Constrained Design of Multisine Signals for Frequency-domain Identification of Electric Drive Trains

The paper at hand deals with the optimization of multisine signals in terms of effective value for identification of electric drive trains, considering constraints on position, velocity, acceleration and torque. The advantage of maximizing the effective value while respecting the constraints rather than minimizing the crest factor of the input signal is delineated. Results with two algorithms suitable for this optimization task are presented for different sets of dynamic constraints. It is shown that the proposed modified clipping algorithm is much faster than the L1 optimization which is dedicated to simultaneous optimization of several different signals, while being slightly less performant in terms of effective value

Model Selection for Servo Control Systems

Physically motivated models of electromechanical motion systems are required in several applications related to control design. However, the effort of modelling is high and automatic modelling would be appealing. The intuitive approach to select the model with the best fit has the shortcoming that the chosen model may be one with high complexity in which some of the parameters are not identiifable or uncertain. Also, ambiguities in selecting the model structure would lead to false conclusions. This paper proposes a strategy for frequency domain model selection ensuring practical identifiability. Also, the paper describes distinguishability analysis of candidate models utilising transfer function coecients and Markov parameters. Model selection and distinguishability analysis are applied to a class of models as they are commonly used to describe servo control systems. It is shown in experiments on an industrial stacker crane that model selection works with little user interaction, except from defining normalised hyperparameters

Distinguishability Analysis for Multiple Mass Models of Servo Systems with Commissioning Sensors

Physically motivated models of electromechanical motion systems enable model-based control theory and facilitate system interpretation. Unfortunately, the effort of modelling restricts the usage of model-based methods in many applications. Some approaches to automatically generate models from measurements choose the best model based on minimizing the residual. These model selection attempts are limited due to ambiguities in reconstructing the internal structure from the input-output behaviour because usually motion systems have only one actuator and one sensor. Often, it is unknown if the resulting model is unique or if other models with different structure would fit equally well. The set of candidate models should be designed to contain only distinguishable models but ambiguities are often unknown to the experimenter. In this paper distinguishability is investigated systematically for a class of multiple mass models representing servo positioning systems. In the analysis a new criterion for indistinguishability is used. The benefit of additional, structural sensors on distinguishability of models is demonstrated which suggests to mount them temporarily for the commissioning phase in order to facilitate the model selection. It turns out that the best results can be achieved if synergies among sensor signals are utilized. © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Structure and Parameter Identification of Process Models with hard Non-linearities for Industrial Drive Trains by means of Degenerate Genetic Programming

The derivation of bright-grey box models for electric drives with coupled mechanics, such as stacker cranes, robots and linear gantries is an important step in control design but often too time-consuming for the ordinary commissioning process. It requires structure and parameter identification in repeated trial and error loops. In this paper an automated genetic programming solution is proposed that can cope with various features, including highly non-linear mechanics (friction, backlash). The generated state space representation can readily be used for stability analysis, state control, Kalman filtering, etc. This, however, requires several special rules in the genetic programming procedure and an automated integration of features into the defining state space form. Simulations are carried out with industrial data to investigate the performance and robustness

Sensitivity-Based Adaptive SRUKF for State, Parameter, and Covariance Estimation on Mechatronic Systems

Since the initial developments in the state-space theory in the 1950s and 1960s, the state estimation has become an extensively researched and applied discipline. All systems that can be modelled mathematically are candidates for state estimators. The state estimators reconstruct the states that represent internal conditions and status of a system at a specific instant of time using a mathematical model and the information received from the system sensors. Moreover, the estimator can be extended for system parameter estimation. The resulting Kalman filter (KF) derivatives for state and parameter estimation also require knowledge about the noise statistics of measurements and the uncertainties of the system model. These are often unknown, and an inaccurate parameterization may lead to decreased filter performance or even divergence. Additionally, insufficient system excitation can cause parameter estimation drifts. In this chapter, a sensitivity-based adaptive square-root unscented KF (SRUKF) is presented. This filter combines a SRUKF and the recursive prediction-error method to estimate system states, parameters and covariances online. Moreover, local sensitivity analysis is performed to prevent parameter estimation drifts, while the system is not sufficiently excited. The filter is evaluated on two testbeds based on an axis serial mechanism and compared with the joint state and parameter UKF

Frequency Domain Model Selection for Servo Systems ensuring Practical Identifiability

Physically motivated models of servo control systems with coupled mechanics are required for control design, simulation etc. Often, however, the effort of modelling prohibits, these model-based methods in industrial applications. Therefore, all approaches of automatic modelling / model selection are naturally appealing. In this paper a procedure for model selection in frequency domain is proposed that minimizes the Kullback-Leibler distance between model and measurement while considering only those models that are practically identifiable. It aims at mechanical models of servo systems including multiplemass resonators. Criteria for practical identifiability are derived locally from the sensitivity matrix which is calculated for different formulations of the equation error. In experiments with two industry-like testbeds the methodology proves to reveal the characteristic mechanical properties of the two setups. © 2020 IEE

Multisensor-Configuration for Improved Identifiability and Observability of Electromechanical Motion Systems

For many problems in the field of control design phenomenological models are required so that the need for parameter identification of given model structures arises. These models can be combined with observers to derive the system states in operation in addition to the parameters. However, identification and observation are limited in accuracy due to the restriction to existing series sensors. In the case of electric drives it is possible that due to elasticities in the structure part of the system is vibrating while the position sensor measures a nearly constant position. In this paper, the use of additional acceleration sensors is investigated in terms of identifiability and observability, which are installed at different points of the structure. The analysis is traced back to measures on the sensitivity matrix, where the integrating behaviour of the plant and the combination of different sensor types (position, velocity, acceleration) require special consideration. An industry-like stacker crane is used as a testbed for validation. It is shown that both identifiability and observability can be improved by the additional sensors in many cases. There is a good agreement between the expected and measured frequency response when the acceleration sensor is mounted on the first or second mass. Deviations only occur when mounted on the load suspension device, which is assumed to be the third mass. © 2020 IEE

Unscented Kalman Filter for State and Parameter Estimation in Vehicle Dynamics

Automotive research and development passed through a vast evolution during past decades. Many passive and active driver assistance systems were developed, increasing the passengers’ safety and comfort. This ongoing process is a main focus in current research and offers great potential for further systems, especially focusing on the task of autonomous and cooperative driving in the future. For that reason, information about the current stability in terms of dynamic behavior and vehicle environment are necessary for the systems to perform properly. Thus, model-based online state and parameter estimation have become important throughout the last years using a detailed vehicle model and standard sensors, gathering this information. In this chapter, state and parameter estimation in vehicle dynamics utilizing the unscented Kalman filter is presented. The estimation runs in real time based on a detailed vehicle model and standard measurements taken within the car. The results are validated using a Volkswagen Golf GTE Plug-In Hybrid for various dynamic test maneuvers and a Genesys Automotive Dynamic Motion Analyzer (ADMA) measurement unit for high-precision measurements of the vehicle’s states. Online parameter estimation is shown for friction coefficient estimation performing maneuvers on different road surfaces

Model Selection ensuring Practical Identifiability for Models of Electric Drives with Coupled Mechanics

Physically motivated models of electric drive trains with coupled mechanics are ubiquitous in industry for control design, simulation, feed-forward, model-based fault diagnosis etc. Often, however, the effort of model building prohibits these model-based methods. In this paper an automated model selection strategy is proposed for dynamic simulation models that not only optimizes the accuracy of the fit but also ensures practical identifiability of model parameters during structural optimization. Practical identifiability is crucial for physically motivated, interpretable models as opposed to pure prediction and inference applications. Our approach extends structural optimization considering practical identifiability to nonlinear models. In spite of the nonlinearity, local and linear criteria are evaluated, the integrity of which is investigated exemplarily. The methods are validated experimentally on a stacker crane

Performance Optimal and Robust Design of an Idle-Speed Controller Considering Physical Uncertainties

Modern passenger vehicles are equipped with a great number of control functions targeting versatile performance aspects like safe drive-ability, comfortable or sporty ride concerning assistance systems or a proper adjustment of engine control functions in order to prevent noise vibration and harshness issues. In this paper a methodology for a performance optimal and robust controller design is presented. This methodology is applied on a given idle-speed controller implementation using a detailed nonlinear drive train model in closed loop considering physical parameter uncertainties. The results are discussed with exemplary selected performance measures