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This paper applies the discrete-time single-input/single-output Model Reference Adaptive Control (MRAC) design technique of Landau and Lozano to the problem of regulating feed force on a lathe under varying cutting conditions. A first-order model is used to represent the relationship between feed force and the control input (feedrate). The MRAC scheme is implemented on a multi-microprocessor based computer-numerical-control system. Results of applying various algorithms derived from the MRAC design technique are presented.

On -line monitoring of the cutting process in CNC lathe is desirable to ensure unattended fault -free operation in an automated environment. The state of the cutting tool is one of the most important parameters which characterises the cutting process. Direct monitoring of the cutting tool or workpiece is not feasible during machining. However several variables related to the state of the tool can be measured on -line. A novel monitoring technique is presented which uses cutting torque as the variable for on -line monitoring. A classifier is designed on the basis of the empirical relationship between cutting torque and flank wear. The empirical model required by the on -line classifier is established during an automated training cycle using machine vision for off -line direct inspection of the tool.

SUMMARYA class of discrete time feed forward tracking controllers is presented for linear multivariable plants subjected to time-varying exogenous inputs with known models. The controller design is based on linear quadratic optimization techniques and the augmented system approach. The optimal controllers use state feedback for regulation purposes. For the tracking objective the future set-points along the reference trajectory vector are used. Necessary and sufficient conditions for the existence of an optimal feedforward solution are presented in terms of the relationships between the eigenvalues of the closed-loop system and those of the trajectory models. For illustration a numerical example is included which is based on a dual-stage positioning carriage modelled as a linear multivariable plant.