Martin Kozek

Comfort Enhancement By an Active Vibration Reduction System for a Flexible Railway Car Body

In order to improve the ride comfort of lightweight railway vehicles, an active vibration reduction system using piezo-stack actuators is proposed and studied in simulations. The system consists of actuators and sensors mounted on the vehicle car body. Via a feedback control loop, the output signals of the sensors which are measuring the flexible deformation of the car body generate a bending moment, which is directly applied to the car body by the actuators. This bending moment reduces the structural vibration of the vehicle car body. Simulations have shown that a significant reduction in the vibration level is achieved.

Vibration Damping of a Flexible Car Body Structure Using Piezo-Stack Actuators

In this work piezo-stack actuators mounted in consoles are utilized to actively dampen vibrations of a flexible car body structure by introducing bending moments. Using an example of a heavy metro vehicle the complete design for the active vibration damping system is presented. Both analytical modeling and a system identification of the vehicle are described, issues of modal representation and model reduction are covered, and a robust controller design is motivated and explained. The excellent performance of the proposed method is documented by both experimental results from a scaled model and an extensive co-simulation of the overall system.

Overview and Motivation

The first chapter is introduced by a short motivation for the ACFA 2020 project given by the “ACARE vision 2020”. The ACFA 2020 project is presented in a concise way, listing the main goals and the associated deliverables together with some key numbers of the project. In order to set the background of the research work done, a section on the state of the art in aircraft configurations is contained. A special emphasis is given to European developments in advanced blended wing body (BWB) aircraft configurations, followed by a section on recent developments in aircraft conceptual design modeling and simulation methods. A section on control concepts for automatic flight control systems concludes the chapter, where both load alleviation and handling qualities are covered.

Identification of Wiener models using optimal local linear models

Abstract Identification of a Wiener model using optimal local linear models (LLMs) is presented. The model consists of a discrete-time transfer function and piece-wise linear functions. Parameter estimation as well as partitioning of the LLMs is simultaneously accomplished by the algorithm. The optimality is threefold: first, each local model is linear in the parameters, thus leading to an optimal solution. Second, the model size of each LLM is adaptively optimized using a chi-squared criterion, explicitly incorporating the measurement noise level. Third, the resulting model has a minimum of parameters for a given performance. Simulation results document that the output noise is balanced with the systems nonlinearity.

Hierachical Fuzzy MPC Concept for Building Heating Control

Abstract This paper presents a hierachical model predictive control (MPC) structure with decoupled MPCs for building heating control using weather forcasts and occupancy information. The two level control structure embeds a fuzzy MPC (FMPC) for user comfort optimization and a mixed-integer MPC (MI-MPC) for energy optimization at minimal costs. As FMPC uses a set of local linear models classical linear MPC theory is applicable, though the underlying system dynamics is non-linear. The supply level in a large modern office building always features switching states of aggregates, hence an MI-MPC is used for energy supply optimization. Additionally, both FMPC and MI-MPC consider all relevant constraints. The innovation in this study, beside the usage of FMPC for building control, is the decoupling of the energy supply level and the user comfort with a single coupling node. Although a global optimum is not guaranteed, a decoupled control system often is more attractive for industrial applications and building operators. The perfomance of the proposed control structure is demonstrated in a simulation with a validated building model, and two different disturbance scenarios are presented.

Identification of Hammerstein/Wiener nonlinear systems with extended Kalman filters

The extended Kalman filter (EKF) is considered for parameter identification of Hammerstein/Wiener nonlinear systems. The EKFs for parameter identification of both combined Hammerstein/Wiener as well as for pure Hammerstein and pure Wiener models are formulated. In order to enable efficient estimation of unknown nonlinearities linear parametrizations with linear static mappings and basis function expansions are proposed and the EKFs for these cases are established. The efficiency and performance of the approach is demonstrated by means of a computer simulation of a Hammerstein and a Wiener model.

Cooperative Fuzzy Model-Predictive Control

In this paper, a cooperative fuzzy model-predictive control (CFMPC) is presented. The overall nonlinear plant is assumed to consist of several parallel input-coupled Takagi-Sugeno (T-S) fuzzy models. Each such T-S fuzzy subsystem is represented in the form of a local linear model network (LLMN). The control of each local linear model in each LLMN is realized by model-predictive control (MPC). For each LLMN, the outputs of the associated MPCs are blended by the fuzzy membership functions, which leads to a fuzzy model-predictive controller (FMPC). The resulting structure is one FMPC for each LLMN subsystem. Overall, a parallel combination of FMPCs results, which mutually affects all LLMN subsystems by their respective manipulated variables. To compensate detrimental cross-couplings in this setup, a cooperation between the FMPCs is introduced. For this cooperation, convergence is proven, and for the closed-loop system, a stability proof is given. It is demonstrated in a simulation example that the proposed input-constraint CFMPC algorithm achieves convergence of the fuzzy LLMNs within few cooperative iteration steps. Simulations are given to demonstrate the effectiveness of the theoretical results.

Linear parameter-varying control of a large blended wing body flexible aircraft

Abstract In this paper a linear parameter-varying (LPV) controller design approach is applied to the longitudinal dynamics of a large blended wing body aircraft. The method is based on parameter-dependent Lyapunov functions utilizing the information given by bounds for the maximum parameter rate of variation. The entire parameter space is approximated by a set of linearized models in trimmed operating points which leads to a finite-dimensional convex optimization problem. Typical design goals for flexible aircraft control such as handling qualities, loads and vibration reduction were considered with the Mach number as scheduling parameter. The obtained LPV controller is extensively tested, and the obtained results demonstrate the high potential of the methodology for flexible aircraft control.

Efficient Physical Modelling of District Heating Networks

A mathematical physical model for dynamic simulation of flow and temperature in district heating networks (DHN) is proposed. The network structure is described by means of a graph-theoretical approach where the network elements are pipe sections, consumers and heat sources. The governing equations for hydraulic flows and heat distribution through pipe networks are presented. In addition, proper orthogonal decomposition is outlined and applied for obtaining a reduced model representation of the hydraulic equations. I t is shown that the proposed methods are suitable for predicting flow and temperature values at each consumer with minimal average error and can therefore be used as a conceptual tool for operational optimization of district heat ing networks.

PID controller design for nonlinear systems represented by discrete-time local model networks

This paper deals with proportional–integral–derivative (PID) controller design for nonlinear systems represented by local model networks. The proposed method is based on the concept of parallel distributed compensators where the scheduling of the local model network is adopted for the PID parameters. The proposed design method for nonlinear PID controllers considers closed-loop stability by means of a Lyapunov stability criterion as well as closed-loop performance. All PID parameters are determined by a multi-objective genetic algorithm (multiGA), which handles the trade-off between stability and performance. A simulation example demonstrates the effectiveness of the proposed method.