Benjamin L. Pence

Brief paper: Recursive maximum likelihood parameter estimation for state space systems using polynomial chaos theory

This paper combines polynomial chaos theory with maximum likelihood estimation for a novel approach to recursive parameter estimation in state-space systems. A simulation study compares the proposed approach with the extended Kalman filter to estimate the value of an unknown damping coefficient of a nonlinear Van der Pol oscillator. The results of the simulation study suggest that the proposed polynomial chaos estimator gives comparable results to the filtering method but may be less sensitive to user-defined tuning parameters. Because this recursive estimator is applicable to linear and nonlinear dynamic systems, the authors portend that this novel formulation will be useful for a broad range of estimation problems.

Sprung mass estimation for off-road vehicles via base-excitation suspension dynamics and recursive least squares

This paper presents a novel method for identifying in real time the mass of an off-road vehicle using measurements of sprung and unsprung mass acceleration. The online estimate can be used for vehicle control strategies such as active safety control, traction control, and powertrain control. The online estimate is needed for vehicles whose mass varies significantly from one loading condition to another. Existing off-road mass estimation strategies that use suspension measurements typically require either known suspension force actuation or a priori knowledge of terrain characteristics. Our unique method for estimating online the mass of an off-road vehicle addresses existing limitations by applying base-excitation concepts to make the measured unsprung mass acceleration become a known input to the recursive least-squares estimator. We present computer simulations to demonstrate the method, and conclude that the method provides a practical solution for real-time, off-road vehicle mass estimation.

Vehicle sprung mass estimation for rough terrain

This paper provides methods and experimental results for recursively estimating the sprung mass of a vehicle driving on rough terrain. A base-excitation model of vertical ride dynamics treats the unsprung vertical accelerations, instead of the terrain profile, as the input to ride dynamics. Recently developed methods based on polynomial chaos and maximum likelihood theory estimate the most likely value of the vehicle sprung mass. The polynomial chaos estimator is compared to least squares and Kalman filtering approaches. An experimental study suggests that the proposed approach provides accurate outputs and is less sensitive to tuning parameters than the benchmark algorithms.

A Base-Excitation Approach to Polynomial Chaos-Based Estimation of Sprung Mass for Off-Road Vehicles

This paper presents a novel method for identifying in real-time the sprung mass of a 2-DOF quarter-car suspension model. It does so by uniquely combining the base-excitation concept with polynomial chaos estimation. This unique combination of the two methods provides two important benefits. First, the base-excitation concept makes it possible to estimate the sprung mass without explicitly measuring or knowing the terrain profile prior to estimation. Second, the polynomial chaos estimation strategy makes it possible to perform such mass estimation using sprung and unsprung acceleration measurements without pseudo-integration filters that can be difficult to tune. This paper derives the proposed method in detail and presents computer simulations to evaluate its convergence speed and accuracy. The simulation results consistently converge to within 10% of the true mass value typically within 120 seconds.Copyright

Recursive Estimation for Reduced-Order State-Space Models Using Polynomial Chaos Theory Applied to Vehicle Mass Estimation

The main contribution of this paper is to present a recursive estimation/detection technique for reduced-order state-space systems. The recursive state and parameter estimator is built on the framework of polynomial chaos theory and maximum likelihood estimation. The estimator quantifies the reliability of its estimate in real-time by recursively calculating a signal-to-noise ratio. The signal-to-noise ratio (SNR) indicates how well the output of the reduced-order estimation model matches the actual system output. A detection algorithm makes decisions to trust or distrust the current estimate by comparing the current value of the SNR ratio against a threshold value. This paper applies the proposed techniques to estimate the sprung mass of an actual vehicle. It uses a reduced-order model to approximate the complex ride dynamics of the vehicle. Despite the modeling approximations and simplifications, the proposed technique is able to reliably estimate the sprung mass of the vehicle to within 10% of the true value.

A Framework for Control Oriented Modeling of PEM Fuel Cells

This paper develops a framework for along-the-channel and through-the-membrane control oriented modeling of polymer electrolyte membrane (PEM) fuel cells. The initial modeling framework is spatially one-dimensional by one-dimensional (1+1D) and is described by unsteady partial differential equations (PDEs). Numerical techniques convert the PDEs and boundary conditions to ordinary differential and algebraic equations that are convenient for state-space modeling. The modeling framework includes two-phase, thermal, and other transient effects. The generality of the modeling framework and its ability to be represented in state-space form facilitate complexity reduction and control-oriented application.Copyright

Markov-parameter-based adaptive control of 3-axis angular velocity in a six-degree-of-freedom stewart platform

Stewart platforms are complex mechanical devices used throughout industry for vibration testing and precision pointing applications. These platforms are nonlinear, strongly coupled MIMO systems. For a six-degree-of-freedom Stewart platform, we consider the problem of three-degree-of-freedom angular-velocity command following. Static nonlinearity inherent in the platform is analyzed, and a closed-loop setup for adaptive command-following control is described. A review of the Markov-parameter-based adaptive control algorithm is given, along with the OKID system identification algorithm, test procedures, and experimental results.

Fuel cell thermal management: Modeling, specifications and correct-by-construction control synthesis

Thermal management is crucial for safe and efficient operation of fuel cells. The goal of this paper is to algorithmically synthesize a provably-correct controller for a fuel cell thermal management system. For this purpose, we start with developing a control-oriented model for the fuel cell thermal management system and list the associated requirements. Then, we identify some structural properties of the system dynamics that can be leveraged for making the abstraction-based synthesis algorithm computationally efficient. Finally, we synthesize a controller for this system and demonstrate the closed-loop system behavior via simulations.