Abstract

Artificial neural networks and genetic algorithms are often quoted in discussions about the contribution of biological systems thinking to engineering design. This paper reviews work on the neuromuscular system, a field in which biological systems thinking could make specific contributions to the development and design of automatic control systems for mechatronics and robotics applications. The paper suggests some specific areas in which a better understanding of this biological control system could be expected to contribute to control engineering design methods in the future. Particular emphasis is given to the nonlinear nature of elements within the neuromuscular system and to processes of neural signal processing, sensing and system adaptivity. Aspects of the biological system that are of particular significance for engineering control systems include sensor fusion, sensor redundancy and parallelism, together with advanced forms of signal processing for adaptive and learning control. 

Highlights

  • For many years engineering researchers have been interested in exploring ways in which knowledge about biology can contribute to engineering design

  • The inclusion of large numbers of sensors that can be embedded in these artificial muscles, like muscle spindles and tendon organs in real muscle, opens up the further possibility of highly redundant control systems that resemble the structure of the neuromuscular system and could begin to offer the high levels of redundancy in terms of sensors, actuation units and signal transmission pathways that seem to offer advantages in the neuromuscular control system

  • Any engineer must inevitably have respect for the excellence of the design that can be seen in biological systems

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Summary

Introduction

For many years engineering researchers have been interested in exploring ways in which knowledge about biology can contribute to engineering design. More recent examples where biology has provided important links that have led to widely-used design tools, include evolutionary methods of optimisation (such as genetic algorithms and genetic programming) and to artificial neural networks. In both of these areas biological analogies and biological thinking have contributed to the tools as they are used at present, but it is clear that the biological context of much of the original research is largely irrelevant to those who routinely use the techniques in solving engineering problems. Structure and function are related at all scales and levels in biological systems in a way that is seldom approached in present-day man-made systems

Biological control systems
The neuromuscular control system
Computational models of the neuromuscular system
An alternative nonlinear model of the neuromuscular system
Discussion
Conclusions
Full Text
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