2DEVELOPMENT OF SHIP MANEUVERING CONTROL SYSTEMWITH ONLINE NONLINEAR OPTIMAL CONTROLINTRODUCTIONMOTIVATIONREVIEW OF THE OPTIMAL CONTROL PROBLEMOPTIMAL THRUST ALLOCATIONEXPERIMENTAL RESULTSROUTE TRACKI ID: 873868
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1 Non Linear DP ControllerMasanori HAMAMAT
Non Linear DP ControllerMasanori HAMAMATSUKawasaki Heavy Industries, Ltd.DYNAMIC POSITIONING CONFERENCE 2 DEVELOPMENT OF SHIP MANEUVERING CONTROL SYSTEMWITH ONLINE NONLINEAR OPT
2 IMAL CONTROL INTRODUCTION MOTIVATIONREVI
IMAL CONTROL INTRODUCTION MOTIVATIONREVIEW OF THE OPTIMAL CONTROL PROBLEMOPTIMAL THRUST ALLOCATION EXPERIMENTAL RESULTSROUTE TRACKING CONCLUSION 3 Motivation Requirements for S
3 hip Maneuvering Control System Online Op
hip Maneuvering Control System Online Optimization for Nonlinear ModelMinimize Fuel ConsumptionMinimize Environmental Effects ( Wind, Current, Waves, Auto-rectification to Thrus
4 ter Failure 4 Motivation CONVENTIONAL ST
ter Failure 4 Motivation CONVENTIONAL STUDYOffline calculation of tracking route by trial and errorShip is controlled to track the pre-calculated route Consideration of changing
5 disturbance and restricted conditions b
disturbance and restricted conditions by the Online OptimizationRealizable berthing assistant systemImpractical !Cannot deal with real disturbances, changing restricted conditi
6 ons Restricted Other ship GPS, DGPS, Au
ons Restricted Other ship GPS, DGPS, Automatic berthing system in harbor 5 Basic Structure of Ship Maneuvering Controller Online optimization Thrust allocator Online optimal co
7 ntroller Actuators ShipSensor signalsMan
ntroller Actuators ShipSensor signalsManual operation Nonlinear observer Ship maneuvering controllerWaypoint settingPosition, Velocity, Thrust commandsMultiple-thruster- allocat
8 ion problem 6 Kawasaki DPS KICS-5000 K
ion problem 6 Kawasaki DPS KICS-5000 Kawasaki DPSKICS-5000 Thrust allocator Online optimal controller Nonlinear observer HMI 7 Controlled object ( nonlinear model ) Finite T
9 ime Cost function Hamiltonian Euler-Lagr
ime Cost function Hamiltonian Euler-Lagrange equation (TPBVP) Online calculation (C/GMRES method) Dr. Otsuka(Osaka Univ.) Unknown variables Necessary Condition Fast algorithm
10 8 DEVELOPMENT OF SHIP MANEUVERING CONTR
8 DEVELOPMENT OF SHIP MANEUVERING CONTROL SYSTEMWITH ONLINE NONLINEAR OPTIMAL CONTROL MOTIVATIONREVIEW OF THE OPTIMAL CONTROL PROBLEMOPTIMAL THRUST ALLOCATION EXPERIMENTAL RES
11 ULTSROUTE TRACKING CONCLUSION 9 u2u
ULTSROUTE TRACKING CONCLUSION 9 u2u3u4u5u N 1(x) 0 x ; 0 0 x ;x )x( Thrust balance equations Thrust Allocation System with the Online Optimizati
12 onThrust commands (given by a feedback c
onThrust commands (given by a feedback controller) Actuators Nonlinear term Lateral force 10 1 (x) !1 u3u4u5u Propeller and rudder interaction xx )( Thrust Allocation S
13 ystem with the Online Optimization 11
ystem with the Online Optimization 11 Optimization Problem Formulation Hamiltonian Optimality condition 1u2u3u4u5u N Solve online by the C/GMRES method 12 u2u3u4u5u N A
14 lgorithm Verification Sinusoidal inputs
lgorithm Verification Sinusoidal inputs 13 5524431212121urururJiii Algorithm Verification Small Compensate 14 Online Thrust Allocation Algorithm Nonlinear thrust balance eq
15 uations Cost Function Time-varying weigh
uations Cost Function Time-varying weighting function Online nonlinear optimizationActuator commandsThrust commands (X,Y,N) Operator setting Actuators failure informationEnlarg
16 e failed actuators weightAllocate with
e failed actuators weightAllocate with remaining actuators 15 -4 -2 2 4 6 0 2 4 6 8 12 Y[m]X[m] -4 -2 0 2 4 6 0 2 4 6 8 10 12 Y[m] Conventional allocator Optimal thrust allocat
17 or Route tracking test with keeping 30 d
or Route tracking test with keeping 30 degree between the ships head and the moving direction. Effectiveness of Auto-rectificationto Thruster Failure Portside propeller Thrus
18 t allocation was re-calculated.Model shi
t allocation was re-calculated.Model ship 16 DEVELOPMENT OF SHIP MANEUVERING CONTROL SYSTEMWITH ONLINE NONLINEAR OPTIMAL CONTROL INTRODUCTION MOTIVATIONREVIEW OF THE OPTIMAL CON
19 TROL PROBLEMOPTIMAL THRUST ALLOCATION E
TROL PROBLEMOPTIMAL THRUST ALLOCATION EXPERIMENTAL RESULTSROUTE TRACKING EXPERIMENTAL RESULTSCOCLUSION 17 Conventional tracking control Online nonlinear optimal control Straig
20 ht(Guide line)Waypoint(WP) Move on the o
ht(Guide line)Waypoint(WP) Move on the optimal route to the next WPCurrent position Comparison with Conventional Controller and Route Optimizing ControllerCurve 18 iyix y v XYfL
21 HTXrvyuxf State
HTXrvyuxf State Variables Hamiltonian Constrained condition (if any, user definition) 19 XrTxKxXTxmNNrruxMm
22 YYrruMmrmNNrruxMmYYrruMmvumxMXXmxMrxMmxM
YYrruMmrmNNrruxMmYYrruMmvumxMXXmxMrxMmxMrvMvuXrvyuxfHGHHGHHG1det/)()(det/)()(cossin)/()()(/)/(sincos223223332DDDDDDD State Equations Cross Flow Model (KARASUNO Model) 20
23 TttiiirefiTtiiireffitd
TttiiirefiTtiiireffitdurxxqxxsJ7131227122121fLHTCost function Hamiltonian Cost Function State variables Thrusters NrYrXr(Equivalent to power minimum) 21
24 Specified position WPiyi
Specified position WPiyi TttiiirefiTtiiireffitdurxxqxxsJ7131227122121Xrvyux (multiplied to the lateral deviation) Route Tracking Results (multiplied to
25 the heading deviation) 22 Configuration
the heading deviation) 22 Configuration of Experimental Equipment GyroJoystickWireless LAN(Automatic Tracking Light Rangefinder) 23 (weight (weight Example of model test resu
26 ltThruster powers are small. 24 (weigh
ltThruster powers are small. 24 (weight (weight Example of model test result 25 (weight (weight Example of model test result 26 (weight Strict(weight Strict Example of m
27 odel test result 27 2 4 6 8 "0 "2 "4 "6
odel test result 27 2 4 6 8 "0 "2 "4 "6 -"0 -6 -4 -2 2 4 x [m]y [m] WP Example of simulation resultRight-angled Tracking Pass on the WP1 at a Constant Speed y (weight (weight
28 (weight 28 2 4 6 8 "0 "2 "4 "6 -"0 -6
(weight 28 2 4 6 8 "0 "2 "4 "6 -"0 -6 -4 -2 2 4 x [m]y [m] Example of simulation resultRight-angled Tracking (Exact Tracking) y (weight (weight (weight 29 5 "0 "5 25 "0 5 "
29 0 x [m]y [m] Example of simulation resul![0
x [m]y [m]
Example of simulation resul](873868/0-x-m-y-m-example-of-simulation-resultst.jpg "0
x [m]y [m]
Example of simulation resul")
0 x [m]y [m] Example of simulation resultStraight Line Tracking (Gust Case) GUST Optimal heading angle 30 Example of model test result 31 WP0WP1WP2WP3WP4WP5 Example of model tes
30 t resultComplicated berthing test with s
t resultComplicated berthing test with six waypoints 32 Nonlinear Observer (Dead Reckoning Function) Thrust allocator Online optimal controller Actuators ShipSensor signals Nonl
31 inear observer Ship maneuvering controll
inear observer Ship maneuvering controllerWaypoint settingPosition, Velocity, Current, Unknown force, Thrust commands Ship & Actuators Nonlinear ModelsShips Motion ModelingThru
32 ster Dynamics Ships Motion Modeling Thr
ster Dynamics Ships Motion Modeling Thruster Dynamics Actuator signals 33 Position sensing signals lost DesiredRoute Dead Reckoning Control Nonlinear Observer (Dead Reckoning F
33 unction) 34 Kawasaki DPS KICS-5000Requ
unction) 34 Kawasaki DPS KICS-5000Requirements for Ship Maneuvering Control System Online Optimization for Nonlinear ModelMinimize Fuel ConsumptionMinimize Environmental Effec
34 ts ( Wind, Current, Waves, )Auto-rectif
ts ( Wind, Current, Waves, )Auto-rectification to Thruster FailureEasy and Quick Route PlanningRealizable berthing assistant systemAutomatic optimal maneuvering and positioning