Recent developments in control, power electronics and renewable energy by Dr Zhong

An Overview of Activities in

C ONTROL AND P OWER

Qing-Chang Zhong
zhongqc@ieee.org

Electrical Drives, Power and Control Group
Dept. of Electrical Eng. & Electronics
The University of Liverpool

Q.-C. Z HONG : A N OVERVIEW OF ACTIVITIES IN C ONTROL T HEORY & E NGINEERING – p. 1/77

Outline

Research activities in control
Research activities in power
Other research activities
Practical experiences
New-ACE
Teaching
Funding
Future research plan


Research activities in control
On the theoretical side, my research has been focus-
ing on robust control, time-delay systems, process
control, and recently applying the theory of inﬁnite-
dimensional systems to time-delay systems. A series
of problems have been solved:
Projections
J-spectral factorisation
Delay-type Nehari problem
Standard H ∞ problem of single-delay systems
Realisation of distributed delays in controllers
Feedback stabilizability of linear systems with
state and input delays in Banach spaces

Major publications

IEEE Trans. Automatic Control: 7
Automatica: 4
Other IEEE Transactions: 3
IET Control Theory & Applications: 4
One research monograph


Projections
For a given nonsingular matrix partitioned as M N , denote
the projection onto the subspace Im M along the subspace Im N
by P . Then, the projection matrix P is
−1
P = M 0 M N .

Similarly, the projection Q onto the subspace Im N along the sub-
space Im M is
−1 −1
Q= 0 N M N = N 0 N M .

If M T N = 0, then the projection matrices reduce to

P = M (M T M )−1 M T and Q = N (N T N )−1 N T .

J-spectral factorisation
J-spectral factorisation is deﬁned as

Λ(s) = W ∼ (s)JW (s),
where the J-spectral factor W (s) is bistable and Λ(s)
∼ . T
is a para-Hermitian matrix: Λ(s) = Λ (s) = Λ (−s).
Assume that Λ, having no poles or zeros on the jω-axis
including ∞, is realised as

Hp BΛ
Λ= = D + CΛ (sI − Hp )−1 BΛ (1)
CΛ D

and denote the A-matrix of Λ−1 as Hz , i.e.,
Hz = Hp − BΛ D−1 CΛ .

Triangular forms of Hp and Hz
Assume that a para-Hermitian matrix Λ as given in (1)
is minimal and has no poles or zeros on the jω-axis
including ∞. There always exist nonsingular matrices
∆p and ∆z (e.g. via Schur decomposition) such that

−1 ? 0
∆p Hp ∆p =
? A+

and
A− ?
∆−1 Hz ∆z
z = ,
0 ?
where A+ is antistable and A− is stable (A+ and A−
have the same dimension).
Note: No structural information of Hp and Hz is needed.

Factorisation with two matrices
Lemma Λ admits a Jp,q -spectral factorisation for some unique
Jp,q (where p is the number of the positive eigenvalues of D and
q is the number of the negative eigenvalues of D) iff

I 0
∆= ∆z ∆p
0 I

is nonsingular. If this condition is satisﬁed, then a J−spectral
factor is formulated as
   
I
 I 0 ∆−1 Hp ∆   I 0 ∆−1 BΛ 
 
 0 
W =

  ,
 (2)
 −∗ I 
 Jp,q DW CΛ ∆   DW 
0

∗
where DW is a nonsingular solution of DW Jp,q DW = D.


Factorisation with one common matrix
In general,
∆z = ∆p .
However, these two can be the same.

Theorem Λ admits a J-spectral factorisation if and
only if there exists a nonsingular matrix ∆ such that

Ap 0 z
A− ?
∆−1 Hp ∆ = −
p , ∆−1 Hz ∆ =
? A+ 0 Az
+

where Az and Ap are stable, and Az and A+ are an-
− − +
p

tistable. When this condition is satisﬁed, a J-spectral
factor W is given in (2).

The Delay-type Nehari problem

Given a minimal state-space realisation Gβ = −C B ,
A
0
which is not necessarily stable, and h ≥ 0, characterise
the optimal value

γopt = inf{ Gβ (s) + e−sh K(s) L∞
: K(s) ∈ H ∞ }

and for a given γ > γopt , parametrise the suboptimal
set of proper K ∈ H ∞ such that

Gβ (s) + e−sh K(s) L∞
< γ.


The optimal value
The optimal value γopt is
 
Lo
γopt ˆ
= max{γ : det Σ22 = 0}, ˆ
Σ22 = −Lc I Σ ,
I

where Lo and Lc are stabilising solutions, respectively, to
  
A γ −2 BB ∗ I
−Lc I    = 0,
0 −A∗ Lc

  
A 0 Lo
I −Lo    = 0.
−C ∗ C −A∗ I
   
Σ11 Σ12 . A γ −2 BB ∗
Σ=  = Σ(h) = exp(  h)
Σ21 Σ22 −C ∗ C −A∗

The structure of K

z' j' u
−sh '
@ '
@ K
e I
T

c
Gβ Z W −1 Q
T T

-
c
E j E
w y


1
Example: Gβ (s) = − s−a (a > 0)
1
0.9 aγopt
0.8
0.7
0.6

aγ
0.5
0.4
ˆ
Σ22 0.3
0.2

ah 0.1
0
0 1 2 3 4 5 6 7 8 9 10
aγ ah

ˆ
ˆ 22 with re- The contour Σ22 = 0 on the
The surface Σ
spect to ah and aγ ah-aγ plane
1
Since I −Lc Lo = 1−4a2 γ 2 , there is ΓGβ = 2a . As a
result, the optimal value γopt satisﬁes 0.5 ≤ aγopt ≤ 1.


∞
The standard H problem of
single-delay systems
Given a γ > 0, ﬁnd a proper controller K such that the
closed-loop system is internally stable and

Fl (P, Ke−sh) ∞
< γ.

'
z '
w
P u
'1
y e−sh I ' u

E K


Simplifying the problem
z'
u
@' 1
@ '
u
−sh
e I

Cr (P ) K
T
E
w y

z' @' 1
@ u @ 'z1
@
u
@' 1
@ '
u
−sh
e I

Cr (P ) Gα Cr (Gβ ) K
T
E E wE
1
w y y

Delay-free problem 1-block delay problem

Gα is the controller generator of the delay-free prob-
. −1
lem. Gβ is deﬁned such that Cr (Gβ ) = Gα . Gα and
Cr (Gβ ) are all bistable.

Solution to the problem
Solvability ⇐⇒ :
H0 ∈ dom(Ric) and X = Ric(H0 ) ≥ 0;
J0 ∈ dom(Ric) and Y = Ric(J0 ) ≥ 0;
ρ(XY ) < γ 2 ;
γ > γh , where γh = max{γ : det Σ22 = 0}.
u
' @'
@

 
c A + B2 C1 B2 − Σ12 Σ−1 C1 Σ−∗ B1
22
∗
22
−1 Q V −1 = C1 I 0 
Z V
−γ −2 B1 Σ∗ − C2 Σ∗
∗
21 22 0 I
T
-
E c
h E
y

Implementation of the controller
As seen above, the control laws associated with delay systems
normally include a distributed delay like
¢ h
v(t) = eAζ Bu(t − ζ)dζ,
0

or in the s-domain, Z(s) = (I − e−(sI−A)h ) · (sI − A)−1 .
The implementation of Z is not trivial because A
1
may be unstable. This problem had confused the 10

delay community for several years and was pro- 0
10

Approximation error
posed as an open problem in Automatica in 2003.
−1
N=1
It was reported that the quadrature implementation 10
might cause instability however accurate the imple- −2 N=5
10
mentation is.
−3
10 N=20
My investigation shows that: −4
10
The quadrature approximation error converges to 0 −2
10 10
−1
10
0
10
1
10
2
10
3

in the sense of H ∞ -norm. Frequency (rad/sec)


A trivial but signiﬁcant result
y(τ) y(t) p(t)

= ∗
1

t−h/N t τ 0 t 0 h/N t

¢ h ¢ t
N
y(t − τ )dτ = y(τ )dτ = y(t) ∗ p(t).
0 h
t− N

N −1 ¢ (i+1) h
¡h N
0 eAζ Bu(t − ζ)dζ = eAζ Bu(t − ζ)dζ
h
iN
i=0
N −1 ¢ h
(i+1) N
h
≈ eiA N B u(t − ζ)dζ
h
iN
i=0
N −1
h h
= eiA N Bu(t − i ) ∗ p(t)
i=0
N

Rational implementation
xN x N −1 x2 x1 ub
Π … Π Π Φ −1 B
u
vr … Π = ( sI − A + Φ ) −1 Φ

Π = (sI − A + Φ)−1 Φ,
¡ h
Φ=( N
0 e−Aζ dζ)−1 .


Unified Smith predictor (USP)
A numerical problem with the modified Smith predictor (MSP) is
identified. See the simple but a little bit extreme example
1 1
P (s) = + .
s + 1000 s − 1
The MSP is
e1000h − e−sh e−h − e−sh
ZMSP (s) = + .
s + 1000 s−1
According to the IEEE Standard 754, e1000h is regarded to be +∞
(INF) for h ≥ 0.71sec. This is not acceptable in practice.
A unified Smith predictor is proposed to fix this problem. An
equivalent structure of systems incorporating USP is derived and
then applied to solve various problems.

Feedback stabilisation of delay systems
The feedback stabilizability of the state–input delay
system
x(t) = A0 x(t) + A1 x(t − r) + P u(t) + P1 u(t − r)
˙

is equivalent to the condition

Rank (P + e−rλi P1 )∗ · ϕi = di , i = 1, 2, · · · , l.

where λi ∈ {λ1 , λ2 , · · · , λl } = {λ ∈ C : det ∆(λ) =
0 and Reλ ≥ 0} with ∆(λ) := λI − A0 − A1 e−rλ .
The dimension of Ker∆(λi )∗ is di and the basis of
Ker∆(λi )∗ is ϕi , ϕi , · · · , ϕi i for i = 1, 2, · · · , l .
1 2 d
Appeared in IEEE Trans. Automatic Control as a reg-
ular paper. Q.-C. Z HONG : A N OVERVIEW OF ACTIVITIES IN C ONTROL T HEORY & E NGINEERING – p. 21/77

Research activities in power
Focusing on power electronics & renewable energy

Voltage control of DC-AC converters
Neutral point generation
Grid-friendly inverters: Synchronverters
Regulation of induction generators for wind power
Control of wind turbines
Energy recovery from landing aircraft
Damping control of inter-area oscillations in power systems
DC and AC drives
AC Ward Leonard drive systems

DC-AC converters in the context
of distributed generation

DC
Local Diode link DC-AC grid
generator Rectifier Converter
Micro-
grid

Fuel cells
Photo-voltaic etc.
Gas turbines
Wind-mills etc.


Control problems involved
voltage control:
e = Vref − Vc as small as
possible
neutral point control: to
provide a non-drifting
neutral point
power control: to regulate
the active/reactive power
phase-locked loop (PLL):
to synchronise the con-
verter with the grid


Voltage control of DC-AC converters
The single-phase circuit:

The objective is to make sure that the output voltage
Vout or Vc is a clean sinusoidal signal even when the
load is nonlinear and/or the public grid is polluted with
harmonics. Q.-C. Z HONG : A N OVERVIEW OF ACTIVITIES IN C ONTROL T HEORY & E NGINEERING – p. 25/77

Structure of voltage controller

Techniques used:
H ∞ control
Repetitive control, where a delay is introduced
into the controller

Formulation of the H ∞ control problem


Nyquist plot of the system
−L(jω)
8

6

4

2
Im

0

−2

−4

−6

−8
−2 −1 0 1 2 3 4 5 6
Re

Simulation results
400 400
V (external) grid
c e 300
300
200 200
micro−grid

Voltage (V)
100
Voltage (v)

100
0 0

−100 −100

−200 −200

−300 −300

−400 −400
0 0.05 0.1 0.15 0.2 0.36 0.37 0.38 0.39 0.4
Time (sec) Time (sec)

(a) Transient response (b) Steady-state response


Experimental results
20

10

Voltage [V]
0

#1:1
#1:2
-10

-20
0.00 0.01 0.02 0.03 0.04 0.05
Time [sec]

(a) voltage and its reference
4
Voltage error [V]

2
#1:1

0

-2

-4

0.00 0.01 0.02 0.03 0.04 0.05
Time [sec]

(b) tracking error

Neutral-point control: Existing schemes

Split DC link

Conventional
neutral leg


Neutral-point control: Proposed scheme

Control objective: to force ic ≈ 0 so that the point N
will be the mid-point of DC supply.
No need to re-design the converter;
The controller is decoupled.

∞
H control design

This is a double-integrator system.


Experimental results

Vave
0.2V/div

iN
50A/div

iL
50A/div

ic
20A/div

0.17 0.18 0.19 0.20 0.21 0.22 0.23 0.24 0.25 0.26 0.27
Time (sec)


Grid-friendly inverters
Many strategies have been set to explore renewable en-
ergy sources, such as wind and solar power, to lead to
a low carbon economy. However, the increasing share
of the electricity generated from these sources (which
is often fed into the grid via inverters) could be a po-
tential threat to the overall stability of the future power
system when it reaches a certain level. Utility com-
panies would expect to minimise the impact of a large
number of grid-connected inverters on the power sys-
tem. Moreover, how to share load among these invert-
ers autonomously is also a problem.
Our Solution:
Synchronverters: Inverters that mimic synchronous generators


Synchronous generators
di
(θ = 0 )
v = −Rs i − Ls + e,
dt
Rotor field axis

Rs , L
˙sinθ−Mf dif cosθ,
e = Mf if θ
Rotation
dt
M M

N
Te = pMf if i, sinθ ,
Field voltage

Rs , L Rs , L

˙
Q = −θMf if i, cosθ ,
M

¨ ˙
J θ = Tm − Te − Dp θ.


The synchronverter
+
Circuit
Ls , R s va Lg , R g Breaker
ia vga
ea vb
VDC ib vgb
eb
vc
ec ic vgc

C
-

(a) The power part
Dp

-
Tm 1 θ& 1 θ
Js s
-
Te
Eqn. (7)
Q Eqn. (8)
Eqn. (9) e
Mf if i

(b) The Zelectronic part
Q.-C. :A O A
HONG N VERVIEW OF CTIVITIES IN C ONTROL T HEORY & E NGINEERING – p. 37/77

Experimental setup


Experimental results: I
Frequency (Hz)

P (W) and Q (Var)
s
d
d
P
Q

©

Time (Second) Time (Second)

(a) synchronverter (b) real power P and
frequency reactive power Q


Experimental results: II
y
ˆ
ˆ
Frequency (Hz)

P (W) and Q (Var)
P
Q
©

Time (Second) Time (Second)

(a) synchronverter (b) real power P and
frequency reactive power Q


Regulation of induction generators
for wind power

Q

P


Control of wind turbines

Patented by Nheolis, France, installed on the department’s rooftop
Experiments show that the new wind turbine is very efﬁcient. The
maximum mechanical power of a prototype with a 2m (diame-
ter) rotor reached 12kW at a wind speed of 20m/s. The nominal
power is 4.1kW at 14 m/s. A 1-meter 3-bladed prototype recorded
2.8kW mechanical power at 14 m/s. This is much more efﬁcient
than any commercial wind turbines available.

Buck Boost
Converter Converter


Energy recovery from landing aircraft

Aircraft
Risen slope to fall when
energy recovery is activated
Coils

Runway Magnets with alternative poles (N, S, N, …)


Voltage and current (zoomed)
6000
4000

Phase A voltage
2000
0
-2000
-4000
-6000
0 0.1 0.2 0.3 0.4 0.5

5
x 10
1
Phase A current

0.5

0

-0.5

-1
0 0.1 0.2 0.3 0.4 0.5
Time

800
600
d
400
200
0
6000
100
4000
Phase A voltage

2000 v 50
0 0
-2000
0
-4000
a -5
-6000
0 5 10 15 20 25 30
-10
7
x 10
2000 2

p 1
Phase A current

1000
0
7
0 x 10
10
-1000 E 5

-2000
0 5 10 15 20 25 30 0
0 5 10 15 20 25 30
Time
Time

(a) Phase current and (b) Distance, speed,
the generated voltage deceleration, power and
(phase) energy

Damping control of inter-area oscilla-
tions in large-scale power systems

TCSC: Thyristor Controlled Switched Capacitors

AC-DC converters: DC drives


AC-DC-AC converters: AC drives

Philips Semiconductors

VVVF speed control by:
using the PWM circuit HEF4752V shown above
using Intel 8051 microcomputer to generate space
vector PWM signal

Ward Leonard drive systems
Prime Load
mover

Constant Variable
speed speed

Controllable field Fixed field

Conventional (DC) Ward Leonard drive systems
Inverter

Variable
speed
Prime Load
VDC SG SM/IM
mover
Variable
speed

Fixed field

AC Ward Leonard drive systems

Exp. results: high-speed, no load

(a) speed (b) torque

(c) current (d) voltage

Exp. results: low-speed, no load

(a) speed (b) torque

(c) current (d) voltage

Other research activities
Rapid control prototyping
dSPACE
MICROGen
Texas Instruments kits
Embedded systems and control
Process control


Rapid control prototyping (RCP)
There are two sets of
dSPACE+Matlab/Simulink/SimPower in the lab.

Single-board PCI hardware for use in PCs
powerful development system for RCP
Real-Time Interface provides Simulink® blocks
for graphical conﬁguration of A/D, D/A, digital
I/O lines, incremental encoder interface and PWM
generation Q.-C. Z HONG : A N OVERVIEW OF ACTIVITIES IN C ONTROL T HEORY & E NGINEERING – p. 54/77

MicroGen

A universal electronic control unit with MPC555
built-in
Software-conﬁgurable I/O and signal
conditioning
Using industry standard SimuLink®
Enabling technology for RCP and HiL applica-
tions


Texas Instruments kits
TI has donated about 20 sets of different digital signal
controllers (including TMS320F28335) equipped with
the full version of latest Code Composer Studio 4.0.


Embedded systems & control
Different development kits for embedded control:
Wind River Workbench + Wind River Probe
Freescale MPC5567
Mathworks xPC target
EasyPIC4
dsPICPro2


Wind River
Support a wide range of processors
USB 2.0-compliant host connection
High-speed JTAG run control and
program download
Hot-plug-capable interconnect system
RTOS: VxWorks, Linux, and ThreadX
Built-in hardware diagnostics
Flash memory programming Wind River Probe

Source-level debugging
Support for Memory Management Units
Open API integration

Freescale
MPC5567

132 MHz PowerPC-based e200z6 core
a dual-channel FlexRay controller (10 Mbit/sec)
Fast Ethernet controller, 5 FlexCAN modules
40-channel dual analog-to-digital converter (ADC)
24-channel PWM
32-channel direct memory :access (DMA) controller E
Q.-C. Z
HONG A O
N A C
VERVIEW OFT &
CTIVITIES IN ONTROL HEORY NGINEERING – p. 59/77

Mathworks xPC target

Provide a high-performance host-target environment
Design a control system using Simulink® and Stateﬂow®
Generate code with Real-Time Workshop® and Stateﬂow
Coder™ and download the code to a target PC running the
xPC Target real-time kernel
Execute the code in real time on low-cost PC-compatible
hardware

EasyPIC4
3 in 1: Development, USB 2.0 programmer, ICD
Supports 8, 14, 18, 20, 28 and 40 pin PIC


dsPICPro2
Supports dsPIC in 64 and 80 pins package.
USB 2.0 programmer on board + A/D + D/A


Chemical process control (1992)

16 reactors, controlled by 3 industrial computers
Effective object code > 100 KB (Intel 8086 assembler)
Analogue control variables include pressure, temperature,
level, ﬂow and weight etc.

Integral processes with dead time
Integral process with dead-time (IPDT): G(s) = Gp (s)e−τ s = k e−τ s
s
Consider the disturbance observer-based control scheme (Zhong and Normey-Rico, 2001)
d
r
Ef E Ef
c u c
Ef E
y
E
d
C(s) Gp (s)e−τ s
T
− T
− r u c y
ˆ E CGm E h
E F (s) E h
E Gp (s)e−τ s E
d (1+CGm )F (s) Gm (1−Qe−τm s )
T
' c
− n
Gm (s) ' E e−τm s Ef' G−1 (s)
m
'
f −

' cn
'
'
Q(s) h
Q(s) F (s)
Disturbance Observer

(a) Disturbance observer-based control scheme (b) equivalent structure for implementation
where

k 1 (2λ + τm )s + 1 1
Gm (s) = , C(s) = , Q(s) = , F (s) =
s kT (λs + 1)2 λs + 1

and λ is a free design parameter.

1
Setpoint response: Gyr (s) =T s+1
e−τm s
Disturbance response: Gyd (s) = k 1 − Q(s)e−τm s
s
e−τm s
Measurement noise response: Gyn (s) = Q(s)e−τm s

Robust stability region

1.5

2
3
0.6 1

3
5
2.

2
6

2.5
0.5
0.4

1.5
5

1.5
4
0.2

2

0.2
1
β

1
0.5
∆k/k
3
0

2
0.2
2

0.5
1.5
1 −0.2

1.5
0
−0.4

1
−0.7 0.5
−0.5

1
−0.3 2 1
−0.1 1.5 1.5
0.1 1 −0.6
∆K/K 0.5 2
0.3 0 ∆τ/τ
1.5
0.5 −0.5
0.7 −1 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1
τ /τ
∆ m


Deadbeat response
Theorem The considered system rejects a step distur-
bance at t = T2 (T2 > T1 > 0) if Q(s) is chosen
as
q0 + q1 e−T1s + q2 e−T2s
Q(s) =
λs + 1
with 
eT2 /λ (λ+τm +T1 )−eT1 /λ (λ+τm +T2 )
 q0 =


 T2 −T1 +T1 eT2 /λ −T2 eT1 /λ



λ+τm +T2 −eT2 /λ (λ+τm )
q1 = T2 −T1 +T1 eT2 /λ −T2 eT1 /λ






 q = − λ+τm +T1 −eT1 /λ (λ+τm )
2 T −T +T eT2 /λ −T eT1 /λ
2 1 1 2

where λ > 0 is a free parameter.

Robustness indicator
2
i=0 |qi |
Point A J = λ
can be interpreted as a robustness indicator:
The lower the point A, the better the robustness.
In order to obtain the largest robust region for given T2 and λ, minimise the robust indicator:
2
|qi |
min J = min i=0
T1 T1 λ
1
where J can be re-written as
0.9

0.8

0.7
1 2(λ + τm )(eT2 /λ
− 1) − 2T2 0.6
J= 1+

T1/T2
λ T2 − T1 + T1 eT2 /λ − T2 eT1 /λ 0.5

0.4

0.3
Since 2(λ + τm )(eT2 /λ − 1) − 2T2 > 0 and
0.2
T2 − T1 + T1 eT2 /λ − T2 eT1 /λ > 0 for T2 > 0.1
1
T1 > 0 and λ > 0, J is always larger than λ . 0
0 1 2 3 4 5 6 7 8 9 10
Differentiate J with respect to T1 and let it be 0, T2/λ

then When T2 /λ → 0, T1 → 0.5T2 ; when
−1 + eT2 /λ − T2 eT1 /λ = 0
λ T2 /λ → ∞, T1 → T2 . Thus, T1 is always
Solve it, we have less than T2 , as expected.
T1 T2 /λ
T2
= λ
T2
ln e T /λ −1
2

Robustness indicator (cont.)
Denote
λ T2
α= and β =
τm τm
then the minimal cost is
 
1
1  2(1 + )(eβ/α
− 1) − 2β/α
α
Jo = 1+ 
β/α −1
ατm β/α + (eβ/α − 1) ln e β/α − 1

150

100

Τm Jo

50

0
1 1
2 2
3 3
T2 Τm 4 4
5 Λ Τm


Simulation example
Consider a process with
Control parameters:
1 T2 = 2τm = 10sec
Gm (s) = , τm = 5 sec,
s λ = 0.5τm = 2.5sec
T1 = 6.5sec
assume that the worst multiplicative uncer-
1
q0 = 2.36, q1 = −1.75, q2 = 0.39
tainty is ∆(s) = 0.1s+1 e−0.5s − 1.

(a) Nominal case (b) The worst case


Practical experiences
Software design
Intel 8086 assembly language:
> 100kB binary code
C language: > 10,000 lines
Database/Javascript
Hardware design
Micro-computers:
Intel 8051, Zilog Z80, Motorola ...
DC, AC drives etc
Lift control systems
System design experience

New-ACE: www.newace.org.uk
Leading a nation-wide collaborative network: New-ACE, which
is funded by a £88k EPSRC grant.

Partners: Imperial, Shefﬁeld, Loughborough and Queen’s
Belfast.
Advisory members: D.J.N. Limebeer (Imperial),
D.H. Owens (Shefﬁeld), R.M. Goodall (Loughborough),
G. Irwin (Queen’s Belfast), Q.H. Wu (Liverpool).
Main activities and outcomes:
to organise six workshops in subject areas including
renewable energy and control in power electronics
to submit 6~12 joint proposals in the coming three
years.

Objectives of the New-ACE
to provide a platform for the members to
exchange ideas, experience and practise
to develop and strengthen long-term collaboration
activities, including joint applications and
collaborations with industry
to support potential future leaders in control
engineering and related areas
to develop and sustain a strong future for control
engineering in the UK


Teaching
Philosophy:
Teaching and research help each other.
Quality teaching provides a constant ﬂow of ex-
cellent students for research. The best student of 2007, whose FYP
was directed by me, has been attracted to study for a PhD degree under my supervision.
He won both the principal Faculty undergraduate award and the IET Prize.

Modules taught this year:
Power electronics and electromechanics
Energy conversion and power systems
Digital control
Discrete-time signals and systems

Funding
Current projects:
Royal Academy of Engineering, £41k
EPSRC: EP/H004351/1, £112k
EPSRC: EP/H004424/1, £68k
EPSRC: EP/E055877/1, £88k
EPSRC: one DTA studentship
EPSRC and Add2: DHPA Award, £90k
ESPRC and Nheolis: DHPA Award, £90k
Completed projects:
EPSRC: EP/C005953/1, £126k

Research team
One part-time secretary
Currently 5 PhD students, one postdoctoral research fellow
and two Honorary Researchers
Another postdoc researcher and one PhD student to join
soon (funding already secured)
A former postdoctoral research fellow is still in active
collaboration.
Also closely working/worked with researchers from Brazil,
China, France, Italy, Israel, Netherlands, Singapore and
USA, in addition to those from the home department, the
Dept of Engineering and other UK universities and industry.


Future research topics

Renewable Energy:
• Wind power
• Solar power
• Other energy sources
Control Theory Power Electronics:
& Engineering • Grid-connected inverters
• Inverter-dominated power systems
• DC drives and AC drives
• Applications in power systems etc
Enabling Control Theory:
• Robust H∝ control
• Time-delay systems
• Grid monitoring, control and stability

Industrial collaboration to consolidate research
Theoretical research to deepen the depth of research

Vision
Closely working with colleagues, to develop the team
into an international key player in research and teach-
ing in control, power electronics and renewable en-
ergy, with long-term collaborations with industrial
partners and world-leading research groups.
Breadth of research: focusing on control theory,
power electronics and renewable energy;
developing activities in automotive electronics
and process control.
Depth of research: Looking for fundamental prob-
lems; providing signiﬁcant/simple solutions.

Recent developments in control, power electronics and renewable energy by Dr Zhong

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