1. What is the proposed method for estimating IPMSM parameters?
The proposed method for estimating IPMSM parameters is the dynamic certainty equivalence (DyCE) adaptive scheme. This method successfully estimates the parameters of IPMSMs even if they fluctuate with temperature and environmental changes. The estimated parameters are differentiated for the input to the automatic current regulator (ACR). However, the system becomes nonlinear when a limiter is inserted, affecting the stability of parameter estimation. The parameters are estimated to achieve zero control error, but a high-gain feedback controller may not be effective due to susceptibility to observation noise. To overcome this, a feedback-error learning (FEL) control is proposed, which compensates for parameter fluctuations adaptively and ensures stability regardless of load changes. The FEL controller includes a phase delay compensator to avoid excessive input to the ACR and improve transient response. Robustness to disturbance torque is also guaranteed. The effectiveness of the proposed position control system is demonstrated through experiments.
read more
2. What are the issues to be solved in the conventional position control system (7)?
The issues to be solved in the conventional position control system (7) include the instability of behavior of a due to the nonlinearity of the limit to prevent excessive inputs to the ACR and the trade-off between disturbance suppression performance and estimation speed of a for the gain of APR. The adaptive scheme is supposed to work in linear systems, but the stability of a is no longer guaranteed due to the nonlinearity introduced by the limit. Additionally, there is a trade-off between disturbance suppression performance and the convergence speed of a, which results in insufficient pole-zero cancellation and reduced phase margin. Therefore, the gain of the APR must be small, sacrificing disturbance suppression performance. These issues need to be addressed to improve the performance of the conventional position control system.
read more
3. What is the purpose of the parallel feedforward compensator (PFC) in the proposed position control system?
The parallel feedforward compensator (PFC) in the proposed position control system is arranged in parallel to s-1P(s) instead of a serial connection to it. This arrangement avoids differential computation of a, which prevents excessive input to the ACR. The PFC is a well-known method in FB controller design to satisfy the passivity of the closed-loop system. By using the PFC, the proposed FEL system estimates uFF, which is the ideal q-axis current reference, to ensure that th re agrees with th * re. This helps in overcoming the trade-off between disturbance suppression performance and learning speed of uFF in the FEL system.
read more
4. How does PFC stabilize learning of P-1(s)s?
The PFC stabilizes learning of P-1(s)s by designing C PFC(s) such that the relative degree of the augmented transfer function G(s) = s-1 P(s) + C PFC(s) becomes one. This ensures passivity and satisfies the passivity condition. In the paper, the APR is designed as a PD controller, acting as a first-order phase lead compensator. By designing C PFC(s) as a first-order delay system, the relative degree of G(s) = P-1(s)s + C PFC(s) becomes two. The cut-off frequency (o c) should be lower than the frequencies included in th* re, and the gain (K) is adjusted to confirm the response of thre and the steady-state value of th PFC. Using the APR and G(s) in (8), the relationship between u FB and P-1(s)s - P-1(s)s is obtained. The relative degree of G FEL(s) is zero, satisfying the passivity condition. The augmented error (e-th PFC) converges to zero using the NN for stabilization of learning of P-1(s)s. However, an offset may remain, requiring a small K in (7) to reduce it. Overall, the PFC design ensures the stabilization of learning of P-1(s)s.
read more