About this Research Topic
During the synthesis and verification of robust procedures for stabilizing controllers and state estimation schemes, engineers need to deal with missing knowledge about parts of a system’s structure, model simplifications, imprecisely identified parameters or external disturbances and noise influencing both system inputs and outputs. Therefore, uncertainty – in all its different forms such as aleatory or epistemic – is omnipresent in modeling, simulation and parameter identification of dynamic systems in each area of engineering, making its quantification and propagation an important research direction.
Until approximately the 1960s, the major possibility to deal with uncertainty was through probability. Since then, many other approaches or generalizations of the classical probability theory have appeared. Now, they are tailored for a given application and type of uncertainty. For instance, techniques from the area of interval and set-valued computation can be employed for representing and propagating bounded uncertainty (more generally, methods with result verification). Moreover, robust model-predictive control strategies, Lyapunov methods for a guaranteed proof of stability despite uncertainty, or robust stochastic filtering techniques can be used.
With the focus on uncertainty, this research topic article collection aims at bringing together methods from the areas of a robust control and estimator synthesis with strategies for an offline verification of performance and stability in the earliest possible design stages. In addition, we plan to highlight novel mathematical foundations and aspects for software implementation of those strategies. Finally, contributions dealing with real-life applications from the areas of energy systems, robotics, marine technology and control for biomedical and life science will be highly welcome.
Potential topics include but are not limited to:
- Interval methods for verified simulation of dynamic systems
- Uncertainty quantification and propagation in engineering
- Control-oriented modeling and identification of complex dynamic systems in engineering
- Reliable distributed control and state estimation procedures
- Lyapunov techniques for control and observer design
- model-predictive control for linear and nonlinear dynamic systems
- Robust control and estimation
- Optimization of systems with uncertainty and disturbances
- Applications to the control of energy systems, robotics applications, marine technology and biomedical processes
Until approximately the 1960s, the major possibility to deal with uncertainty was through probability. Since then, many other approaches or generalizations of the classical probability theory have appeared. Now, they are tailored for a given application and type of uncertainty. For instance, techniques from the area of interval and set-valued computation can be employed for representing and propagating bounded uncertainty (more generally, methods with result verification). Moreover, robust model-predictive control strategies, Lyapunov methods for a guaranteed proof of stability despite uncertainty, or robust stochastic filtering techniques can be used.
With the focus on uncertainty, this research topic article collection aims at bringing together methods from the areas of a robust control and estimator synthesis with strategies for an offline verification of performance and stability in the earliest possible design stages. In addition, we plan to highlight novel mathematical foundations and aspects for software implementation of those strategies. Finally, contributions dealing with real-life applications from the areas of energy systems, robotics, marine technology and control for biomedical and life science will be highly welcome.
Potential topics include but are not limited to:
- Interval methods for verified simulation of dynamic systems
- Uncertainty quantification and propagation in engineering
- Control-oriented modeling and identification of complex dynamic systems in engineering
- Reliable distributed control and state estimation procedures
- Lyapunov techniques for control and observer design
- model-predictive control for linear and nonlinear dynamic systems
- Robust control and estimation
- Optimization of systems with uncertainty and disturbances
- Applications to the control of energy systems, robotics applications, marine technology and biomedical processes
Keywords: Interval methods, Estimation and identification, Robust and predictive control, Verification & Validation, Uncertainty quantification
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
