Robust control is normally used to handle systems with uncertainties & disturbances and with high performances. The objective of robust control is to be able to design controllers that achieve a desired level of performance and also, be able to handle a collection of uncertainty structures specified by the designer. A typical approach is to try to maximise the tolerated uncertainty bounds.
The robust control techniques are based on a formal description of the control problem where the stability and performance objectives must be fulfilled for any plant, S, belonging to a family G. The family of plants may be described by parameter variations, as bounds in frequency response, etc.
Two problems are the core of robust control methodologies:
Analysis – establishing if a regulator, designed with any methodology, will withstand a known modelling error structure and size, in terms of:
- Robust stability (RS) – excluding the possibility of an unstable closed loop for the available modelling error estimation.
- Robust performance (RP) – asserting that the performance objectives will be fulfilled for any plant in the uncertain family.
Synthesis – determining a regulator that maximises tolerable modelling error for a given performance level, or alternatively, determining the maximum performance level for a given modelling error description.
You may also read:
- Control Modes in Automation Systems
- The Performance Limits for PID Controllers
- Hierarchical Control Strategies for a Process Plant
- Basic Steps to Consider in Designing a Control System
- 5 Control Techniques – Their Features & Applications
- Key Causes of Uncertainty in Control Systems
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