Adaptive high-gain control in the application of biologically inspired receptor systems

The main focus of this chapter is the adaptive signal processing of mechanoreceptors for tactile sensing of environmental information. This is, exemplarily, very important in designing measuring and monitoring systems. These systems have to exhibit an adjustment and an adaptation of their sensitivity to the environment because of unknown surroundings. The approach in this chapter is inspired from biology: animal vibrissae or sensilla. The detection of their displacements is achieved by mechanoreceptors by transducing a (mechanical) stimulus to neural impulses. We focus on their main functionality: a receptor never continues to respond to a non-changing stimulus in transducing impulses to the central nervous system (CNS). Depending on the stimulus, a receptor offers a rapid and brief excitation. This response declines if the stimulus continues. Due to the permanently changing environment, the receptor has to be in a permanent state of adaptation. Therefore, the adaption process has to include the possibility of not only increasing, but also decreasing the damping of the receptor system and therefore ensuring sensitivity. We aim to achieve a predefined movement of a spring-mass-damper system modeling the receptor dynamics, such as tracking a set point trajectory yref or stabilization, by implementing an adaptive controller to exert a control force u(t). Starting with a classical adaptor, we present a sequence of improved adaptive controllers. The working principle of each controller is shown in numerical simulations which demonstrate that the controller in fact works effectively.