Stochastic distribution control (SDC) systems are widely seen in practical industrial processes, the aim of the controller design being generation of output probability density functions for non-Gaussian systems. Examples of SDC processes are: particle-size-distribution control in chemical engineering, flame-distribution control in energy generation and combustion engines, steel and film production, papermaking and general quality data distribution control for various industries. SDC is different from well-developed forms of stochastic control like minimum-variance and...

Modern mineral processing plants are required to be safe and profitable and to minimize their environmental impact. The consequent quest for higher operational standards at reduced cost is leading the industry towards automation technologies as capital-effective means of attaining these objectives.

Advanced Control and Supervision of Mineral Processing Plants describes the use of dynamic models of major items of mineral processing equipment in the design of control, data reconciliation and soft-sensing schemes; through examples, it illustrates tools integrating simulation and...

The Internet plays a significant and growing role in real-time industrial manufacturing, scheduling and management. A considerable research effort has led to the development of new technologies that make it possible to use the Internet for supervision and control of industrial processes.

Internet-based Control Systems addresses the challenges that need to be overcome before the Internet can be beneficially used not only for remote control and monitoring of industrial plants but also of domestic appliances and in medicine and education. New design issues such as requirement...

Adaptive Control of Flexible Structures deals with vibration control for large-scale flexible structures which are, or may be, subject to a broadband primary vibration field. The authors present a complete rationale for solving problems of vibration, from experimental structural characterization to controller implementation. In reaching this objective they address the following steps:

- experimental identification of the dynamic model of the structure;

- optimal placement of sensors and actuators;

- formulation of control constraints in terms of controller...

Tracking of autonomous vehicles and the high-precision positioning of robotic manipulators require advanced modeling techniques and control algorithms. Controller design should take into account any model nonlinearities.

Nonlinear Control of Vehicles and Robots develops a unified approach to the dynamic modeling of robots in terrestrial, aerial and marine environments. To begin with, the main classes of nonlinear systems and stability methods are summarized. Basic nonlinear control methods useful in manipulator and vehicle control - linearization, backstepping, sliding-mode...

The tandem cold rolling of metal strip is a large, highly complex nonlinear multivariable process the control of which poses a significant engineering challenge. Existing controllers have proven to be susceptible to disturbances and uncertainties in process variables and attempts to increase robustness to these phenomena have had their own drawbacks so a new approach is needed.

Tandem Cold Metal Rolling Mill Control deals primarily with a novel and practical advanced method for control of tandem cold metal rolling processes based on the recently emerging state-dependent...

Integral processes with dead time are frequently encountered in the process industry; typical examples include supply chains, level control and batch distillation columns. Special attention must be paid to their control because they lack asymptotic stability (they are not self-regulating) and because of their delays. As a result, many techniques have been devised to cope with these hurdles both in the context of single-degree-of-freedom (proportional-integral-differential (PID)) and two-degree-of-freedom control schemes.

Control of Integral Processes with Dead Time provides...