Estimating disturbances and model uncertainty in model validation for robust control

Abstract—Deterministic approaches to model validation for robust control are investigated. In common deterministic model validation approaches, a trade-off between disturbances and model uncertainty is present, resulting in an ill-posed problem. In this paper, an approach to model validation is presented that attempts to remedy the ill-posedness. By employing accu-rate, non-parametric, deterministic disturbance models in con-junction with enforcing averaging properties of deterministic disturbances, a novel framework enabling model validation for robust control is obtained. The approach results in a realistically estimated model uncertainty and a disturbance model, and is illustrated in a simulation example. I

Fixed structure feedforward controller tuning exploiting iterative trials, applied to a high-precision electromechanical servo system

Abstract-In this paper, the feedforward controller design problem for high-precision electromechanical servo systems that execute finite time tasks is addressed. The presented procedure combines the selection of the fixed structure of the feedforward controller and the optimization of the controller parameters on the basis of measurement data from iterative trials. A linear parameterization of the feedforward controller in a two-degreeof-freedom control architecture is chosen, which for a linear time-invariant (LTI) plant results in a feedforward controller that is applicable to a class of motion profiles as well as in a convex optimization problem with the objective function being a quadratic function of the tracking error. Optimization by iterative trials results in the controller parameter values that are optimal with respect to the actual plant, which leads to a high tracking performance. The use of iterative trials in general outperforms techniques that are based on a detailed a priori plant model only, whereas the fixed structure of the feedforward controller, i.e., the approximative inverse plant model, guarantees a high tracking performance for a class of motion profiles, unlike for example iterative learning control (ILC). Experimental results on a high-precision wafer stage illustrate the procedure

Robust optimal control of an electro-mechanical system

The control system design for high performance electrcrmechanical systems\u3cbr/\u3eis investigated. The interaction between the fast dynamics of the electrical\u3cbr/\u3epart and the slow and structural dynamics of the mechanical part must be\u3cbr/\u3eaccounted for in the control system design. For a wind energy conversion\u3cbr/\u3esystem as an example a multivariable controller is designed. Using the method\u3cbr/\u3eof optimal output feedback in combination with a multi-model approach, the\u3cbr/\u3eselection of the controller structure can be used as part of the design process,\u3cbr/\u3eand robustness is achieved with respect to non- linearities. The results for the\u3cbr/\u3ewind turbine system are shown, using a non-linear dynamic model, and these\u3cbr/\u3eare compared to results obtained with a classical PID-controller based design

Dealing with Flexible Modes in 6 DOFs Robust Control

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Dealing with Flexible Modes in 6 DOFs Robust Servo Control

Control of high performance multi-input multi-output electromechanical systems with flexible dynamics is investigated. Present feedforward and feedback control design approaches mainly focus on rigid-body plant behaviour. To achieve higher performance, flexible dynamics should be explicitly addressed during the control design. This implies that the model complexity increases in a model-based design approach. The present research project (2005-2009) focuses on an experimental and closed-loop relevant identification approach that can reliably handle six degrees-of-freedom with flexibilities. This model will be validated by means of an experimental validation and used in a robust control design