recent developments in control, power electronics and renewable energy by dr zhong

An Overview of Activities in

CONTROL AND POWER

Qing-Chang [email protected]

Electrical Drives, Power and Control Group

Dept. of Electrical Eng. & Electronics

The University of Liverpool

Q.-C. ZHONG: AN OVERVIEW OF ACTIVITIES IN CONTROL THEORY & ENGINEERING – 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 controlOn the theoretical side, my research has been focus-ing on robust control, time-delay systems, processcontrol, and recently applying the theory of infinite-dimensional systems to time-delay systems. A seriesof problems have been solved:

Projections

J-spectral factorisation

Delay-type Nehari problem

StandardH∞ problem of single-delay systems

Realisation of distributed delays in controllers

Feedback stabilizability of linear systems withstate and input delays in Banach spaces


Major publications

IEEE Trans. Automatic Control: 7

Automatica: 4

OtherIEEE Transactions: 3

IET Control Theory & Applications: 4

One research monograph


ProjectionsFor a given nonsingular matrix partitioned as

[M N

], denote

the projection onto the subspace ImM along the subspace ImN

by P . Then, the projection matrixP is

P =[

M 0] [

M N

]−1

.

Similarly, the projectionQ onto the subspace ImN along the sub-

space ImM is

Q =[

0 N

] [M N

]−1

=[

N 0] [

N M

]−1

.

If MTN = 0, then the projection matrices reduce to

P = M(MTM)−1MT and Q = N(NTN)−1NT .


J-spectral factorisationJ-spectral factorisation is defined as

Λ(s) = W∼(s)JW (s),

where theJ-spectral factorW (s) is bistable andΛ(s)

is a para-Hermitian matrix:Λ(s) = Λ∼(s).= ΛT (−s).

Assume thatΛ, having no poles or zeros on thejω-axisincluding∞, is realised as

Λ =

[Hp BΛ

CΛ D

]= D + CΛ(sI − Hp)

−1BΛ (1)

and denote theA-matrix ofΛ−1asHz, i.e.,

Hz = Hp − BΛD−1CΛ.Q.-C. ZHONG: AN OVERVIEW OF ACTIVITIES IN CONTROL THEORY & ENGINEERING – p. 6/77

Triangular forms of Hp and Hz

Assume that a para-Hermitian matrixΛ as given in (1)is minimal and has no poles or zeros on thejω-axisincluding∞. There always exist nonsingular matrices∆p and∆z (e.g. via Schur decomposition) such that

∆−1p Hp∆p =

[? 0

? A+

]

and

∆−1z Hz∆z =

[A− ?

0 ?

],

whereA+ is antistable andA− is stable (A+ andA−

have the same dimension).Note: No structural information ofHp andHz is needed.


Factorisation with two matricesLemma Λ admits aJp,q-spectral factorisation for some unique

Jp,q (wherep is the number of the positive eigenvalues ofD and

q is the number of the negative eigenvalues ofD) iff

∆ =

[∆z

[I

0

]∆p

[0

I

] ]

is nonsingular. If this condition is satisfied, then aJ−spectralfactor is formulated as

W =

[I 0

]∆−1Hp∆

I

0

[I 0

]∆−1BΛ

Jp,qD−∗W CΛ∆

I

0

DW

, (2)

whereDW is a nonsingular solution ofD∗W Jp,qDW = D.


Factorisation with one common matrixIn general,

∆z 6= ∆p.

However, these two can be the same.

Theorem Λ admits aJ-spectral factorisation if andonly if there exists a nonsingular matrix∆ such that

∆−1Hp∆ =

[A

p− 0

? Ap+

], ∆−1Hz∆ =

[Az

− ?

0 Az+

]

whereAz− andA

p− are stable, andAz

+ andAp+ are an-

tistable. When this condition is satisfied, aJ-spectralfactorW is given in (2).


The Delay-type Nehari problem

Given a minimal state-space realisationGβ =[

A B

−C 0

],

which is not necessarily stable, andh ≥ 0, characterisethe optimal value

γopt = inf{∥∥Gβ(s) + e−shK(s)

∥∥L∞

: K(s) ∈ H∞}

and for a givenγ > γopt, parametrise the suboptimalset of properK ∈ H∞ such that

∥∥Gβ(s) + e−shK(s)∥∥

L∞< γ.


The optimal valueThe optimal valueγopt is

γopt = max{γ : det Σ22 = 0}, Σ22 =[−Lc I

]Σ

Lo

I

,

whereLo andLc are stabilising solutions, respectively, to

[−Lc I

] A γ−2BB∗

0 −A∗

I

Lc

= 0,

[I −Lo

] A 0

−C∗C −A∗

Lo

I

= 0.

Σ =

Σ11 Σ12

Σ21 Σ22

.

= Σ(h) = exp(

A γ−2BB∗

−C∗C −A∗

h)


The structure of K

j

Gβ Z

e−shI

W−1

j

Q

K@@�

--

u

y

z

w

�

-

��

6

6

6

?

?


Example: Gβ(s) = − 1s−a

(a > 0)

Σ22

ah

aγ

The surfaceΣ22 with re-spect toah andaγ

0 1 2 3 4 5 6 7 8 9 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

ah

aγ

aγopt

The contourΣ22 = 0 on theah-aγ plane

SinceI−LcLo = 1−4a2γ2, there is∥∥ΓGβ

∥∥ = 12a . As a

result, the optimal valueγopt satisfies0.5 ≤ aγopt ≤ 1.


The standard H∞ problem ofsingle-delay systemsGiven aγ > 0, find a proper controllerK such that theclosed-loop system is internally stable and

∥∥Fl(P, Ke−sh)∥∥∞

< γ.

P

e−shI

K

�

� �

�

y

z

u

w

u1

-


Simplifying the problem

Cr(P )

@@ e−shI

K

-

� � �

w

z u

y

u1

6

Cr(P )

@@

Gα

@@

Cr(Gβ)

@@ e−shI

K

Delay-free problem 1-block delay problem

-

�

-

�

-

� � �

w

z u

y

u1

6w1

z1

y

u1

Gα is the controller generator of the delay-free prob-lem. Gβ is defined such thatCr(Gβ)

.= G−1

α . Gα andCr(Gβ) are all bistable.


Solution to the problemSolvability⇐⇒ :

H0 ∈ dom(Ric) andX = Ric(H0) ≥ 0;

J0 ∈ dom(Ric) andY = Ric(J0) ≥ 0;

ρ(XY ) < γ2;

γ > γh, whereγh = max{γ : det Σ22 = 0}.

Z V −1

h

Q

@@

--

u

y-

��

6

?

?

V −1 =

A + B2C1 B2 − Σ12Σ−122 C∗

1 Σ−∗22 B1

C1 I 0

−γ−2B∗1Σ

∗21 − C2Σ

∗22 0 I


Implementation of the controllerAs seen above, the control laws associated with delay systems

normally include a distributed delay like

v(t) =

� h

0

eAζBu(t − ζ)dζ,

or in thes-domain, Z(s) = (I − e−(sI−A)h) · (sI − A)−1.The implementation ofZ is not trivial becauseA

may be unstable. This problem had confused the

delay community for several years and was pro-

posed as an open problem inAutomatica in 2003.

It was reported that the quadrature implementation

might cause instability however accurate the imple-

mentation is.

My investigation shows that:

The quadrature approximation error converges to0

in the sense ofH∞-norm.10

−210

−110

010

110

210

310

−4

10−3

10−2

10−1

100

101

Frequency (rad/sec)

N=1

N=5

N=20 A

ppro

xim

atio

n er

ror


A trivial but significant result

τ t t−h/N

y(τ)

t 0

y(t) p(t)

t

1

0 h/N

∗=

� hN

0y(t − τ)dτ =

� t

t− hN

y(τ)dτ = y(t) ∗ p(t).

� h0 eAζBu(t − ζ)dζ =

N−1∑

i=0

� (i+1) hN

i hN

eAζBu(t − ζ)dζ

≈

N−1∑

i=0

eiA hN B

� (i+1) hN

i hN

u(t − ζ)dζ

=

N−1∑

i=0

eiA hN Bu(t − i

h

N) ∗ p(t)


Rational implementation

1x2xΠ

Nx 1−Nx

B1−Φbu

u

rv

…

ΦΦ+−=Π −1)( AsI

Π Π

…

Π = (sI − A + Φ)−1Φ,

Φ = (

� hN

0 e−Aζdζ)−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

P (s) =1

s + 1000+

1

s − 1.

The MSP is

ZMSP(s) =e1000h − e−sh

s + 1000+

e−h − e−sh

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 predictoris proposed to fix this problem. An

equivalent structure of systems incorporating USP is derived and

then applied to solve various problems.Q.-C. ZHONG: AN OVERVIEW OF ACTIVITIES IN CONTROL THEORY & ENGINEERING – p. 20/77

Feedback stabilisation of delay systemsThe feedback stabilizability of the state–input delaysystem

x(t) = A0x(t) + A1x(t − r) + Pu(t) + P1u(t − r)

is equivalent to the condition

Rank[(P + e−rλiP1)

∗ · ϕi]

= di, i = 1, 2, · · · , l.

whereλi ∈ {λ1, λ2, · · · , λl} = {λ ∈ C : det ∆(λ) =

0 andReλ ≥ 0} with ∆(λ) := λI − A0 − A1e−rλ.

The dimension ofKer∆(λi)∗ is di and the basis of

Ker∆(λi)∗ is ϕi

1, ϕi2, · · · , ϕi

difor i = 1, 2, · · · , l .

Appeared in IEEE Trans. Automatic Control as a reg-ular paper. Q.-C. ZHONG: AN OVERVIEW OF ACTIVITIES IN CONTROL THEORY & ENGINEERING – p. 21/77

Research activities in powerFocusing 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 contextof distributed generation

Local generator

Diode

Rectifier

DC-AC

Converter

Micro-grid

grid DC link

Gas turbines Wind-mills etc.

Fuel cells Photo-voltaic 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 voltageVout or Vc is a clean sinusoidal signal even when theload is nonlinear and/or the public grid is polluted withharmonics. Q.-C. ZHONG: AN OVERVIEW OF ACTIVITIES IN CONTROL THEORY & ENGINEERING – p. 25/77

Structure of voltage controller

Techniques used:

H∞ control

Repetitive control, where a delay is introducedinto the controller


Formulation of the H∞ control problem


Nyquist plot of the system

−2 −1 0 1 2 3 4 5 6−8

−6

−4

−2

0

2

4

6

8−L(jω)

Re

Im


Simulation results

0 0.05 0.1 0.15 0.2−400

−300

−200

−100

0

100

200

300

400

Time (sec)

Vol

tage

(v)

Vc e

0.36 0.37 0.38 0.39 0.4−400

−300

−200

−100

0

100

200

300

400

Vol

tage

(V

) micro−grid

(external) grid

Time (sec)

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


Experimental results

-20

-10

0

10

20

Vo

lta

ge

[V

]

0.00 0.01 0.02 0.03 0.04 0.05

Time [sec]

#1:1

#1:2

(a) voltage and its reference

-4

-2

0

2

4

Vo

ltag

e e

rro

r [V

]

0.00 0.01 0.02 0.03 0.04 0.05

Time [sec]

#1:1

(b) tracking errorQ.-C. ZHONG: AN OVERVIEW OF ACTIVITIES IN CONTROL THEORY & ENGINEERING – p. 30/77

Neutral-point control: Existing schemes

Split DC link

Conventionalneutral leg


Neutral-point control: Proposed scheme

Control objective: to forceic ≈ 0 so that the pointNwill be the mid-point of DC supply.

No need to re-design the converter;

The controller is decoupled.Q.-C. ZHONG: AN OVERVIEW OF ACTIVITIES IN CONTROL THEORY & ENGINEERING – p. 32/77

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 invertersMany strategies have been set to explore renewable en-ergy sources, such as wind and solar power, to lead toa low carbon economy. However, the increasing shareof the electricity generated from these sources (whichis often fed into the grid via inverters) could be a po-tential threat to the overall stability of the future powersystem when it reaches a certain level. Utility com-panies would expect to minimise the impact of a largenumber 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

M

M M

Rs , L Rs , L

Rs , L

Rotor field axis

( 0=θ )

Field voltage

Rotation

N

v = −Rsi − Ls

di

dt+ e,

e = Mf if θsinθ−Mf

dif

dtcosθ,

Te = pMf if

⟨i, sinθ

⟩,

Q = −θMf if 〈i, cosθ〉 ,

Jθ = Tm − Te − Dpθ.


The synchronverter

+

-

Ls , Rs va

vb

vc

ia

ib

ic

ea

eb

ec

VDC

C

vga

vgb

vgc

Circuit Breaker

Lg , Rg

(a) The power part

Te Eqn. (7) Eqn. (8) Eqn. (9)

s

1

Dp

Tm

-

θ θ&

i

e

Mf if

Q

Js

1

-

(b) The electronic partQ.-C. ZHONG: AN OVERVIEW OF ACTIVITIES IN CONTROL THEORY & ENGINEERING – p. 37/77

Experimental setup


Experimental results: I

Time (Second)

Fre

quen

cy(H

z)

(a) synchronverterfrequency

Time (Second)

P(W

)an

dQ

(Var

)

P@@I

Q��

(b) real powerP andreactive powerQ


Experimental results: II

Time (Second)

Fre

quen

cy(H

z)

(a) synchronverterfrequency

Time (Second)

P(W

)an

dQ

(Var

)

PXXy

Q�

(b) real powerP andreactive powerQ


Regulation of induction generatorsfor wind power

Q

P


Control of wind turbines

Patented by Nheolis, France, installed on the department’srooftop

Experiments show that the new wind turbine is very efficient.Themaximum mechanical power of a prototype with a 2m (diame-ter) rotor reached 12kW at a wind speed of 20m/s. The nominalpower is 4.1kW at 14 m/s. A 1-meter 3-bladed prototype recorded2.8kW mechanical power at 14 m/s. This is much more efficientthan any commercial wind turbines available.


Buck Converter

Boost Converter


Energy recovery from landing aircraft

Coils

Risen slope to fall when energy recovery is activated

Aircraft

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


Voltage and current (zoomed)

0 0.1 0.2 0.3 0.4 0.5-6000

-4000

-2000

0

2000

4000

6000

Pha

se A

vol

tage

0 0.1 0.2 0.3 0.4 0.5-1

-0.5

0

0.5

1x 10

5

Time

Pha

se A

cur

rent


0 5 10 15 20 25 30-6000

-4000

-2000

0

2000

4000

6000P

hase

A v

olta

ge

0 5 10 15 20 25 30-2000

-1000

0

1000

2000

Time

Pha

se A

cur

rent

(a) Phase current andthe generated voltage

(phase)

0200400600800

d

0

50

100

v

-10

-5

0

a

0

1

2x 10

7

p

0 5 10 15 20 25 300

5

10x 10

7

Time

E

(b) Distance, speed,deceleration, power and

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 spacevector PWM signal


Ward Leonard drive systems

Constant speed

Variable speed

Controllable field Fixed field

Prime mover

Load

Conventional (DC) Ward Leonard drive systems

Variable speed

Variable speed

Fixed field

SM/IM Load

SG Prime mover VDC

Inverter

AC Ward Leonard drive systemsQ.-C. ZHONG: AN OVERVIEW OF ACTIVITIES IN CONTROL THEORY & ENGINEERING – p. 50/77

Exp. results: high-speed, no load

(a) speed (b) torque

(c) current (d) voltageQ.-C. ZHONG: AN OVERVIEW OF ACTIVITIES IN CONTROL THEORY & ENGINEERING – p. 51/77

Exp. results: low-speed, no load

(a) speed (b) torque

(c) current (d) voltageQ.-C. ZHONG: AN OVERVIEW OF ACTIVITIES IN CONTROL THEORY & ENGINEERING – p. 52/77

Other research activitiesRapid control prototyping

dSPACEMICROGenTexas 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® blocksfor graphical configuration of A/D, D/A, digitalI/O lines, incremental encoder interface and PWMgeneration


MicroGen

A universal electronic control unit with MPC555built-in

Software-configurable I/O and signalconditioning

Using industry standard SimuLink®

Enabling technology for RCP and HiL applica-tions


Texas Instruments kitsTI has donated about 20 sets of different digital signalcontrollers (including TMS320F28335) equipped withthe full version of latest Code Composer Studio 4.0.


Embedded systems & controlDifferent development kits for embedded control:

Wind River Workbench + Wind River Probe

Freescale MPC5567

Mathworks xPC target

EasyPIC4

dsPICPro2


Wind RiverSupport 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

Source-level debugging

Support for Memory Management Units

Open API integration

Wind River Probe


FreescaleMPC5567

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) controllerQ.-C. ZHONG: AN OVERVIEW OF ACTIVITIES IN CONTROL THEORY & ENGINEERING – 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


EasyPIC43 in 1: Development, USB 2.0 programmer, ICD

Supports 8, 14, 18, 20, 28 and 40 pin PIC


dsPICPro2Supports 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.Q.-C. ZHONG: AN OVERVIEW OF ACTIVITIES IN CONTROL THEORY & ENGINEERING – p. 63/77

Integral processes with dead timeIntegral process with dead-time (IPDT): G(s) = Gp(s)e−τs = k

se−τs

Consider the disturbance observer-based control scheme (Zhong and Normey-Rico, 2001)

f f f

f f

- C(s)

Gm(s) e−τms

Gp(s)e−τs

G−1m (s)

Q(s)

�

�

- ��- �

6 6

?

?

d

r u y

n

−−

−

Disturbance Observer

c

d

- - - --

h h

h

CGm(1+CGm)F (s)

F (s)

Gm(1−Qe−τms)Gp(s)e−τs

Q(s)F (s)

� �

6

?

?

d

r u y

n

−

- - - - --

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

where

Gm(s) =k

s, C(s) =

1

kT, Q(s) =

(2λ + τm)s + 1

(λs + 1)2, F (s) =

1

λs + 1

andλ is a free design parameter.

Setpoint response: Gyr(s) = 1T s+1

e−τms

Disturbance response: Gyd(s) = ks

(1 − Q(s)e−τms

)e−τms

Measurement noise response: Gyn(s) = Q(s)e−τms


Robust stability region

−1−0.5

00.5

11.5

2−0.7

−0.5−0.3

−0.10.1

0.30.5

0.7

0

1

2

3

4

5

6

β

∆K/K∆τ/τ

−1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

τ∆/τm

∆k/k

0.20.2

0.5

0.5

0.5

0.5

1

11

1

11

1.51.5

1.5

1.5

1.5

1.5

1.5

2

2

2

2

2

2.5

2.5

3 3


Deadbeat responseTheorem The considered system rejects a step distur-bance att = T2 (T2 > T1 > 0) if Q(s) is chosenas

Q(s) =q0 + q1e

−T1s + q2e−T2s

λs + 1

with

q0 = eT2/λ(λ+τm+T1)−eT1/λ(λ+τm+T2)

T2−T1+T1eT2/λ−T2e

T1/λ

q1 = λ+τm+T2−eT2/λ(λ+τm)


T1/λ

q2 = − λ+τm+T1−eT1/λ(λ+τm)


T1/λ

whereλ > 0 is a free parameter.Q.-C. ZHONG: AN OVERVIEW OF ACTIVITIES IN CONTROL THEORY & ENGINEERING – p. 66/77

Robustness indicatorPoint AJ =

∑2

i=0|qi|

λcan be interpreted asa robustness indicator:

The lower the point A, the better the robustness.

In order to obtain the largest robust region for givenT2 andλ, minimise the robust indicator:minT1

J = minT1

∑2i=0 |qi|

λ

whereJ can be re-written as

J =1

λ

(1 +

2(λ + τm)(eT2/λ − 1) − 2T2

T2 − T1 + T1eT2/λ − T2eT1/λ

)

Since2(λ + τm)(eT2/λ − 1) − 2T2 > 0 and

T2 − T1 + T1eT2/λ − T2eT1/λ > 0 for T2 >

T1 > 0 andλ > 0, J is always larger than1λ

.

DifferentiateJ with respect toT1 and let it be0,

then

−1 + eT2/λ − T2

λeT1/λ = 0

Solve it, we have

T1

T2= λ

T2ln eT2/λ−1

T2/λ

0 1 2 3 4 5 6 7 8 9 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

T2/λ

T 1/T2

When T2/λ → 0, T1 → 0.5T2; when

T2/λ → ∞, T1 → T2. Thus,T1 is always

less thanT2, as expected.


Robustness indicator (cont.)Denote

α =λ

τmandβ =

T2

τm

then the minimal cost is

Jo =1

ατm

1 +

2(1 + 1α

)(eβ/α − 1) − 2β/α

β/α + (eβ/α − 1)(

ln eβ/α−1β/α

− 1)

12

34

5 Λ�Τm �

12

34

5T2�Τm

0

50

100

150

ΤmJo

12

34Λ�Τ �

0

50

100


Simulation exampleConsider a process with

Gm(s) =1

s, τm = 5 sec,

assume that the worst multiplicative uncer-

tainty is∆(s) = 10.1s+1

e−0.5s − 1.

Control parameters:

T2 = 2τm = 10sec

λ = 0.5τm = 2.5sec

T1 = 6.5sec

q0 = 2.36, q1 = −1.75, q2 = 0.39

(a) Nominal case (b) The worst case


Practical experiencesSoftware design

Intel 8086 assembly language:> 100kB binary codeC language: > 10,000 linesDatabase/Javascript

Hardware design

Micro-computers:Intel 8051, Zilog Z80, Motorola ...DC, AC drives etcLift control systemsSystem design experience


New-ACE: www.newace.org.ukLeading a nation-wide collaborative network: New-ACE, which

is funded by a £88k EPSRC grant.

Partners:Imperial, Sheffield, LoughboroughandQueen’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.Q.-C. ZHONG: AN OVERVIEW OF ACTIVITIES IN CONTROL THEORY & ENGINEERING – p. 71/77

Objectives of the New-ACEto provide a platform for the members toexchange ideas, experience and practise

to develop and strengthen long-term collaborationactivities, including joint applications andcollaborations with industry

to support potential future leaders in controlengineering and related areas

to develop and sustain a strong future for controlengineering in the UK


TeachingPhilosophy:

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 systemsQ.-C. ZHONG: AN OVERVIEW OF ACTIVITIES IN CONTROL THEORY & ENGINEERING – p. 73/77

FundingCurrent 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, £126kQ.-C. ZHONG: AN OVERVIEW OF ACTIVITIES IN CONTROL THEORY & ENGINEERING – p. 74/77

Research teamOne 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

Control Theory & Engineering

Renewable Energy: • Wind power • Solar power • Other energy sources

Power Electronics: • 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 researchQ.-C. ZHONG: AN OVERVIEW OF ACTIVITIES IN CONTROL THEORY & ENGINEERING – p. 76/77

VisionClosely 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 electronicsand 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

Education