Acronyms
PMSM	Permanent magnetic synchronous motor
SVPWM	Space vector pulse width modulation
NN	Neural network
RBFNN	Radial basis function neural network
DO	Disturbance observer
PPF	Prescribed performance function
FDSC	Funnel dynamic surface control
NDSC	Neural dynamic surface control
PID	Proportion integral differential
IAE	Integration over the absolute value of the error
ITAE	Integration over time and the absolute value of the error
ISE	Integration over squared error
Variables
$x_i,i=1,\cdots ,4$	State variables
$\Delta E$	External disturbance term
$\Delta l_i(x(t-{\tau }_i)),i=1,\cdots ,4$	Time delay terms
${\wp }^{\ast }\in R^l$	Desired weight vector of RBFNN
$\widehat{\wp }$	Updated weight vector
$\psi (X)$	Estimation error
${\nu }_i={[{\nu }_{i1},\dots ,{\nu }_{im}]}^T$	Receptive field centers
${\beta }_i,i=1,\cdots ,4$	Unknown variables
$u_{ic},i=2,3$	The output of first-order filters
$u_i$	Virtual controllers, the input of first-order filters
${\varphi }_i,i=2,3$	First-order filter errors
${\eta }_1$	Modified funnel variable
Functions
$f_1(t)$	Positive funnel prescribed performance function
$F_k(\cdot )$	Gain function over time
$G(t)$	Scaling function
${\xi }_{ij}>0$	$\mathrm{Smooth} \mathrm{functions} \mathrm{in} \mathrm{Lyapunov}–\mathrm{Krasovskii} \mathrm{functional} V_T$
Parameters
$l>1$	Node numbers of RBFNN
${\chi }_i$	Widths of the Gaussian functions