Johannes Krettek

Optimal State Space Control of DC Motor

Abstract In comparison to classical cascade control architecture of DC motors, the state feedback control offers advantages in terms of design complexity, hardware realization and adaptivity. This paper presents a methodic approach to state space control of a DC motor. The state space model identified from experimental data provides the basis for a linear quadratic regulator (LQR) design. The state feedback linear control is augmented with a feedforward control for compensation of Coulomb friction. The controller is successfully applied and the closed loop behavior is evaluated on the experimental testbed under various reference signals.

Interactive evolutionary multiobjective optimization for hydraulic valve controller parameters

This paper proposes a novel approach of interactive evolutionary multiobjective optimization. The approach combines an evolutionary algorithm with an instance based supervised online learning scheme for user preferences. Interactive preference articulation constitutes an alternative to a priori as well as a posteriori decision making in multiobjective optimization. The proposed scheme is applied to the interactive user guided parameter optimization of hydraulic valve controllers. The tight integration of expert knowledge into the design and optimization process results in an efficient search of feasible solutions. The proposed interactive scheme extends the scope and applicability of computational intelligence aided design to mechatronic and engineering problems.

Optimal Large View Visual Servoing with Sets of SIFT Features

This paper presents a novel approach to large view visual servoing in the context of object manipulation. In many scenarios the features extracted in the reference pose are only perceivable across a limited region of the work space. The limited visibility of features necessitates the introduction of additional intermediate reference views of the object and requires path planning in view space. In our scheme the visual control is based on decoupled moments of SIFT-features, which are generic in the sense that the control operates with a dynamic set of feature correspondences rather than a static set of individual features. The additional freedom of dynamic feature sets enables flexible path planning in the image space and online selection of optimal reference views during servoing to the goal view. The time to convergence to the goal view is estimated by a neural network based on the residual feature error and the quality of the SIFT feature distribution. The transition among reference views occurs on the basis of this estimated cost which is evaluated online based on the current set of visible features. The dynamic switching scheme achieves robust and nearly time-optimal convergence of the visual control across the entire task space. The effectiveness and robustness of the scheme is confirmed in an evaluation in a virtual reality simulation and on a real robot arm with a eye-in-hand configuration.

Robust Identification of Nonlinear Frictional Dynamics for Advanced Controller Design

Abstract The robust identification of nonlinear frictional dynamics constitutes a significant challenge for the model based friction compensation in advanced control of electro-mechanical drives. This paper discusses the frictional phenomena in pre-sliding and gross sliding regime described by the advanced General-Maxwell-Slip friction model and proposes a robust identification approach to determine their distributed parameters. The nonlinear dynamics of a drive chain with multiple frictional surfaces is described. The appropriate control signals are designed to excite the friction dynamics in both pre-sliding and sliding regimes. The estimation of friction parameters relies on the recursive least square (RLS) technique with a suitable set of regressors. The identified model is compared with experimental data and applied in a tunable framework for the model based friction compensation.

Detection and self-tuning compensation of periodic disturbances by the control of DC motor

The accuracy with which a DC motor and drive system track a reference velocity profile is limited by periodic torque disturbances. The cogging and ripples torques, and constructive imperfections in the mechanical assembly of the drive cause disturbance pulsation harmonics with a base frequency dictated by the rotational velocity. This paper describes a novel compensation technique based on disturbance observation and a self-tuning feed-forward compensation algorithm. The DC motor is modeled as a linear system augmented by the nonlinear Coulomb friction and is experimentally identified from a set of the system responses. The disturbance harmonics are detected by means of the fast Fourier transformation (FFT) and analytically described by a spatial Fourier transform with respect to the angular position of the rotor. The online algorithm tunes the parameters of the feed-forward compensator using the recursive estimation technique. The proposed self-tuning compensator is experimentally verified as part of an open control loop at different level of the rotational velocity and system load.

Preference modeling and model management for interactive multi-objective evolutionary optimization

Multiobjective optimization and decision making are strongly inter-related. This paper presents an interactive approach for the integration of expert preferences into multi-objective evolutionary optimization. The experts underlying preference is modeled only based on comparative queries that are designed to distinguish among the non-dominant solutions with minimal burden on the decision maker. The preference based approach constitutes a compromise between global approximation of a Pareto front and aggregation of objectives into a scalar utility function. The model captures relevant aspects of multi-objective decision making, such as preference handling, ambiguity and incommensurability. The efficiency of the approach in terms of number of expert decisions and convergence to the optimal solution are analyzed on the basis of an artificial decision behavior with respect to optimization benchmarks.

Evolutionary hardware-in-the-loop optimization of a controller for cascaded hydraulic valves

This paper presents a method for evolutionary optimization of a cascaded control structure for an industrial hydraulic valve. It proposes an iterative optimization scheme, in which the controller optimization alters between inner and outer loop parameters. The evolutionary optimization is a combination of hardware-in-the-loop evaluation of the pilot valve and a dynamic simulation of the main valve behavior. The experimental results demonstrate that the intertwined scheme results in faster convergence as it breaks down the overall optimization of the control structure into the less complex subproblems of separated optimization of inner and outer loop.

Interactive Incorporation of User Preferences in Multiobjective Evolutionary Algorithms

This paper proposes a novel interactive scheme to incorporate user preferences into evolutionary multiobjective optimization. The approach combines an evolutionary algorithm with an instance based supervised online learning scheme for user preference modeling. The user is queried to compare pairs of prototype solutions in terms of comparability and quality. The user decisions form a preference model which induces a ranking on the population. The model acts as a critic in the selection among non-dominated solutions on behalf of the expert. The user preference is extrapolated from a minimal number of pairwise comparisons to minimize the burden of interactive expert decisions. The preference model includes the concept of comparability to allow simultaneous convergence to multiple disconnected regions of the Pareto front. The preference model comprehends the specific preference scenarios of scalar optimization, goal oriented scenarios, ranking of criteria and global approximation of the Pareto front. It thus represents a general scheme for interactive optimization that does not depend on prior assumptions on either the problem or user preference structure.

Gaussian Mixture Models vs. Fuzzy Rule-Based Systems for Adaptive Meta-scheduling in Grid/Cloud Computing

Adaptive scheduling strategies are about considering the state of computational grids to obtain efficient and reliable schedules and to prevent the system performance deterioration. In this work, emerging adaptive strategies in grid computing, namely Fuzzy Rule-Based Systems (FRBS) -based strategies and a new adaptive scheduling approach, gaussian scheduling founded on Gaussian Mixture Models (GMMs) are compared. Both types of strategies focus on modeling the state of resources and select the most convenient site of the grid at every scheduling step given the current conditions. FRBSs provide a fuzzy characterization of the grid state and the inference of a suitability index based on their own knowledge given in the form of fuzzy IF-THEN rules. Besides, a GMM can be trained to model a complex probability density distribution indicating the suitability of every site in the grid to be the target of the schedule with the current conditions of its resources. This way the GMM scheduler assigns a probability to every state of the site where a higher probability is associated to a higher suitability of selection. Simulations based on real grid facilities are conducted to test the FRBS and GMM-based models and results are analyzed in terms of accuracy and convergence behaviour of their associated learning processes.

Structure and parameter identification of nonlinear systems with an evolution strategy

Modeling and identification of dynamic systems often is a prerequisite for the engineering of technical solutions, for example control system design. This paper presents an multi-objective evolutionary approach for identification of dynamic systems of variable structure. The evolutionary algorithm employs domain specific operators in order to evolve the block oriented structure of the model and simultaneously optimize its parameters. Based on the observed inputs and outputs the multi-objective method identifies an entire set of optimal compromise models which contrast model accuracy against complexity. The models are constructed from a set of basic blocks that capture phenomenons such as linear transfer functions, nonlinear gains and hysteresis that typically occur in mechanical, hydraulic and electrical systems. This representation enables the incorporation of domain knowledge in terms of building blocks and the interpretation of the identified model for further analysis and design. The feasibility of the proposed method is validated in the identification of an artificial dynamic system as well as a hydraulic proportional valve.