Robust Control

Integral Quadratic Constraints (IQC)

Dynamic and parametric uncertainties can be bounded using the integral quadratic constraint (IQC) framework. IQCs are incorporated into performance analysis algorithms to determine a system’s performance with respect to uncertainty, also known as robust performance. Typically, this requires knowledge of the type and magnitude of the uncertainty in order to define a model-based IQC. Here at the Chair of Flight Mechanics and Control, we are exploring data-driven methods for computing the robust performance of a system without requiring exact knowledge of the uncertainty. In collaboration with Peter Seiler at UMich, a data-driven robust performance algorithm—applicable to dynamic or nonlinear uncertainties—was applied to a spacecraft sloshing use case. Here, a single optimization run simultaneously determines the optimal performance and the corresponding IQC based solely on measured data from the uncertain behavior. Furthermore, a robust synthesis algorithm was presented at LPVS/ROCOND 2025 that was capable of synthesizing a controller robust to a measurable, memoryless, nonlinear operator. The algorithm iterates between a robust performance analysis using data-driven IQCs and a nominal synthesis. The two steps are linked by a third scaling step that accounts for the performance of the previous iteration; this ensures that performance improves with each iteration until convergence is achieved.

Selected Publications:

• E. Burgin, P. Seiler, and H. Pfifer: “Robust Performance Analysis of Linear Parameter-Varying Systems Using Data-Driven Integral Quadratic Constraints,” in IEEE Control Systems Letters, 2026

• F. Thiele, H. Pfifer, F. Biertümpfel: “Finite-Horizon Robustness Analysis under Mixed Disturbances using Signal-IQCs” in IFAC-PapersOnline 2025 (FIS)

Linear Parameter-Varying (LPV) Control

Linear parametric-varying (LPV) control has powerful capabilities for designing self-adaptive control systems. Performance is typically quantified in terms of the induced L2 norm. This represents a natural extension of the widely used Hinf control framework. Thus, various control objectives can be specified within a classical mixed sensitivity setting, which simplifies controller design. Research at the Chair of Flight Mechanics and Flight Control focuses on developing computationally efficient methods for synthesizing LPV controllers with a specified structure. A key focus is on the hardware implementation of the designed controllers, including aspects such as anti-windup.

Selected publications:

• Theis J. and Pfifer H., "Observer-Based Synthesis of Linear Parameter-Varying Mixed
  Sensitivity Controllers," International Journal of Robust and Nonlinear Control 30 (13), 5021–5039, 2020,10.1002/rnc.5038.
• Theis J., Sedlmair N., Thielecke F., and Pfifer H., "Observer-Based LPV Control with
  Anti-Windup Compensation: A Flight Control Example," IFAC World Congress, 2020.

Linear Time-Varying Control (LTV)

A wide variety of automated systems follow precomputed trajectories, meaning their nonlinear dynamics are exclusively time-dependent. Direct analysis of these nonlinear dynamics—especially in the presence of disturbances or uncertainties—is extremely complex and time-consuming. However, if these dynamics are linearized along the reference trajectory, the result is linear time-varying systems, for which a wide range of analytical robustness criteria exist. These criteria enable the rapid and efficient identification of worst-case scenarios in the design and certification process of control systems.

Selected Publications:

• Biertümpfel F., Theis J., and Pfifer H., "Observer-Based Synthesis of Finite Horizon Linear Time-Varying Controllers," American Control Conference, 2022. 10.23919/ACC53348.2022.9867184, pdf
• Evangelisti L., Pfifer H., "Probabilistic Robustness Analysis of Uncertain LTV Systems in Linear Fractional Representation," IEEE L-CSS, 2021. 10.1109/LCSYS.2021.3078881
• Ossmann D. and Pfifer H., "Robustness Analysis of Continuous Periodic Systems Using Integral Quadratic Constraints," IEEE 58th Conference on Decision and Control (CDC), 2019. 10.1109/CDC40024.2019.9029808
• Biertümpfel F. and Pfifer H., "Worst-Case Gain Computation of Linear Time-Varying Systems over a Finite Horizon," IEEE Conference on Control Technology and Applications (CCTA), 2018, 10.1109/CCTA.2018.8511591.