Neuromorphic engineering is a field that uses brain-inspired spiking neural networks for various applications and designs algorithms, materials, devices, and architecture to reach a level of accuracy, abstraction and energy efficiency that is close to the brain. Designing efficient neuromorphic architectures for the sensing and control of non-linear systems and robotic assemblies is a problem that we are working on and this poster shows some of the results, emanating from this work. The idea is not only to simulate a controller for these systems, but also to look at hardware, both neuromorphic chips and electro-mechanical control systems. In this poster, we have demonstrated a certain class of canonical systems (the cartpole) for optimal control, and a more complex system (the lunar lander) for data driven control, and showed the results of implementing non-neuromorphic and neuromorphic algorithms on it. Provided that it has already been shown that the neuromorphic approach offers low power and low latency benefits which becomes crucial for edge devices, we are also trying to investigate if the dynamics of spiking neurons make them more suitable for a certain class of control problems.