1. Truck suspensions

2. Conventional passive suspensionzs zu zr
suspension spring
suspension damper
tyre stiffness Kt
sprung mass (body) Ms
unsprung mass (wheel, axle) Mu

3. Active suspension

4. Fully-active suspensionsMs zs zu zr mC Mu Kt
high bandwidth actuator
control signal
sensor data
(a) Ms zs zu zr mC Mu Kt
High BW actuator
sensor data Ks
(b)
Static load supported by passive spring
Actuator provides total suspension force

5. Slow-active suspensionMs zs zu zr mC Mu Kt
Lower BW actuator
sensor data
spring
damper

6. Slow-active suspension

7. Semi-active suspension - dissipative forces onlyMs mC Mu Kt
actuator
sensor data Ks zr Fd Vs Vu
dissipative actuator

9. Hardware-in-the-Loop simulationMs Mu Kt Ks Zr
M68HC11
ADC
DIO
DAC Zu Zs
Xvc
accelerometer
relative velocity sensor
spool position command
SIMULINK model

10. Vs Fd Qv = Ap|Vrel| Pst P1 = Pst + DP V1 P1 PV P2= Pst P2 Vr Pst V2 Q2Ar
Pst V1
P1 = Pst + DP P1 PV
Extension: Vrel < 0
P2= Pst
Ap+Ar P2 Vr
Pst V2 Q2
Pst Vu
Vrel = Vu - Vs Fd

11. Fd Vs Qv = Ar|Vrel| Pst V1 P1 = Pst + DP P1 PV Q1 Vr V2 P2 P2=P1 Pst
Contraction: Vrel > 0 Q1
Ap+Ar Vr V2 P2
P2=P1
Pst
Pst Vu
Vrel = Vu - Vs
P1 = P2 = Pst + DP Fd

12. Proportional control valveB
flow Qv
pressure P1 - DP
pressure P1 Xv
spool displacement T
orifice flows
solenoid
LVDT

13. Mechanical design Determine the leading dimensions of the damper
rod length, diameter and wall thickness;
inner tube bore and wall thickness;
outer tube bore
Remember the important specification that the bump and rebound force-velocity characteristics are to be symmetrical.

14. Damper design
Convert the pressureflow envelope of figure 7 to a damping forcerelative velocity envelope for your design.
Make plots on this chart of the damper force Fd versus relative velocity Vrel for values of Xv = 0.1, 0.2, 0.3, 1.0.
Make a separate plot of Fd versus Xv for different values of Vrel.

15. Spool position controller
Force controller
Force controller
Spool position controller
Damper dynamics
Force transducer
Xvc Xv Fd
Fdsa
MSD force command
Damper
force + -
Vrel

16. Feedforward + Feedback
Linear feedback controller
Spool and damper dynamics
Force transducer
Xvfb
Xvc Fd
Fdsa + -
Vrel
Nonlinear feedforward controller
Xvff

17. Force controller design
Given the linearised plant model, design a PI or PID controller for a chosen nominal operating condition, and check its robustness against changes in operating point.
A suggested nominal operating condition is Fd0 = 2500 N, Vrel0 = 0.15 m/s.
Recall the specification that the desired bandwidth for the force controller is 20 Hz.

18. rltool

19. Alternative controller design
Use the Ziegler-Nichols ‘ultimate sensitivity’ method to design a PI or PID controller.
That is, initially set the integral and derivative gains to zero, and increase the proportional gain until the system oscillates on the point of instability.
Then measure the ‘ultimate gain’ Ku and the ‘ultimate period’ Pu, and apply the tuning rules to obtain a first-cut set of values for the controller gains.

20. fctrl.mdl

21. MSD controller design
Design a real-time program for the M68HC11 microcontroller to perform the semi-active damper control task.
The MSD control law is defined in equations (4) and (5). Suitable initial parameter values are Cm = 45 kN/(m/s) and  = 0.2.
(4)
(5)

22. Implementation
Then implement your program in a hardware-in-the-loop simulation, using the SIMULINK model HiL_sys provided.
The roadway roughness input can be selected to be deterministic (e.g., sinusoidal corrugations) or random (corresponding to a road profile that could be encountered on a main road at 70 km/h).
Time histories of simulation variables will be written into the MATLAB workspace, so that the performance of the controller can be assessed.

23. Design tools provided SIMULINK model, SIM_sys
This is identical with HiL_sys, except that a subsystem block M68HC11 is included as a representation of the microcontroller.
You can modify this block to create your own SIMULINK representation of your controller code, to test its operation before attempting the HiL simulation.
Ziegler-Nichols tuning tool fctrl
invoke with fctrl_start

24. SIM_sys.mdl

25. Schedule

26. PID controllers PID = Proportional + Integral + Derivative
Also known as "three-term controller"
About 90% of all control loops are closed with some form of PID controller
In this group of lectures we will find out:
why PID controllers are used so often
ways of "tuning" a PID controller
how to deal with actuator saturation

27. Functions of control system+
W load disturbance
Gp(s)
Plant U
control Y
output
Gc(s)
Controller + - R
reference input, or set-point E
sensed error + N
sensor noise
Track reference input, or maintain set point, despite:
load disturbances (usually low frequency)
sensor noise (usually high frequency)
Achieve specified bandwidth, and transient response characteristics

28. Performance of control system
Gc(s)
Controller + N
sensor noise
W load disturbance
Gp(s)
Plant U
control Y
output - R
reference input, or set-point E
sensed error
Sensor noise reproduced just like reference input
use low noise sensors!
seek to make
To reject disturbances, make

29. PID controller functionsKp + P I D
e(t)
u(t) Kp + P I D
E(s)
U(s)
Output feedback
from Proportional action
Eliminate steady-state offset
from Integral action
Anticipation
from Derivative action
compare output with set-point
apply constant control even when error is zero
react to rapid rate of change before error grows too big

30. Transfer function of PID controller
derivative time constant
integral time constant, or 'reset time'
If no derivative action, we have PI controller:
proportional gain
integral gain

31. Effects on open-loop transfer function
Example:
s-plane
pole at origin
increases Type No. o   j  o 
Plant poles
zeros pull root locus branches to left: stabilising
Closed-loop poles for Kp = 11.5

32. Effects on open-loop transfer function
Frequency response
problem! amplifies high freq. noise
amplitude boost at low frequencies to reduce steady-state error
0dB
log w
+90º
-90º
LogMag
Phase
phase lead to increase phase margin, bandwidth

33. Application of PID control
PID regulators provide reasonable control of most industrial processes, provided performance demands not too high
PI control generally adequate when plant/process dynamics are essentially 1st-order
plant operators often switch D-action off: "dificult to tune"
PID control generally OK if dominant plant dynamics are 2nd-order
More elaborate control strategies needed if process has long time delays, or lightly-damped vibrational modes

34. Simulink PID models

35. Simulink PID models