No / An adaptive iterative learning control approach is proposed for a class of single-input single-output uncertain nonlinear systems with completely unknown control gain. Unlike the ordinary iterative learning controls that require some preconditions on the learning gain to stabilize the dynamic systems, the adaptive iterative learning control achieves the convergence through a learning gain in a Nussbaum-type function for the unknown control gain estimation. This paper shows that all tracking errors along a desired trajectory in a finite time interval can converge into any given precision through repetitive tracking. Simulations are carried out to show the validity of the proposed control method.

No / A multivariable model of an automotive gas turbine, obtained from the linearized system equations is investigated. To facilitate vehicle speed changes, whilst protecting the system against thermal damage, control of the power turbine inlet gas temperature and gas generator speed is proposed by feedback regulation. Fuel flow and the power turbine nozzle area variations are the selected, manipulatable inputs. Owing to the limited control energy available for regulation purposes a multivariable, optimum, minimum control effort strategy is employed in the inner loop controller design study. Simulated, open and closed loop system responses are presented for purposes of comparison. Significant improvements in the transient response interaction reaction times and low steady state output interaction achieved using passive compensation and output feedback alone. Simplification of the closed loop configuration is proposed in the final implementation without performance penalties.