The Development of Neural Network Based System Identification ...

178 CHAPTER 6 NEURAL NETWORK BASED PREDICTIVE CONTROL SYSTEM
Outer Loop
Control
Inner Loop
Control
Helicopter
y ref
y,v
PID v
φref
PID u lat
φ
u lat
Lateral
Dynamic
x ref
x,u
PID u
θ ref
PID u long
θ
u long
Longitudinal
Dynamic
z ref
PID z
z
w ref
w
PID u col
u col
Heave
Dynamic
ψref
ψ
PID r
r ref
PID u ped
r
u ped
Yaw
Dynamic
State Estimation
Figure 6.3 The helicopter control with cascaded control approach. Multiple SISO based PID
controllers is used in the inner and outer loop.
responsible for guidance and generation of attitude or velocity commands for the inner
loop control to achieve the desired position targets. In this study, the input/output
pairing for the inner loop control is selected based on suggestions in Valavanis [2007]
and Mettler [2003]. The suggested input/output pair is also expressed in Figure 6.3.
The overall control system architecture designed for the helicopter UAS consists of
the control subsystems: a MIMO MPC with roll and pitch dynamics; a SISO MPC with
yaw rate dynamics and a SISO MPC with altitude rate dynamics. The control system
architecture is depicted in Figure 6.4. The control system architecture is selected as a
TITO system considering coupling between lateral and longitudinal channels while yaw
rate and altitude rate (heave) dynamics are treated separately [Mettler, 2003, Valavanis,
2007, Samal, 2009]. The arrangement of this controller architecture assumes that the
tail rotor cyclic and the collective pitch do not influence the roll and pitch channel. In
each of the control subsystems, a HMLP network is utilised to represent the dynamic
model of the MPC. The training of these HMLP networks can be done using the off-line
LM or recursive based GN training. In recursive based training, the initial weights of