  Practical Active Noise Control (ANC) systems typically require a restriction
in their maximum output power, to prevent overdriving the loudspeaker and
causing system instability. Recently, the minimum output variance
filtered-reference least mean square (MOV-FxLMS) algorithm was shown to have
optimal control under output constraint with an analytically formulated penalty
factor, but it needs offline knowledge of disturbance power and secondary path
gain. The constant penalty factor in MOV-FxLMS is also susceptible to
variations in disturbance power that could cause output power constraint
violations. This paper presents a new variable penalty factor that utilizes the
estimated disturbance in the established Modified-FxLMS (MFxLMS) algorithm,
resulting in a computationally efficient MOV-MFxLMS algorithm that can adapt to
changes in disturbance levels in real-time. Numerical simulation with real
noise and plant response showed that the variable penalty factor always manages
to meet its maximum power output constraint despite sudden changes in
disturbance power, whereas the fixed penalty factor has suffered from a
constraint mismatch.
