In recent years there has been extensive interest in feedback control systems that
automatically adjust their controller settings to compensate for changes in the process or the …

Many authors have noted the difficulty of developing the models required for nonlinear
model predictive control (NMPC) and other nonlinear, model-based control strategies. One …

In this work, a novel data-driven control approach, model-free adaptive control, is presented
based on a new dynamic linearization technique for a class of discrete-time single-input and …

In this paper, we examine the global stability and instability problems for a class of discrete-
time adaptive nonlinear stochastic control. The systems to be controlled may exhibit chaotic …

In this paper the potentials of neural networks-based control techniques are explored by
applying a nonlinear generalized minimum variance control methodology to a simulated …

We investigate the feasibility of applying connectionist networks with hidden units to
forecasting and process control. We place the focus on establishing parallels with the …

A robust version of the self‐tuning regulator is developed. The regulator, which requires
relatively little knowledge of system characteristics (estimated order of transfer function …

A control-weighted self-tuning minimum-variance controller with a non-linear difference
equation structure is described. An extended recursive least-squares estimation algorithm is …

This paper presents adaptive control algorithms which have been designed for use on two
applications, namely dissolved oxygen control in activated sludge waste water treatment …

Two new self-tuning control (STC) strategies for nonlinear control problems are proposed.
These strategies are applicable to a broad class of nonlinear single-input, single-output …