspectives on system identification

Ljung¹

2010

Annual Reviews in Control

567

302

View full text Add to dashboard Cite

show abstract

“…Each state space parameter of the LPV system is not a constant; instead, it is a function of the exogenous parameter which is predefined, measured or estimated upon operation of the system [29]. To identify the model, a Gauss-Newton type of gradient search method [30], hybrid linear/nonlinear procedure (where a model is divided into two parts, the nonlinear part is identified through neural network and the linear parametric part through an LS algorithm), recursive least-squares (RLS) algorithm [31] etc. are proposed.…”

Section: Modeling a Dynamical Systemmentioning

confidence: 99%

Estimating the non-linear dynamics of free-flying objects

Kim

Billard

2012

Robotics and Autonomous Systems

View full text Add to dashboard Cite

a b s t r a c tThis paper develops a model-free method to estimate the dynamics of free-flying objects. We take a realistic perspective to the problem and investigate tracking accurately and very rapidly the trajectory and orientation of an object so as to catch it in flight. We consider the dynamics of complex objects where the grasping point is not located at the center of mass.To achieve this, a density estimate of the translational and rotational velocity is built based on the trajectories of various examples. We contrast the performance of six non-linear regression methods (Support Vector Regression (SVR) with Radial Basis Function (RBF) kernel, SVR with polynomial kernel, Gaussian Mixture Regression (GMR), Echo State Network (ESN), Genetic Programming (GP) and Locally Weighted Projection Regression (LWPR)) in terms of precision of recall, computational cost and sensitivity to choice of hyper-parameters. We validate the approach for real-time motion tracking of 5 daily life objects with complex dynamics (a ball, a fully-filled bottle, a half-filled bottle, a hammer and a pingpong racket). To enable real-time tracking, the estimated model of the object's dynamics is coupled with an Extended Kalman Filter for robustness against noisy sensing.

show abstract

“…However, a city car is for transportation inside traffic jams, so its transient current plays the key to energy economy and driving safety. To this, we develop linear parameter-varying (LPV) modeling and identification to remedy such a situation, following the previous works in [24][25][26][27][28][29][30]. Here, the tire speed is taken as the slow-time state variable and simultaneously the scheduling parameter.…”

Section: Introductionmentioning

confidence: 99%

Online Energy Management of City Cars with Multi-Objective Linear Parameter-Varying L2-Gain Control

Hong

2015

Energies

View full text Add to dashboard Cite

Abstract:This work aims at online regulating transient current out of the batteries of small-sized electric cars that transport people and goods around cities. In a city with heavy traffic, transient current dominates the energy economy and propulsion capability, which are in opposition to each other. In order to manage the trade-off between energy consumption per distance and propulsion capability in transience, the authors improve on previous work on multi-objective linear parameter-varying (LPV) L2-gain control. The observer embedded into this multi-objective controller no longer assumes Kalman-filtering structure, and structural conservatism is thus removed. A full-spectrum set of experiments is performed. The results reveal that the feedback design significantly improves energy-motion management.

show abstract

Identification of linear parameter varying models

Cited by 367 publications

References 13 publications

Perspectives on system identification

Perspectives on system identification

Estimating the non-linear dynamics of free-flying objects

Online Energy Management of City Cars with Multi-Objective Linear Parameter-Varying L2-Gain Control

Contact Info

Product

Resources

About