Multiple Model Dual Adaptive Control of Spatial Multimodal Systems

Abstract

The concept of multimodality suggests an interesting way of handling systems whose dynamics are characterized by nonlinear functions. The dynamics of such systems could be interpreted as a scheduled multimodal problem, where each mode captures the dynamics within a restricted (local) range of operating conditions and the scheduling is determined by the operating conditions themselves. This method is particularly appealing for spatially complex systems because they typically exhibit very different characteristics along different zones of the operating space. A multimodal interpretation is attractive because the local modes could be individually modelled by a less complex structure than would have been the case if one higher order model was chosen to capture the global nonlinear dynamics, such as a conventional neural network. This method of treating nonlinear systems naturally lends itself to multiple model based techniques, both for control and system identification. In control, it represents the fundamental principle behind the Gain Scheduling control technique [23, 217, 226, 227]. This scenario is distinct from the jump system case considered in the previous chapter because now the mode transitions are scheduled by some measurable operating conditions, instead of being arbitrary.