# Neuroadaptive Robust Speed Control for PMSM Servo Drives with Rotor Failure
## 4 Simulation Results  
The PIBSNN controller is applied for speed regulation of PMSM. When the PIBSNN is required to generate a specific function in the output vector, it is necessary to train it iteratively offline. The weights of the PIBSNN are adjusted off-line using the MFOA. The complete simulations are implemented MATLAB R2020b and are implemented in a PC with an Intel Core i9 processor with 2.40 GHz speed and 32.00 GB RAM. The inputs to the PIBSNN are current error in \(q\) axis (\(e_{iq}\)), current error in \(d\) axis (\(e_{id}\)), \(e_{cur}\), and their derivatives (\(\dot{e}_{iq}\), \(\dot{e}_{iq}\)_and_\(\dot{e}_{cur}\)) respectively. During the progress of training on-line, the neurocontroller adjusts PI controller parameters \(k_{p,\mu,r}\), \(k_{i,\mu,r}\), \(k_{p,id}\), \(k_{i,id}\), \(k_{p,iq}\) and \(k_{i,iq}\) and regulates the speed of the PMSM rotor efficiently against bounded disturbances. Figure 1 displays the block diagram of PIBSNN controller strategy. This control scheme has fourteen unknown parameters of three adaptive controllers that should be determined by optimization techniques. The design parameters are weights factors (\(w_{1,kpx}\), \(w_{2,kpx}\), \(w_{3,kpx}\), \(w_{4,kpx}\), \(w_{1,kix}\), \(w_{2,kix}\), \(w_{3,kix}\), \(w_{4,kix}\)) and PI parameters (\(k_{p,\mu,r}\), \(k_{i,\mu,r}\), \(k_{p,id}\), \(k_{i,id}\), \(k_{p,iq}\) and \(k_{i,iq}\)). Table 1 presents the parameters and simulation coefficients considered in the simulation of the dynamic PMSM system. Figure 3 is presented the process of searching for the parameters of the speed control scheme for the nonlinear dynamic system of PMSM. Using the MFOA, the search and test of the operation of the control scheme parameters are carried out. Table 2 displays the parameters used to implement the MFOA. To find the initial conditions of the PI controllers and weights factors on the PIBSNN controller using the MFOA algorithm; a reference speed \(\omega_{\rm r}\) = 400 rpm is employed, with a load torque of 4 N.m, \(a_{1}\) = 0.2, \(a_{2}\) = 3, \(c_{1}\) = 0.2, \(c_{2}\) = 0.015, and \(c_{3}\) = 0.02. Figure 3: Flowchart of MFOA. The set of transient responses delivered by the first iteration of the MFOA, which comply with the parameters established in the algorithm, to find the most optimal parameters of the neuroadaptive robust speed control for PMSM servo drives with rotor failure, are shown in Figure 4 in different colors. The best transient response of the rotor speed when the execution of the MFOA ends is shown in Figure 5. The optimal parameters for the neuroadaptive controller found by the MFOA are: \(w_{1,kpr}\ =\ 390.5\), \(w_{2,kpr}\ =130.5\), \(w_{3,kpr}\ =9.1\), \(w_{4,kpr}\ =362.8\), \(w_{1,kvir}\ =21.45\), \(w_{2,kvir}\ =80.1\), \(w_{3,kir}\ =127.85\), \(w_{4,kir}\ =37.1\), \(k_{p,\omega}\), \(=0.1839\), \(k_{i,\omega}\), \(=51.71\), \(k_{p,id}\ =47.08\), \(k_{i,id}\ =19.2\), \(k_{p,iq}\ =25.12\), and \(k_{i,iq}\ =5.11\). In this work, to know the performance and behavior of the neuroadaptive controller, two different contexts are assumed. The first case evaluates when the PMSM has a variable load torque under normal operating conditions. In the second context, to verify the robustness and efficiency of the neuroadaptive controller, a load torque with abrupt variations is added to the rotor shaft and PMSM presents unknown friction in the rotor.