Abstract Model-based control strategies are widely used for optimal operation of chemical
processes to respond to the increasing performance demands in the chemical industry. Yet …

Through the past 20 years, the framework of Linear Parameter-Varying (LPV) systems has
become a promising system theoretical approach to handle the control of mildly nonlinear …

The current state-of-the-art in the fields of control-oriented LPV modelling and LPV system
identification is surveyed and the potential synergies between the two research areas are …

This paper describes the quasi-linear parameter varying (quasi-LPV) modeling,
identification and control of a Twin Rotor MIMO System (TRMS). The non-linear model of the …

This paper presents synthesis conditions for the design of gain‐scheduled dynamic output
feedback controllers for discrete‐time linear parameter‐varying systems. The state‐space …

This paper presents a new method to estimate linear parameter-varying (LPV) state-space
models for single-input single-output systems whose dynamics depend on one or more time …

Accurate and efficient aeroelastic models are critically important for enabling the
optimization and control of highly flexible aerospace structures, which are expected to …

This paper presents State-space Model Interpolation of Local Estimates (SMILE), a
technique to estimate linear parameter-varying (LPV) state-space models for multiple-input …

A common approach for modeling LPV systems is to interpolate between local LTI models,
often obtained by system identification methods. We study the results of interpolating in …

The problem of deriving MIMO parameter-dependent models for gain-scheduling control
design from data generated by local identification experiments is considered and a …