Recursive least squares filter

About: Recursive least squares filter is a research topic. Over the lifetime, 8907 publications have been published within this topic receiving 191933 citations.

Real-time coupling coefficient estimation and maximum efficiency control on dynamic wireless power transfer for electric vehicles

Abstract: Maximum efficiency control using a DC/DC converter on the secondary side can improve transmitting efficiency in dynamic wireless power transfer system for electric vehicles. However, the information of coupling coefficient has to be estimated to implement the control although coupling coefficient changes dynamically. In this paper, a simple method of coupling coefficient estimation with RLS (Recursive least squares) filter is proposed. Dynamics of WPT system is analyzed with transfer functions. Moreover, the modeling and the control method of DC/DC converter are introduced. The experimental results of real-time coupling coefficient estimation and maximum efficiency control are provided and they indicate the effectiveness of the proposed control in a real dynamic wireless power transfer system for EVs.

Calibration of Resolver Sensors in Electromechanical Braking Systems: A Modified Recursive Weighted Least-Squares Approach

Abstract: Resolver sensors are utilized as absolute position transducers to control the position and speed of actuators in many industrial applications. The accuracy and convergence of the position and speed measurements provided by resolvers in electromechanical braking system (EMB) designs directly contribute to the braking performance and vehicle safety. In practice, the dc drifts, amplitudes, and phase shift of the resolver signals vary with aging and temperature, and adaptive techniques are required for the calibration of these parameters of resolvers. Existing classical adaptive techniques such as recursive least squares are unable to track the parameters during resting (low-speed actuation or stationary) periods and also a transient period after them. This paper proposes a new approach for real-time tracking of resolver parameters specially developed for actuator-control applications with varying speed and long resting periods. We formulate the algebraic relationship between the resolver parameters and the parameters of resolver characteristic ellipse, which is the ellipse formed by plotting the resolver signals versus each other. Having known the characteristic ellipse parameters, the resolver parameters are calculated using the formulated algebraic relation. Then, a new recursive and adaptive estimator is proposed to track the parameters of characteristic ellipse. The low computational complexity of the proposed method makes it desirable for real-time applications like the EMBs, where limited computational power and memory are available. Experimental results show that the proposed technique is able to track the resolver parameters and the accurate actuator position with a small error in real-time, while other adaptive estimators are unable to track the resolver parameters during and after resting periods

Dynamic Updating for $\ell_{1}$ Minimization

Abstract: The theory of compressive sensing (CS) has shown us that under certain conditions, a sparse signal can be recovered from a small number of linear incoherent measurements. An effective class of reconstruction algorithms involve solving a convex optimization program that balances the l1 norm of the solution against a data fidelity term. Tremendous progress has been made in recent years on algorithms for solving these l1 minimization programs. These algorithms, however, are for the most part static: they focus on finding the solution for a fixed set of measurements. In this paper, we present a suite of dynamic algorithms for solving l1 minimization programs for streaming sets of measurements. We consider cases where the underlying signal changes slightly between measurements, and where new measurements of a fixed signal are sequentially added to the system. We develop algorithms to quickly update the solution of several different types of l1 optimization problems whenever these changes occur, thus avoiding having to solve a new optimization problem from scratch. Our proposed schemes are based on homotopy continuation, which breaks down the solution update in a systematic and efficient way into a small number of linear steps. Each step consists of a low-rank update and a small number of matrix-vector multiplications - very much like recursive least squares. Our investigation also includes dynamic updating schemes for l1 decoding problems, where an arbitrary signal is to be recovered from redundant coded measurements which have been corrupted by sparse errors.