Dual Control Method Research Articles

ADAPTIVE CONTROL AND OPTIMAL INPUT DESIGN: A REALISTIC APPROACH

SUMMARYThe interplay between estimation and control is crucial in adaptive control algorithms. Most proposed adaptive dual control methods are either too complex for practical applications or else do not rely on any stability proof. By restricting ourselves to a limited class of systems (linear time‐invariant) and a very crude dual action (single‐step optimizing input) we arrive at a very simple approach, which should be interesting for more complex situations also.

Adaptive dual control for stochastic nonlinear systems with free end-time

This short paper discusses the problem of controlling stochastic nonlinear systems where the end-time of the process has to be determined on-line. Since the optimal solution is not obtainable, a dual control method is developed for this class of problems. The algorithm has the property that the control, which is restricted to be causal, utilizes the statistical description of the future observations, i.e., it is of the closed-loop, rather than feedback type. This property allows the controller to enhance the estimation via the dual effect and thus improve the overall performance. The method is illustrated by applying it to an interception problem. Simulation studies on this particular problem provide further insight into how the dual effect of the control can be utilized in nonlinear problems. They also indicate the potential value of the algorithm presented here to practical problems.

An on line adaptive control method

This paper presents a method for the suboptimization of closed-loop adaptive systems by use of a simple dual control method. Dynamic programming is employed for optimization while simultaneously the system is identified by use of the least-squares method. The system is described by difference equations which connect the observed output variables with the input, or control, variables. For multivariable systems this procedure results in a great reduction of programming effort. Simulation studies were carried out and it was found that in many closed-loop situations high-order systems may be well approximated by second order models, and that non-linear systems may be replaced by low-order time-varying linear models. Several algorithm parameters are free, and must be adapted during operation; the interconnection between control and identification renders this task difficult.