Based on the previous discussion about intelligence and assuming that an intelligent controller should be able to emulate the most essential traits of intelligence, then it must possess at least the following competencies:
- Prediction: Considering that prediction represents the ultimate function of the nervous system, the intelligent controller must be able to incorporate the available knowledge about the plant, in order to anticipate the appropriate control action.
- Adaptation: All intelligent organisms use adaptation to accommodate themselves to the needs for survival. Hence, the control scheme should also adapt itself to changes in the plant and in the environment.
- Learning: The capacity to learn by experience and acquire knowledge by interacting with the environment is essential to an intelligent system. Within the proposed framework, online learning improves the ability to predict the dynamics of the plant during task execution.
It is important to note that the three attributes considered in the proposed framework can also be used to distinguish the wide range of control schemes. Model-based controllers, for example, are able to consider only a priori knowledge. Purely adaptive schemes can not learn and always have to adapt themselves, even to previously experienced situations. In fact, it is easy to infer that neither adaptive nor model-based controllers could be considered intelligent. Nevertheless, there are several situations where the distinction would not be so straightforward.
Some control approaches that make use of soft computing techniques might eventually be considered intelligent in a broad context. Artificial neural networks employing off-line supervised training or fuzzy methods based on heuristic schemes have been widely applied for control purposes and certainly have their application scopes as well. However, a mechatronic agent equipped with such controller could not adapt itself in face of new experiences, neither would it perform online learning by interacting with the environment. Therefore, in a strict sense, that agent would not be able to come up with intelligent behavior.
Lastly, I would like to emphasize that the capacity to emulate intelligence does not necessarily imply better control performance. There are situations, as in the case of industrial robots operating in a well structured environment and performing repetitive tasks, in which a model-based controller would be the most appropriate choice as long as contact dynamics are not predominant. Moreover, robust or adaptive methods can also effectively deal with plants subject to structured uncertainties, i.e., parameter variations. However, when a high level of uncertainties comes on the scene and a mechatronic agent must operate in an environment of imperfect information, intelligent controllers represent the most appropriate strategy.
This post is an adapted extract from the paper A biologically inspired framework for the intelligent control of mechatronic systems and its application to a micro diving agent, by W. M. Bessa et al. [1]
[1] W. M. Bessa, G. Brinkmann, D. A. Duecker, E. Kreuzer, and E. Solowjow. A biologically inspired framework for the intelligent control of mechatronic systems and its application to a micro diving agent. Mathematical problems in engineering, 2018. [DOI]