This invention relates to electronic devices capable of maximizing or minimizing a value which is a function of a number of parameters, and what is generically referred to as "cost function". The invention is more particularly directed to a multiple parameter control circuit capable of simultaneously adjusting controlled input variables for maximizing or minimizing a particular performance criterion, for example, in an industrial process.
In a typical industrial system, such as a machine, power plant, or the like, the operation generates a so-called "cost function", for example, power, efficiency, fuel economy, safety, etc. The cost function, in turn, is a function of a number of controlled parameters, such as fuel feed rate, combustion air pressure and temperature, exhaust damper settings, etc. Each of these parameters may be adjusted independently of the others, but the optimum value of the cost function will require optimal setting of all of the various parameters.
Cost functions, which can be optimized in terms of the setting of a multiplicity of input parameters, can also come about in computational problem solving, such as curve fitting, maximum likelihood calculations, polynomial expansions, and the like. Cost functions are also used in computer simulations, such as the determination of optimal designs in engineering, scheduling problems, many-body particle interactions, crystal growth problems, and other problems of physics and the sciences.
An orthogonal modulation technique for optimizing control systems has been described in U.S. Pat. No. 3,617,717. In that technique, a number of control signals result from multiplying a performance indicating signal by respective modulation signals, and the result is integrated and sampled. However, this technique is rather complex and so requires complex circuitry. In particular, a digital computer is required to carry out a large number of iterative calculations. The procedure is rather time consuming, which means that the cost function will not always converge quickly to an optimal value. That system can control only a small number of parameters. The correlation between feedback and cost function is weak, and convergence is slow.