Memristor‐enhanced humanoid robot control system – Part II: Circuit theoretic model and performance analysis. (30th November 2017) - Baumann, D.
Summary: Neuromorphic circuits shall be considered in electronics to perform complex computing tasks in a time‐efficient and energy‐efficient fashion and to adapt their problem‐solving methodologies to changes in initial conditions and parameters. One of the key biological paradigms at the basis of their operation, allowing them to exhibit higher performance levels as compared with state‐of‐the‐art electronic systems, is the mem‐computing functionality, i.e. the capability to process and store data in the same physical location, which represents the core principle to overcome the time inefficiency of von Neumann machine architectures. With the advent of memristors, the interest in the exploitation of this principle to develop dynamic circuits for the implementation of innovative signal processing strategies has grown considerably. Here, we leverage the mem‐computing capability inherent in these devices to propose an innovative control system for motion control in a humanoid robot. In the part I paper, we introduced the paradigm theoretic foundations. In this part II manuscript, we propose circuit‐theoretic models for the new control system based upon an ideal and upon a physical memristor model and demonstrate through numerical simulations how it outperforms the old approach in terms of time‐efficiency and energy‐efficiency, maintaining a good degree of adaptability to changes in environmental conditions. Abstract : Neuromorphic circuits shall be considered in electronics to perform complex computing tasks in a time‐ and energy‐efficient fashion, and to adapt their problem‐solving methodologies to changes in initial conditions and parameters. One of the key biological paradigms at the basis of their operation, allowing them to exhibit higher performance levels as compared to state‐of‐the‐art electronic systems, is the mem‐computing functionality, i.e. the capability to process and store data in the same physical location, which represents the core principle to overcome the time‐inefficiency of von Neumann machine architectures. With the advent of memristors, the interest in the exploitation of this principle to develop dynamic circuits for the implementation of innovative signal processing strategies has grown considerably. Here we leverage the mem‐computing capability inherent in these devices to propose an innovative control system for motion control in a humanoid robot. In the Part I paper we introduced the paradigm theoretic foundations. In this Part II manuscript we propose circuit‐theoretic models for the new control system based upon an ideal as well as upon a physical memristor model, and demonstrate through numerical simulations how it outperforms the old approach in terms of time‐ and energy‐efficiency, maintaining a good degree of adaptability to changes in environmental conditions.