A control device (100, 100A, 300) of a straightener, the control device (100, 100A, 300) determining a feedforward compensation value (rf) corresponding to a feedback operation amount (rb) to the straightener such that a straightening curvature (q) of a wire (Wr) straightened by the straightener approaches a target curvature (qr), the straightener including: a wire supply unit (Bn) including the wire (Wr) that is wound; a plurality of rollers (Ro1 to Ro7) that are alternately disposed so as to sandwich the wire (Wr) drawn out from the wire supply unit (Bn) and that are configured to press the wire (Wr); and a wire moving unit (Mu) configured to pull out the wire (Wr) from the wire supply unit (Bn) to cause the wire (Wr) to pass through the plurality of rollers (Ro1 to Ro7), the straightening curvature (q) being a curvature of a first part of the wire (Wr) that has passed through the plurality of rollers (Ro1 to Ro7), the feedback operation amount (rb) being determined by a feedback control means based on an error (eq) between the target curvature (qr) and the straightening curvature (q), a sum of the feedback operation amount (rb) and the feedforward compensation value (rf) being output to the straightener as an operation amount (r), the control device (100, 100A, 300) comprising: a feedforward compensation means configured to determine the feedforward compensation value (rf) from a measurement value (do) obtained from the straightener, using a predictive model (Mp); and a learning means configured to perform machine learning on the prediction model (Mp) using teaching data (Ds), wherein the learning means: acquires at least one first combination including the target curvature (q), the operation amount (r) corresponding to the target curvature (qr), the measurement value (do), and the straightening curvature (q) corresponding to both the measurement value (do) and the operation amount (r); and adds at least one second combination in the at least one first combination to the teaching data (Ds), the at least one second combination including the measurement value (do) and the operation amount (r) when an absolute value of the error (eq) is smaller than a first reference value (α). a wire supply unit (Bn) including the wire (Wr) that is wound; a plurality of rollers (Ro1 to Ro7) that are alternately disposed so as to sandwich the wire (Wr) drawn out from the wire supply unit (Bn) and that are configured to press the wire (Wr); and a wire moving unit (Mu) configured to pull out the wire (Wr) from the wire supply unit (Bn) to cause the wire (Wr) to pass through the plurality of rollers (Ro1 to Ro7), the straightening curvature (q) being a curvature of a first part of the wire (Wr) that has passed through the plurality of rollers (Ro1 to Ro7), the feedback operation amount (rb) being determined by a feedback control means based on an error (eq) between the target curvature (qr) and the straightening curvature (q), a sum of the feedback operation amount (rb) and the feedforward compensation value (rf) being output to the straightener as an operation amount (r), the control device (100, 100A, 300) comprising: a feedforward compensation means configured to determine the feedforward compensation value (rf) from a measurement value (do) obtained from the straightener, using a predictive model (Mp); and a learning means configured to perform machine learning on the prediction model (Mp) using teaching data (Ds), wherein the learning means: acquires at least one first combination including the target curvature (q), the operation amount (r) corresponding to the target curvature (qr), the measurement value (do), and the straightening curvature (q) corresponding to both the measurement value (do) and the operation amount (r); and adds at least one second combination in the at least one first combination to the teaching data (Ds), the at least one second combination including the measurement value (do) and the operation amount (r) when an absolute value of the error (eq) is smaller than a first reference value (α). a feedforward compensation means configured to determine the feedforward compensation value (rf) from a measurement value (do) obtained from the straightener, using a predictive model (Mp); and a learning means configured to perform machine learning on the prediction model (Mp) using teaching data (Ds), wherein the learning means: acquires at least one first combination including the target curvature (q), the operation amount (r) corresponding to the target curvature (qr), the measurement value (do), and the straightening curvature (q) corresponding to both the measurement value (do) and the operation amount (r); and adds at least one second combination in the at least one first combination to the teaching data (Ds), the at least one second combination including the measurement value (do) and the operation amount (r) when an absolute value of the error (eq) is smaller than a first reference value (α). acquires at least one first combination including the target curvature (q), the operation amount (r) corresponding to the target curvature (qr), the measurement value (do), and the straightening curvature (q) corresponding to both the measurement value (do) and the operation amount (r); and adds at least one second combination in the at least one first combination to the teaching data (Ds), the at least one second combination including the measurement value (do) and the operation amount (r) when an absolute value of the error (eq) is smaller than a first reference value (α).