Identification and control of nonlinear electro-mechanical systems

Electro-mechanical systems are systems composed of both electrical and mechanical parts. They include motors, robots, cranes, compactors, electro-mechanical positioning systems, nanopositioning systems, and piezoelectric actuators, amongst others. As a consequence, mechanical and electrical engineering communities encompass a variety of fields such as robotics, mechatronics, electrotechnics, electronics, and power engineering, all of which can be considered a part of an over-arching electro-mechanical community. Within the electro-mechanical community, system identification refers to the whole process of identifying the most appropriate model form and estimating the parameters of this model from measured input/output data or from a combination of such data and prior knowledge. Dynamical models obtained in this manner are useful for tasks such as analysing the system properties (e.g. identification of nonlinear friction models); performing simulation experiments; and control system design (e.g. model-based control, predictive control, sliding-mode control). Such dynamical models are usually formulated in terms of differential equations, or transfer functions in the differential operator, because the physical laws on which the models are based are normally synthesised in terms of differential equation relationships based on natural laws, such as Newton's laws, Ohm's law, Kirchhoff's relations and Maxwell's equations. It is not surprising, therefore, that most electro-mechanical engineering theory and practice is based on continuous-time models. Electro-mechanical system control has similar objectives to most automatic control systems, i.e. to control the system so that its output follows a desired reference, which may be a fixed or changing value (a set point or trajectory). Despite the similarities between the automatic control and electro-mechanical engineering communities, some important differences remain. For example, within the electro-mechanical community, the identification and/or control methodology is mostly devoted to specific real-world systems rather than to general systems. The theoretical aspects of methodology, such as the statistical efficiency of the model parameter estimates or the optimality of the control system, are addressed much less often, whereas these are prolific topics in the automatic control community, where many papers are based on theoretical analysis and results. The electro-mechanical community, on the other hand, is often more reluctant to make general theoretical assumptions, tending to mistrust such generalisations when dealing with real-world systems and producing experimental results. And finally, both nonlinearity and high dimensionality are often unavoidable in electro-mechanical systems and so the consideration of such factors is much more prevalent in the electro-mechanical engineering community. Fortunately, there is evidence that these differences between the two communities are growing less and the present special issue is intended to encourage still greater cross-fertilisation between the communities, which we believe will result in advantages to both. It presents papers dealing with examples that are concerned with the identification and control of various different electro-mechanical systems; and examples that help to reveal the capabilities of current identification/control methods when they are applied to challenging, real-world applications. In the review of the content below, the papers have been separated into those that are involved with the implementation of the proposed methodology on a real electro-mechanical system, which are considered first, and then those that use computer simulation to demonstrate the feasibility of the proposed methodology.