A combined B-Spline-Neural-Network and ARX Model for Online Identi cation of Nonlinear Dynamic Actuation Systems

This paper presents a block oriented nonlinear dynamic model suitable for online identi cation.The model has the well known Hammerstein architecture where as a novelty the nonlinear static part is represented by a B-spline neural network (BSNN), and the linear static one is formalized by an auto regressive exogenous model (ARX). The model is suitable as a feed-forward control module in combination with a classical feedback controller to regulate velocity and position of pneumatic and hydraulic actuation systems which present non stationary nonlinear dynamics. The adaptation of both the linear and nonlinear parts is taking place simultaneously on a patterby- patter basis by applying a combination of error-driven learning rules and the recursive least squares method. This allows to decrease the amount of computation needed to identify the model's parameters and therefore makes the technique suitable for real time applications. The model was tested with a silver box benchmark and results show that the parameters are converging to a stable value after 1500 samples, equivalent to 7.5s of running time. The comparison with a pure ARX and BSNN model indicates a substantial improvement in terms of the RMS error, while the comparison with alternative non linear dynamic models like the NNOE and NNARX, having the same number of parameters but greater computational complexity, shows comparable performances

A combined B-Spline-Neural-Network and ARX Model for Online Identi cation of Nonlinear Dynamic Actuation Systems

This paper presents a block oriented nonlinear dynamic model suitable for online identi cation.The model has the well known Hammerstein architecture where as a novelty the nonlinear static part is represented by a B-spline neural network (BSNN), and the linear static one is formalized by an auto regressive exogenous model (ARX). The model is suitable as a feed-forward control module in combination with a classical feedback controller to regulate velocity and position of pneumatic and hydraulic actuation systems which present non stationary nonlinear dynamics. The adaptation of both the linear and nonlinear parts is taking place simultaneously on a patterby- patter basis by applying a combination of error-driven learning rules and the recursive least squares method. This allows to decrease the amount of computation needed to identify the model's parameters and therefore makes the technique suitable for real time applications. The model was tested with a silver box benchmark and results show that the parameters are converging to a stable value after 1500 samples, equivalent to 7.5s of running time. The comparison with a pure ARX and BSNN model indicates a substantial improvement in terms of the RMS error, while the comparison with alternative non linear dynamic models like the NNOE and NNARX, having the same number of parameters but greater computational complexity, shows comparable performances

Development and identification of hierarchical nonlinear mixed effects models for the analysis of dynamic systems: identification and application of hierarchical nonlinear mixed effects models for the determination of steady-state and dynamic torque responses of an SI engine

Multi-level or hierarchical models present various features for dealing with data grouped at several levels. The majority of applications of hierarchical models use clustered data that is static in nature and collected over a long period of time. The purpose of this study is investigating hierarchical models for application with highly dynamic systems. Steady-state data are conventionally employed for engine torque mapping purposes. The data takes much time to collect and the dynamics of the system are routinely ignored. This valuable information could be used for better control of the system.In this study, an innovative transient spark-sweep approach is developed for collecting dynamic torque data more efficiently. The means of data collection implies a structure for which a multi-level model is best suited. A multi-model augmented D-optimal design is created, and the experimental data collected. Spark excitation is applied at speed/load points using Amplitude Modulated Pseudo Random Signal (AMPRS), and the torque response over the operating space is thus obtained. Conditional first-order linearization is used within the identification process for determining the hierarchical model parameters. The level-1 Nonlinear Auto Regressive eXogenous (NARX) models are separately determined using an Iterative Generalized Least Square (IGLS) method and the results are employed for initialisation of the covariance matrix and the model level-2 parameters. A novel gradient optimiser was established to facilitate the dynamic hierarchical model identification. Additionally, the uncertainty associated with model selection was mitigated using a multi-model approach. The model identified is evaluated and compared with experimental dynamic and steady-state data. It shows behaviour, both dynamic and steady state, providing prediction over a wider extrapolated spark range than conventional approaches. The new approach is eight time faster than current state-of-the-art approaches.</div

Precision Control of a Sensorless Brushless Direct Current Motor System

Sensorless control strategies were first suggested well over a decade ago with the aim of reducing the size, weight and unit cost of electrically actuated servo systems. The resulting algorithms have been successfully applied to the induction and synchronous motor families in applications where control of armature speeds above approximately one hundred revolutions per minute is desired. However, sensorless position control remains problematic. This thesis provides an in depth investigation into sensorless motor control strategies for high precision motion control applications. Specifically, methods of achieving control of position and very low speed thresholds are investigated. The developed grey box identification techniques are shown to perform better than their traditional white or black box counterparts. Further, fuzzy model based sliding mode control is implemented and results demonstrate its improved robustness to certain classes of disturbance. Attempts to reject uncertainty within the developed models using the sliding mode are discussed. Novel controllers, which enhance the performance of the sliding mode are presented. Finally, algorithms that achieve control without a primary feedback sensor are successfully demonstrated. Sensorless position control is achieved with resolutions equivalent to those of existing stepper motor technology. The successful control of armature speeds below sixty revolutions per minute is achieved and problems typically associated with motor starting are circumvented.Research Instruments Ltd