Figure 2.5 Advanced intelligent automatic system with multi-control levels, knowledge base, inference mechanisms, and interfaces.

for maintenance or even redundancy actions, economic optimization, and coordination. The tasks of the higher levels are sometimes summarized as “process management.”

Special Signal Processing

The described methods are partially applicable for nonmeasurable quantities that are reconstructed from mathematical process models. In this way, it is possible to control damping ratios, material and heat stress, and slip, or to supervise quantities like resistances, capacitances, temperatures within components, or parameters of wear and contamination. This signal processing may require special filters to determine amplitudes or frequencies of vibrations, to determine derivated or integrated quantities, or state variable observers.

Model-based and Adaptive Control Systems

The information processing is, at least in the lower levels, performed by simple algorithms or software-modules under real-time conditions. These algorithms contain free adjustable parameters, which have to be adapted to the static and dynamic behavior of the process. In contrast to manual tuning by trial and error, the use of mathematical models allows precise and fast automatic adaptation.

The mathematical models can be obtained by identification and parameter estimation, which use the measured and sampled input and output signals. These methods are not restricted to linear models, but also allow for several classes of nonlinear systems. If the parameter estimation methods are combined with appropriate control algorithm design methods, adaptive control systems result. They can be used for permanent precise controller tuning or only for commissioning [20].

FIGURE 2.6 Scheme for a model-based fault detection.

Supervision and Fault Detection

With an increasing number of automatic functions (autonomy), including electronic components, sen-sors and actuators, increasing complexity, and increasing demands on reliability and safety, an integrated supervision with fault diagnosis becomes more and more important. This is a significant natural feature of an intelligent mechatronic system. Figure 2.6 shows a process influenced by faults. These faults indicate unpermitted deviations from normal states and can be generated either externally or internally. External faults can be caused by the power supply, contamination, or collision, internal faults by wear, missing lubrication, or actuator or sensor faults. The classical way for fault detection is the limit value checking of some few measurable variables. However, incipient and intermittant faults can not usually be detected, and an in-depth fault diagnosis is not possible by this simple approach. Model-based fault detection and diagnosis methods were developed in recent years, allowing for early detection of small faults with normally measured signals, also in closed loops [21]. Based on measured input signals, U(t), and output signals, Y(t), and process models, features are generated by parameter estimation, state and output observers, and parity equations, as seen in Fig. 2.6.

These residuals are then compared with the residuals for normal behavior and with change detection methods analytical symptoms are obtained. Then, a fault diagnosis is performed via methods of classi-fication or reasoning. For further details see [22,23].

A considerable advantage is if the same process model can be used for both the (adaptive) controller design and the fault detection. In general, continuous time models are preferred if fault detection is based on parameter estimation or parity equations. For fault detection with state estimation or parity equations, discrete-time models can be used.

Advanced supervision and fault diagnosis is a basis for improving reliability and safety, state dependent maintenance, triggering of redundancies, and reconfiguration.

Intelligent Systems (Basic Tasks)

The information processing within mechatronic systems may range between simple control functions and intelligent control. Various definitions of intelligent control systems do exist, see [24–30]. An intel-ligent control system may be organized as an online expert system, according to Fig. 2.5, and comprises

• multi-control functions (executive functions),

• a knowledge base,

• inference mechanisms, and

• communication interfaces.

The online control functions are usually organized in multilevels, as already described. The knowledge base contains quantitative and qualitative knowledge. The quantitative part operates with analytic (math-ematical) process models, parameter and state estimation methods, analytic design methods (e.g., for control and fault detection), and quantitative optimization methods. Similar modules hold for the qualitative knowledge (e.g., in the form of rules for fuzzy and soft computing). Further knowledge is the past history in the memory and the possibility to predict the behavior. Finally, tasks or schedules may be included.

The inference mechanism draws conclusions either by quantitative reasoning (e.g., Boolean methods) or by qualitative reasoning (e.g., possibilistic methods) and takes decisions for the executive functions.

Communication between the different modules, an information management database, and the man– machine interaction has to be organized.

Based on these functions of an online expert system, an intelligent system can be built up, with the ability “to model, reason and learn the process and its automatic functions within a given frame and to govern it towards a certain goal.” Hence, intelligent mechatronic systems can be developed, ranging from “low-degree intelligent” [13], such as intelligent actuators, to “fairly intelligent systems,” such as self-navigating automatic guided vehicles.

An intelligent mechatronic system adapts the controller to the mostly nonlinear behavior (adaptation), and stores its controller parameters in dependence on the position and load (learning), supervises all relevant elements, and performs a fault diagnosis (supervision) to request maintenance or, if a failure occurs, to request a fail safe action (decisions on actions). In the case of multiple components, supervision may help to switch off the faulty component and to perform a reconfiguration of the controlled process.