Adam Lagerberg

Backlash Estimation With Application to Automotive Powertrains

In automotive powertrains, backlash imposes well-known limitations on the quality of control and, hence, on vehicle driveability. High-performance controllers for backlash compensation require high-quality measurements of the current state of the powertrain. Information about the size of the backlash is also needed. In this paper, nonlinear estimators for backlash size and state are developed, using the Kalman filtering theory. A linear estimator for fast and accurate estimation of the angular position of a wheel and the engine is also described. It utilizes standard engine speed sensors and the antilock brake system speed sensors and event-based sampling at each pulse from these sensors. The estimators are validated through experiments on a real vehicle and the results show that the estimates are of high quality, and hence, useful for improving backlash compensation functions in the powertrain control system

Estimation of backlash with application to automotive powertrains

In rotating systems, backlash imposes limitations on the quality of control. Automotive powertrains is an example where this is a well-known limitation. In order to increase the controller performance, knowledge of the backlash properties (size and current position) is needed. In this paper, nonlinear estimators for backlash size and position are described, both based on Kalman filtering theory. The size and position estimators are combined, and the result is an estimate of the state of the backlash. This information can be used in feedback control of the rotating system. Simulation and experimental results show that the estimates are of high quality, and robust to modelling errors.

MODEL PREDICTIVE CONTROL OF AUTOMOTIVE POWERTRAINS WITH BACKLASH

Abstract In automotive powertrains, the existence of backlash causes driveability problems, which to some extent are remedied by the engine control system. The control problem has a constrained, minimum-time character, which motivates an investigation of the usability of model predictive control, MPC, in this application. Recent developments in MPC theory make an off-line calculation of the control law possible. This makes MPC more attractive for implementation in fast control loops such as the one under study here. The results indicate that MPC has a potential in this application. However, the off-line computation time is significant, and further robustness investigations are needed.

Estimation of Backlash in Automotive Powertrains — An Experimental Validation

Abstract In automotive powertrains, backlash imposes well-known limitations on the quality of control and hence the driveability. In order to increase the controller performance, knowledge of the backlash properties (size and current position) is needed. In previous papers, nonlinear estimators for backlash size and position are described, based on Kalman filtering theory. The size and position estimators are combined, and the result is an estimate of the state of the backlash. In this paper, experimental results from vehicle tests show that the estimates are of high quality, and hence useful for improving backlash compensation functions in the powertrain control system.

CONTROL SYSTEM DESIGN IN DISTILLATION PROCESSES AND ITS DEPENDENCE OF THE ORIGINAL PROCESS DESIGN

Abstract Process design of today is characterized by the use of stationary simulations only. For example, in the design of a packed distillation column, the column height and diameter as well as the packing material needed to meet the specifications, are to be determined. The process information obtained from steady state simulations is conventionally used to solve this design problem. It is claimed that the additional information obtained from dynamic process simulations must also be considered already in the process design situation. This is illustrated by way of an example (the atmospheric Benzene/Toluene system). Here, it is shown that the relative gain array (RGA) of the plant can be used as a measure when selecting the column dimension at different operating conditions as it directly reflect how illconditioned the plant will be. The results are compared with those obtained front PROCESS, a commercial package for stationarv simulations. Further, it is indicated that the condition number of the plant does not contain t he same information as the 1-norm of RGA. Therefore, the condition number turns out to be a questionable tool in the calculation of column height and diameter.