Linear Parameter Varying (LPV) systems are linear systems whose parameters are functions of a scheduling signal. The scheduling signal may be external or internal. LPV systems with internal scheduling signals are known as quasi-LPV systems.
The LPV concept derived from the gain scheduling approach to control nonlinear systems. Presently it is widely used to design control systems for nonlinear systems. Its main advantage is to allow the use of well-known linear control design techniques. But control design is based on LPV models. LPV modeling may be done by analytical methods, based on the availability of reliable nonlinear equations for the dynamics of the plant, or by experimental methods, entirely based on identification. Thus, LPV system identification emerged with the LPV paradigm. Many real systems in areas such as aeronautics, space, automotive, mechanics, mechatronics, robotics, bioengineering, process control, semiconductor manufacturing and computing systems, just to name a few, can be reasonably described by LPV models. But, despite the theoretical results with great potential produced by an intense research activity in recent years, there are still few applications in the real world.
The aim of this research topic is cover applications of LPV systems to mechatronic, automotive, aerospace, robotics, advanced manufacturing, chemical processes, biological systems, (renewable) energy systems, network systems, etc.. Both control and estimation problems will be addressed. Applications to be covered in this Research Topic may include, but are not limited to:
- Modelling and identification of LPV and quasi-LPV systems.
- Stability and stabilization, robustness issues.
- LPV and quasi LPV systems observer design, fault detection and isolation, predictive maintenance, and monitoring.
- LPV and quasi-LPV Control Systems design: H∞/H2 control, optimal control, predictive control, constrained control, fault tolerant control, virtual reference feedback tuning, sampled-data control, event and self- triggered control.
Linear Parameter Varying (LPV) systems are linear systems whose parameters are functions of a scheduling signal. The scheduling signal may be external or internal. LPV systems with internal scheduling signals are known as quasi-LPV systems.
The LPV concept derived from the gain scheduling approach to control nonlinear systems. Presently it is widely used to design control systems for nonlinear systems. Its main advantage is to allow the use of well-known linear control design techniques. But control design is based on LPV models. LPV modeling may be done by analytical methods, based on the availability of reliable nonlinear equations for the dynamics of the plant, or by experimental methods, entirely based on identification. Thus, LPV system identification emerged with the LPV paradigm. Many real systems in areas such as aeronautics, space, automotive, mechanics, mechatronics, robotics, bioengineering, process control, semiconductor manufacturing and computing systems, just to name a few, can be reasonably described by LPV models. But, despite the theoretical results with great potential produced by an intense research activity in recent years, there are still few applications in the real world.
The aim of this research topic is cover applications of LPV systems to mechatronic, automotive, aerospace, robotics, advanced manufacturing, chemical processes, biological systems, (renewable) energy systems, network systems, etc.. Both control and estimation problems will be addressed. Applications to be covered in this Research Topic may include, but are not limited to:
- Modelling and identification of LPV and quasi-LPV systems.
- Stability and stabilization, robustness issues.
- LPV and quasi LPV systems observer design, fault detection and isolation, predictive maintenance, and monitoring.
- LPV and quasi-LPV Control Systems design: H∞/H2 control, optimal control, predictive control, constrained control, fault tolerant control, virtual reference feedback tuning, sampled-data control, event and self- triggered control.
