To extend the neuromorphic control framework to more complex mechanical systems like legged robots or multi-link manipulators, several key considerations need to be addressed. Firstly, the rhythmic control architecture used for the pendulum can be scaled up by incorporating multiple oscillators to control different parts of the system. Each oscillator can be responsible for generating specific rhythmic patterns that coordinate the movements of different limbs or links in the robot. By synchronizing these oscillators through excitatory and inhibitory connections, the system can achieve coordinated motion.
Furthermore, the adaptive control mechanisms demonstrated in the pendulum model can be extended to regulate the behavior of more complex systems. By incorporating adaptive neuromodulation, the control parameters of the neural network can be dynamically adjusted to respond to changing environmental conditions or system requirements. This adaptability enhances the system's resilience and performance in varied scenarios.
Maintaining the event-based nature of control in these complex systems is crucial for energy efficiency and robustness. By confining energy exchange to specific events, the system can operate with high impedance during interactions, ensuring stability and efficiency. The temporal sparsity of events also contributes to low impedance control when averaged over time, combining the benefits of soft actuation with robust control requirements.