Abstract-This paper extends a recently developed interpolation-based approach to design gain-scheduled controllers for linear parameter varying systems with a thorough evaluation, comprising both simulations and experiments, on an overhead crane system. In the first step of the approach, linear time-invariant controllers are designed for local working conditions of the system using a multi-objective H∞ method. With the help of this method, the fundamental trade-off between reference tracking and disturbance rejection in the overhead crane control problem is analyzed. In the second step, a statespace interpolation method is used to calculate a gain-scheduled controller. Although this approach does not guarantee stability and performance under parameter variations, experiments on the crane setup show that these variations do not compromise the performance of the obtained controller.

This paper presents a practical way to design gain-scheduled controllers for linear parameter varying (LPV) systems. An existing state-space model interpolation method for LPV systems is exploited in order to derive the desired controller. The interpolation requires designing local LTI controllers for local working conditions of the system, which is performed using a multi-objective H∞ approach. To simplify the weighting function design, the H∞ objective is broken apart into different H∞ design objectives and constraints, each related to various input-output combinations. The developed LPV control design approach is illustrated on an over-head crane system.

This paper deals with overshoot reduction in fixedorder controller design for linear systems subject to polytopic uncertainty. The basis of the developed synthesis method is a recent convex parameterization for fixed-order stabilizing controllers based on the polynomial approach. Two convex constraints are developed in order to decrease the overshoot of the closed-loop step response. First, based on the existing convex parameterization and peak-to-peak gain performance a criterion is developed to minimize the peak value of the step response. In the second method, Markov parameters of the system are used to achieve a step response with less overshoot. Simulation results illustrate the effectiveness of the developed methods.

Ahstract-While reliable and efficient tools are widely avail able to solve (full-order) Hoo control problems, formulating appropriate weighting functions constitutes the main obstacle to a good Hoo controller design. In order to facilitate the weighting function design, we propose to break apart the classical Hoo objective into various Hoo design objectives and constraints, each relating to a particular closed-loop subsystem. The result is a versatile and intuitive control design approach, and the resulting control problem is solved using the Lyapunov shaping paradigm. The potential of our approach is demonstrated on an overhead crane setup.

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