Real Industrial Control Research Articles

Neural-network approximation of piecewise continuous functions: application to friction compensation

One of the most important properties of neural nets (NNs) for control purposes is the universal approximation property. Unfortunately,, this property is generally proven for continuous functions. In most real industrial control systems there are nonsmooth functions (e.g., piecewise continuous) for which approximation results in the literature are sparse. Examples include friction, deadzone, backlash, and so on. It is found that attempts to approximate piecewise continuous functions using smooth activation functions require many NN nodes and many training iterations, and still do not yield very good results. Therefore, a novel neural-network structure is given for approximation of piecewise continuous functions of the sort that appear in friction, deadzone, backlash, and other motion control actuator nonlinearities. The novel NN consists of neurons having standard sigmoid activation functions, plus some additional neurons having a special class of nonsmooth activation functions termed "jump approximation basis function." Two types of nonsmooth jump approximation basis functions are determined- a polynomial-like basis and a sigmoid-like basis. This modified NN with additional neurons having "jump approximation" activation functions can approximate any piecewise continuous function with discontinuities at a finite number of known points. Applications of the new NN structure are made to rigid-link robotic systems with friction nonlinearities. Friction is a nonlinear effect that can limit the performance of industrial control systems; it occurs in all mechanical systems and therefore is unavoidable in control systems. It can cause tracking errors, limit cycles, and other undesirable effects. Often, inexact friction compensation is used with standard adaptive techniques that require models that are linear in the unknown parameters. It is shown here how a certain class of augmented NN, capable of approximating piecewise continuous functions, can be used for friction compensation.

Roadway traffic control software

Roadway traffic control is an important practical example of real time control applications, but effective control is hampered by a variety of organizational, financial and technical considerations. One major hurdle is the current reliance on outmoded field hardware and software. A systematic approach to traffic engineering software development could provide significant advantages with regard to software capability, flexibility and maintenance. Improved traffic controllers will likely be essential for many of the proposed intelligent vehicle highway systems (IVHS) applications. This paper describes the roadway traffic control problem generally and introduces a computable language that can be used for constructing real time traffic control software. This computable language is designed to be configured by a graphical user interface that does not require extensive software engineering training to use, yet provides much more flexibility and capability than possible by simply changing program parameters. The model is based upon the function block metaphor commonly used for constructing robust and efficient real time industrial control systems. The software model has been implemented in C on an open architecture traffic controller (OATC) hardware platform and demonstrated under simulated conditions for applications such as signalized intersection control, ramp metering, and communications with existing traffic control devices. System users can construct applications from a library of function blocks. The paper describes a demonstration application to freeway ramp metering control in Sacramento, CA.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>