Experimental Verification of Optimal Sliding Mode Controllers for Hoisting Cranes

Similar documents
Gain and Phase Margins Based Delay Dependent Stability Analysis of Two- Area LFC System with Communication Delays

Advanced D-Partitioning Analysis and its Comparison with the Kharitonov s Theorem Assessment

CHAPTER 4 DESIGN OF STATE FEEDBACK CONTROLLERS AND STATE OBSERVERS USING REDUCED ORDER MODEL

A Simple Approach to Synthesizing Naïve Quantized Control for Reference Tracking

Fractional-Order PI Speed Control of a Two-Mass Drive System with Elastic Coupling

Bogoliubov Transformation in Classical Mechanics

THE PARAMETERIZATION OF ALL TWO-DEGREES-OF-FREEDOM SEMISTRONGLY STABILIZING CONTROLLERS. Tatsuya Hoshikawa, Kou Yamada and Yuko Tatsumi

A Simplified Methodology for the Synthesis of Adaptive Flight Control Systems

Evolutionary Algorithms Based Fixed Order Robust Controller Design and Robustness Performance Analysis

ROBUST CONTROLLER DESIGN WITH HARD INPUT CONSTRAINTS. TIME DOMAIN APPROACH. Received August 2015; revised November 2015

USING NONLINEAR CONTROL ALGORITHMS TO IMPROVE THE QUALITY OF SHAKING TABLE TESTS

Stability regions in controller parameter space of DC motor speed control system with communication delays

CHEAP CONTROL PERFORMANCE LIMITATIONS OF INPUT CONSTRAINED LINEAR SYSTEMS

Determination of the local contrast of interference fringe patterns using continuous wavelet transform

Finding the location of switched capacitor banks in distribution systems based on wavelet transform

SERIES COMPENSATION: VOLTAGE COMPENSATION USING DVR (Lectures 41-48)

Robust Decentralized Design of H -based Frequency Stabilizer of SMES

LOW ORDER MIMO CONTROLLER DESIGN FOR AN ENGINE DISTURBANCE REJECTION PROBLEM. P.Dickinson, A.T.Shenton

The stabilization interval system of a tethered descent underwater vehicle

CONTROL SYSTEMS, ROBOTICS AND AUTOMATION Vol. VIII Decoupling Control - M. Fikar

Direct Torque Tracking PI-Controller Design for Switched Reluctance Motor Drive using Singular Perturbation Method

Simple Observer Based Synchronization of Lorenz System with Parametric Uncertainty

Control of Delayed Integrating Processes Using Two Feedback Controllers R MS Approach

Digital Control System

DIRECT TORQUE CONTROL OF THREE PHASE INDUCTION MOTOR USING FUZZY LOGIC SPEED CONTROLLER FOR STEADY/DYNAMIC STATE RESPONSE

SMALL-SIGNAL STABILITY ASSESSMENT OF THE EUROPEAN POWER SYSTEM BASED ON ADVANCED NEURAL NETWORK METHOD

Lecture 8. PID control. Industrial process control ( today) PID control. Insights about PID actions

Chapter 9: Controller design. Controller design. Controller design

Sliding Mode Control of a Dual-Fuel System Internal Combustion Engine

Hybrid Projective Dislocated Synchronization of Liu Chaotic System Based on Parameters Identification

The Influence of the Load Condition upon the Radial Distribution of Electromagnetic Vibration and Noise in a Three-Phase Squirrel-Cage Induction Motor

SENSORLESS DIRECT TORQUE CONTROL OF RUSHLESS AC MACHINE USING LUENBERGER OBSERVER

Sensorless speed control including zero speed of non salient PM synchronous drives

CHAPTER 8 OBSERVER BASED REDUCED ORDER CONTROLLER DESIGN FOR LARGE SCALE LINEAR DISCRETE-TIME CONTROL SYSTEMS

Massachusetts Institute of Technology Dynamics and Control II

Mechanics. Free rotational oscillations. LD Physics Leaflets P Measuring with a hand-held stop-clock. Oscillations Torsion pendulum

CONTROL OF INTEGRATING PROCESS WITH DEAD TIME USING AUTO-TUNING APPROACH

An estimation approach for autotuning of event-based PI control systems

A PLC BASED MIMO PID CONTROLLER FOR MULTIVARIABLE INDUSTRIAL PROCESSES

Direct Torque Control of Saturated Induction Machine with and without speed sensor

EXTENDED STABILITY MARGINS ON CONTROLLER DESIGN FOR NONLINEAR INPUT DELAY SYSTEMS. Otto J. Roesch, Hubert Roth, Asif Iqbal

Control Systems Engineering ( Chapter 7. Steady-State Errors ) Prof. Kwang-Chun Ho Tel: Fax:

ISSN: [Basnet* et al., 6(3): March, 2017] Impact Factor: 4.116

Dynamic Simulation of a Three-Phase Induction Motor Using Matlab Simulink

Analysis of Prevention of Induction Motors Stalling by Capacitor Switching

Simulation and Analysis of Linear Permanent Magnet Vernier Motors for Direct Drive Systems

Lecture 10 Filtering: Applied Concepts

Lecture 12 - Non-isolated DC-DC Buck Converter

Multi-dimensional Fuzzy Euler Approximation

Section Induction motor drives

The Measurement of DC Voltage Signal Using the UTI

A Sliding Mode Controller Design for Position Synchronization of Dual Spindle Servo Systems

March 18, 2014 Academic Year 2013/14

III.9. THE HYSTERESIS CYCLE OF FERROELECTRIC SUBSTANCES

Unified Design Method for Flexure and Debonding in FRP Retrofitted RC Beams

µ-analysis OF INDIRECT SELF CONTROL OF AN INDUCTION MACHINE Henrik Mosskull

A Comparative Study on Control Techniques of Non-square Matrix Distillation Column

Liquid cooling

Reliability Analysis of Embedded System with Different Modes of Failure Emphasizing Reboot Delay

Low Pass Filtering Based Artificial Neural Network Stator Flux Estimator for AC Induction Motors

Industrial Temperature PID Controller for Pb-Free Soldering Iron?

Direct Torque Control using Matrix Converters

Jump condition at the boundary between a porous catalyst and a homogeneous fluid

Question 1 Equivalent Circuits

An Approach to Design MIMO FO Controllers for Unstable Nonlinear Plants

GNSS Solutions: What is the carrier phase measurement? How is it generated in GNSS receivers? Simply put, the carrier phase

Self-Scheduled Control of a Gyroscope

LOAD FREQUENCY CONTROL OF MULTI AREA INTERCONNECTED SYSTEM WITH TCPS AND DIVERSE SOURCES OF POWER GENERATION

THE EXPERIMENTAL PERFORMANCE OF A NONLINEAR DYNAMIC VIBRATION ABSORBER

arxiv: v1 [cs.sy] 24 May 2018

Equivalent POG block schemes

The Hassenpflug Matrix Tensor Notation

White Rose Research Online URL for this paper: Version: Accepted Version

Adaptive Control of Level in Water Tank: Simulation Study

Throttle Actuator Swapping Modularity Design for Idle Speed Control

FUNDAMENTALS OF POWER SYSTEMS

A Constraint Propagation Algorithm for Determining the Stability Margin. The paper addresses the stability margin assessment for linear systems

Open Access Study of Direct Torque Control Scheme for Induction Motor Based on Torque Angle Closed-Loop Control. Xuande Ji *, Daqing He and Yunwang Ge

OBSERVER DESIGN FOR DISCRETE-TIME LINEAR SWITCHING SYSTEMS 1

Optimal Coordination of Samples in Business Surveys

An Improved Flux Observer for Sensorless Permanent Magnet Synchronous Motor Drives with Parameter Identification

Copyright 1967, by the author(s). All rights reserved.

THE IDENTIFICATION OF THE OPERATING REGIMES OF THE CONTROLLERS BY THE HELP OF THE PHASE TRAJECTORY

Then C pid (s) S h -stabilizes G(s) if and only if Ĉpid(ŝ) S 0 - stabilizes Ĝ(ŝ). For any ρ R +, an RCF of Ĉ pid (ŝ) is given by

A VIBRATION ISOLATION SYSTEM USING STIFFNESS VARIATION CAPABILITY OF ZERO-POWER CONTROL

into a discrete time function. Recall that the table of Laplace/z-transforms is constructed by (i) selecting to get

R. W. Erickson. Department of Electrical, Computer, and Energy Engineering University of Colorado, Boulder

What lies between Δx E, which represents the steam valve, and ΔP M, which is the mechanical power into the synchronous machine?

A RECONFIGURABLE MARS CONSTELLATION FOR RADIO OCCULTATION MEASUREMENTS AND NAVIGATION

Real-Time Identification of Sliding Friction Using LabVIEW FPGA

Bernoulli s equation may be developed as a special form of the momentum or energy equation.

Tuning of High-Power Antenna Resonances by Appropriately Reactive Sources

RaneNote BESSEL FILTER CROSSOVER

Assessment of Performance for Single Loop Control Systems

H DESIGN OF ROTOR FLUX ORIENTED CONTROLLED INDUCTION

Quantifying And Specifying The Dynamic Response Of Flowmeters

RECURSIVE LEAST SQUARES HARMONIC IDENTIFICATION IN ACTIVE POWER FILTERS. A. El Zawawi, K. H. Youssef, and O. A. Sebakhy

ABSTRACT- In this paper, a Shunt active power filter (SAPF) is developed without considering any harmonic detection

Annex-A: RTTOV9 Cloud validation

Finite Element Analysis of a Fiber Bragg Grating Accelerometer for Performance Optimization

Transcription:

Experimental Verification of Optimal Sliding Mode Controller for Hoiting Crane Alekandra Nowacka-Leverton Dariuz Pazderki Maciej Micha lek Andrzej Bartozewicz Intitute of Automatic Control, Technical Univerity of Lódź, Lódź, Poland, (e-mail: alekandra.nowacka-leverton@p.lodz.pl, andrzej.bartozewicz@p.lodz.pl) Poznań Univerity of Technology, Chair of Control and Sytem Engineering, Poznań, Poland, (e-mail: dariuz.pazderki@put.poznan.pl, maciej.michalek@put.poznan.pl) Abtract: In thi paper we propoe a robut liding mode control trategy for the poition control of a hoiting crane. The trategy employ a time-varying witching line which move with a contant jerk and a contant angle of inclination to the origin of the error tate pace. The line i elected optimally in the ene of IAE criterion, conidering contraint of the payload acceleration. Appropriate deign of the witching line reult in non-ocillatory convergence of the regulation error in the cloed-loop ytem and the acceleration contraint atified during the whole regulation proce. Theoretical conideration are verified by experimental tet conducted on a laboratory cale hoiting crane. Moreover, the tet are compared with the imilar experiment performed for two other method of the witching line deign. 1. INTRODUCTION Poition control of hoiting crane ha recently become an important reearch iue (Chang 27], Neupert et al. 21], Stergiopoulo et al. 29]). In practical application a reference et-point value of the crane payload ma hould be achieved monotonically (without overhoot or ocillation) and a fat a poible, however ubject to ome additional contraint. For example thee could be acceleration and/or velocity contraint impoed by a uer or reulting directly from drive limit. In practice, it i expected that thee deirable propertie will be achieved alo in the preence of model uncertaintie or external diturbance. An additional complexity of the problem reult from the fact that the upenion rope can exert only unidirectional force on the payload. It make the payload manipulation more difficult due to the neceity of maintaining poitive tenion in the upenion cable during the whole control proce. In thi paper we propoe to apply the liding mode control (Bartolini et al. 28], Bartozewicz et al. 28], De- Carlo et al. 1998], Hung et al. 1998] Or lowka-kowalka et al. 21], Utkin 1977]) to regulate the payload poition of hoiting crane. We introduce a control algorithm employing a time-varying witching line (Bartozewicz Thi work wa partly upported by the Polih State budget in the year 21 212 a a Reearch Project N N514 18638 Application of regulation theory method to the control of logitic procee. A. Nowacka-Leverton i a cholarhip holder of the project Innovative education without limit - integrated progre of the Technical Univerity of Lódź - univerity management, modern education, and employment upport and alo upport for the diabled upported by the European Social Fund. and Nowacka-Leverton 29], Bartozewicz and Nowacka- Leverton 21], Betin et al. 22], Tokat et al. 22]) which move with a contant jerk, and a contant angle of inclination to the origin of the error tate pace. The line i deigned in uch a way that the ytem repreentative point on the phae plane belong to it already at the initial time. Thi trategy allow u to avoid the reaching phae and yield robutne with repect to model uncertainty and external diturbance from the very beginning of the control proce. In order to enure good dynamic performance of a cloed-loop ytem, the line i optimally elected by minimizing the Integral of Abolute Error (IAE). The deign reult in monotonic error convergence without overhoot or ocillation. Moreover, during the line ynthei the acceleration contraint i directly conidered. The propoed algorithm i experimentally verified on a laboratory hoiting crane (which i commercially available from Inteco) and furthermore, it i compared with other two liding mode control method, preented in (Bartozewicz 1996]) and (Bartozewicz and Nowacka-Leverton 29]), where the line wa hifted with a contant velocity (Bartozewicz and Nowacka-Leverton 29]) and with a contant deceleration (Bartozewicz 1996]). The algorithm propoed in (Bartozewicz 1996]) and(bartozewiczandnowacka-leverton29])mightbe treated a pecial cae of the one hown here. Finally, the effect of replacing a dicontinuou term with it fractional approximation in practical verion of liding mode controller i formally analyzed uing the Lyapunov method. 2. SYSTEM MODEL In thi paper we take into account a hoiting crane whoe mechanical tructure i illutrated in Fig. 1. For thi Copyright by the International Federation of Automatic Control (IFAC) 116

We analyze the robut et-point regulation problem for ytem (5). The control objective i to achieve x 1d = cont., x 2d with uncertainty F determined above. We alo require non-ocillatory convergence of the regulation error e 1 = x 1 x 1d with imultaneou preervation of the payload acceleration contraint, i.e. we require that t ẋ 2 (t) A m. The maximum admiible value A m can reultdirectlyfromdrivelimitorcanbechoenarbitrarily by a uer treating it a additional deign parameter. Fig. 1. Mechanical tructure of a hoiting crane. ytem, from the d Alembert principle, we obtain the following three equation of motion J m q m +f m (q m, q m ) = τ m ητ d, (1) J d q d +f d (q d, q d ) = τ d τ g, (2) mẍ+f g (x,ẋ)+m g = τ g /r (3) wherewehave,repectively:q m,q d,x anangularpoition of the hoiting motor, an angular poition of the hoiting drum, and a linear poition of the payload; τ m,τ d,τ g motor driving torque, motor torque exerted on a drum haft, and dynamic load torque (reulting from the motion effect and tatic influence of the payload ma); J m,j d motor moment of inertia, and hoiting drum moment of inertia; m payload ma; f m,f d,f g function repreenting unmodeled dynamic of the hoiting motor, the hoiting drum, and the payload ma motion, repectively; g gravity acceleration; r hoiting drum radiu; η gear reduction ratio between the hoiting motor and the hoiting drum (η < 1). Auming that inductance of the armature winding i negligible the driving torque τ m may be expreed a a function of armature winding voltage τ m = k i i k i R (u k e q m ) (4) where i i an armature circuit current, u an armature voltage (phyically realized control input ignal), k i,k e machine contant, R an armature winding reitance. Combining equation (1)-(4), then uing linear relation between q m, q d and x (related to each other by the gear ratio η), and introducing the tate variable x 1 := x and x 2 := ẋ, one can obtain the following hoiting crane dynamic with the armature voltage a a control input ignal ẋ 1 = x 2, ẋ 2 = F(x 1,x 2 )+ 1 u, (5) α with F(x 1,x 2 ) repreenting the tructural uncertainty where F(x 1,x 2 ) = R k i α ( x1 ηf d r, x 2 r ) +f m ( x1 rη, x 2 rη ) + + ηrf g (x 1,x 2 )+ηrmg] β α x 2, (6) α = R rk i (J d η +J m η 1 +mr 2 η), β = k e rη. Motivatedbypoiblepracticaldifficultieinmodelingand identificationofcranedynamicweaumethatparameter α > i not known exactly, and that uncertainty i bounded, i.e. x1,x 2 F (x 1,x 2 ) < µ. 3. TIME VARYING SWITCHING LINE In thi ection the time-varying witching line i introduced and the optimal (in the ene of IAE criterion) parameter are derived. The line move with a contant jerk and a contant angle of inclination to the origin of the tate pace. At the time t f the line top moving and it remainfixedattheoriginwhichimpliethatthewitching line i decribed by the following equation = e 2 +ce 1 +(Dt 3 +Ct 2 +Bt+A) δ =, (7) where A, B, C, D R, and c > are deign parameter, e 1 = x 1 x 1d, e 2 = ė 1 = ẋ 1 = x 2, (8) are regulation error and it time derivative, and { 1 for t,tf ) δ = for t t f, ), (9) with t f > being the time intant when the line i topped.furthermore,itiaumedthatattheinitialtime t = e 1 () = e 1 and e 2 () =, and the witching of δ defined by (9) preerve continuity of line (7) at t f. Notice that when D = and C we obtain the cae of the witching line moving with a contant deceleration (Bartozewicz 1996]). On the other hand, for D = and C = the witching line move with a contant velocity (Bartozewicz and Nowacka-Leverton 29]). The experimental reult for thee two cae will be alo preented further in the paper. Firt of all, the witching line parameter are choen in uch a way that the ytem repreentative point belong to the line at the initial time t =. In thi way the inenitivity of the ytem with repect to model uncertainty from the very beginning of the control action i guaranteed. Therefore, the following condition hould be atified e 2 ()+ce 1 ()+A =. Taking into account initial condition e 1 () = e 1 and e 2 () =, we obtain A = ce 1. (1) Furthermore, the parameter are uppoed to enure the minimum value of the following control quality criterion J = e 1 dt (11) ubject to the acceleration contraint given by inequality ẋ 2 (t) A m. The procedure for finding the optimal witching line parameter begin with calculation of the regulation error and it derivative. For that purpoe firt we olve equation (7) with D, C for δ = 1, i.e. for the witching line moving with the contant jerk. Taking into account initial condition e 1 and e 2 = and relation (1), we obtain 117

e 1 = (Bc 2 2cC +6D)tc 3 (cc 3D)t 2 c 2 Dt 3 c 1 +( Bc 2 +2cC 6D)c 4 (e ct 1)+e 1, (12) e 2 = 3Dt 2 c 1 +( Bc 2 +2cC 6D)c 3 (e ct +1)+ (2cC 6D)tc 2. (13) A the line top moving at time t f, the following relation i atified Dt 3 f +Ct 2 f +Bt f +A =. (14) Furthermore,inordertoavoidrapidinputchange,theacceleration and the velocity of the introduced line hould be changed moothly. Thu, the following condition hould be fulfilled 6Dt f +2C =, (15) and 3Dt 2 f +2Ct f +B =. (16) Uing (1) and (14) (16), after ome calculation we obtain D = ce 1 t 3, (17) f C = 3ce 1 t 2, (18) f and conequently, t f = 3 e 1c B. (19) Having obtained relation (19), we calculate value of (12) and it derivative (13) for t = t f, which are initial condition neceary to olve equation (7) with D and C for δ =, i.e. when the line doe not move. After ome calculation, we obtain the evolution of the error for t t f e 1 = (Bc 2 2cC +6D)t f c 3 Dt 3 fc 1 + +( Bc 2 +2cC 6D)c 4 (e ct f 1)+ (cc 3D)t 2 fc 2 +e 1 ]e ct+ct f, (2) e 2 =(Bc 2 2cC +6D)t f c 2 +Dt 3 f + ( Bc 2 +2cC 6D)c 3 (e ct f 1)+ +(cc 3D)t 2 fc 1 ce 1 ]e ct+ct f. (21) Notice that the regulation error decribed by (12) and (2) converge to zero monotonically. Then, criterion (11) can be expreed a J = e 1 dt. (22) Subtituting (12) and (2) into (22) and calculating appropriate integral, we obtain J = e 1t f + e 1 c Bt2 f 2c Ct3 f 3c Dt4 f 4c. (23) Then, uing relation (17)-(19), we get J = e 1 + 3ce2 1 c 4 B. (24) Inordertofacilitatetheminimizationprocedure,wedefine the following poitive contant k = e 1c 2 B. (25) Then, calculating c from (25) we have Bk c =, (26) e 1 and criterion (24) can be expreed a ( ) J = e 1 3/2 1 + 3 k. (27) B k 4 Since the witching line election fully determine the ytemmotionanditperformance,witchinglineparameter A,B,C, D and c hould be carefully choen in accordance with the pecified requirement. In order to elect thee parameter we minimize (27) with the ytem acceleration contraint ẋ 2 (t) A m. Calculating the maximum value of ẋ 2 (t), we get max ẋ 2 (t) = B. (28) t Taking into account condition ẋ 2 (t) A m, we obtain the following inequality B A m. (29) Becaue criterion (27) decreae with increaing value of B, then the minimization of criterion J a a function of two variable (k,b) with the acceleration contraint may be replaced by the minimization of the following ingle variable function ( ) J(k) = e3/2 1 1 + 3 k (3) Am k 4 without any contraint. Calculating the derivative of function (3) ( dj(k) = e3/2 1 1 dk Am 2k k + 3 ) 8 (31) k and equating it to zero, we get that criterion (3) reache it minimum for k opt = 4 3. Then, B opt = A m gn(e 1 ). (32) The other witching line parameter can be calculated from A m c opt = 2 3 e 1, (33) Am e 1 A opt = 2 gn(e 1 ), (34) 3 C opt = A3/2 m 2 3 e 1 gn(e 1), (35) D opt = A2 m. (36) 36e 1 The line top moving at the time intant t fopt = 2 3 e 1 A m. (37) In thi way we deigned the optimal (in the ene of the IAE criterion) witching line. The derived parameter enure the minimization of IAE with the acceleration contraint. In the next ection we preent the control law deign. 4. CONTROL LAW A tated above, we propoe the liding mode approach with a time-varying witching line. In the previou ection 118

we elected the witching line in the optimal way. Now we analyze the control law utilizing the general form of thi witching line and we how robutne of the reultant cloed-loop ytem under uncertainty. The following theorem formulate a general control law which enure liding motion on the line defined by (7). Theorem 1. The following liding mode control law u = ˆαγgn()+ce 2 + ( 3Dt 2 +2Ct+B ) δ], γ > (38) with ˆα > being an etimate of parameter α, applied to ytem (5) with uncertainty (6), enure liding motion on witching line (7) for the ufficiently large coefficient γ. Proof: Calculating the time derivative of function V = 1 2 2 one obtain V =ė 2 +cė 1 + ( 3Dt 2 +2Ct+B ) δ] = =F + 1 α u+ce 2 + + ( 3Dt 2 +2Ct+B ) δ = F ˆα α γ + + ( 1 ˆα α) ce2 + ( 3Dt 2 +2Ct+B ) δ ] = =F ˆα α γ +f αcx 2 + ( 3Dt 2 +2Ct+B ) δ] ( F ˆα α γ) + f α cx 2 + ( 3Dt 2 +2Ct+B ) δ µ ˆα α γ + f α cx 2 + ( 3Dt 2 +2Ct+B ) δ ] κ, (39) where f α = 1 ˆα/α, and κ > i an arbitrarily mall contant. Inequality (39) i atified if where (x 2 ) = αˆα which end the proof. γ up x 2 (x 2 )], (4) µ+κ+ fα cx 2 + ( 3Dt 2 +2Ct+B ) δ ] (41) Remark 1: Notice that in practical application for any poitive value of ˆα one can find big enough value of parameter γ. Furthermore, a a reult of (38), in the deign procedure one hould rather conider proper election of product ˆα γ than election of ˆα and γ eparately. In order to avoid chattering, function gn() in equation (38)canbereplacedbyitcontinuouapproximationgiven by /( +ν), where ν > i a mall poitive deign coefficient. Then, the following theorem can be proved (in the imilar way to the proof of Theorem 1). Theorem 2. The following approximation û = ˆα γ +ν +ce 2 + ( 3Dt 2 +2Ct+B ) ] δ (42) of control input (38) with mall deign coefficient ν > and ˆα > being an etimate of parameter α, applied to ytem (5) with uncertainty (6) enure liding motion in the neighborhood of witching line (7) for the ufficiently large coefficient γ. Proof: Uing, in relation (39), modified control ignal (42) intead of (38) one get V = F + 1 αû+cx 2 + ( 3Dt 2 +2Ct+B ) ] δ f α cx2 + ( 3Dt 2 +2Ct+B ) δ ] + µ ˆα α γ +ν + F ˆα α γ 2 +ν ] + + f α cx 2 + ( 3Dt 2 +2Ct+B ) δ. (43) Auming now that γ atifie (4) with ˆ (x 2 ) = µ+ f α cx 2 + ( 3Dt 2 +2Ct+B ) δ. (44) one obtain the following upper bound V νˆ (x 2 ) +ν κ2 +ν = νˆ (x 2 ) κ. (45) +ν Then one can conclude that > 1 κ νˆ (x 2 ) V <. Thi concluion end the proof. The above conideration lead to the concluion that modified control (42) doe not bring the repreentative point of the ytem exactly onto the witching line but it force the repreentative point to the neighborhood of the line, with radiu ǫ = νˆ (x 2 ) κ >. (46) Remark 2: Note that modified input ignal (42) can be applied alo to the control algorithm with the witching line moving with either contant velocity (C =, D = ) or contant deceleration (C, D = ). Thi property will turn out to be ueful in the experimental tet. 5. EXPERIMENTAL RESULTS The liding mode control trategy propoed above ha been applied to a laboratory model of an indutrial hoiting crane commercially available from Inteco Ltd (www.inteco.com.pl). The mechanical tructure of the hoiting ubytem i compatible with the model illutrated in Fig. 1. The maximum control input magnitude i limited to u m = 24V. In the firt experiment E1 the applicationofapproximatedcontrol(42)withthewitchingline which move with a contant jerk (C and D ) ha beenverified.then,twoothermethodwereappliedtothe ytem. Experiment E2 employ the witching line moving with a contant deceleration (Bartozewicz 1996]) (C and D = ). In the third experiment E3 (correponding to C = and D = ) we take into account the witching line moving with a contant velocity (Bartozewicz and Nowacka-Leverton29]).Foreachexperimentthefollowing value of the deign parameter, initial condition, reference poition, and acceleration limit have been elected: ˆα = 3., γ = 5/3 and ν =.5, x 1 =.1m, x 1d =.5m, A m =.15m/ 2. For experiment E1 the optimal witching line ha been obtained by application of the ynthei preented in thi paper. For experiment E2, we ued the modification of the ynthei given in (Bartozewicz 1996]). Then, uing control law propoed in (Bartozewicz and Nowacka-Leverton 29]) appropriately modified for the conidered ytem, we obtained the reult of the experiment E3. The parameter of the witching line for each caeareummarizedintable1.figure2-5howreultof experiment E1, Figure 6-9 preent reult of experiment 119

Table 1. Optimal parameter for each experiment Parameter D C B A c t f ] E1.16.265.15.283.77 5.66 E2.187.15.3.75 4. E3.15.346.866 2.31 E2, and finally, Figure 1-13 illutrate experiment E3. From Fig. 2, Fig. 6 and Fig. 1 one can conclude that in all of the three cae poition error converge to ome mall vicinity of zero without overhoot. The time plot given in Figure 3, 7, 11 how the ytem velocity. The accelerationignal 1 illutratedinfigure4,8,12indicate that their value practically do not exceed aumed limit A m. A illutrated in Fig. 5, Fig. 9 and Fig. 13 liding variable i bounded but become different from zero, particularly during the firt tage of the control proce when the witching line i moving. Such behavior can be eaily explained by referring to the theoretical reult given by (46). Namely, the uncertainty term ˆ (x 2 ) increae along with a magnitude of payload velocity a a reult of vicou friction and electromagnetic damping phenomena, which implie higher value of ǫ. Comparing plot preented in the figure one can conclude that experimental reult correpond to the theoretical one(obtained byimulation)quite well.notethatdepite all of the inaccuracie and model uncertaintie the acceleration contraint ha not been violated (for any of tet E1, E2 and E3). Furthermore, experiment E1 confirm that the acceleration and the velocity of the payload ma changemoothly.finally,itiworthnotingthatparticular value of parameter ˆα in (42) i not critical in practical application it can be looely choen without ubtantial influence on the overall control performance. 6. CONCLUSION In thi paper a liding mode controller for the poition etpointregulationofahoitingcranehabeenpropoed.the preented controller employ the time-varying witching line moving toward the origin of the error tate pace. Matching the line poition with initial condition of the crane in a phae pace, eliminate the reaching phae and conequently enure robutne of the cloed-loop ytem from the very beginning of the control proce. The witching line i choen auming contant jerk of it motion. Utilization of the deigned controller guarantee monotonic poition error convergence with minimization of the IAE criterion, and imultaneou preervation of the acceleration contraint impoed by the uer on the cloedloop ytem dynamic. The control algorithm introduced in the paper ha been verified by experimental tet conducted on the laboratory model of an indutrial hoiting crane. REFERENCES G. Bartolini, L. Fridman, A. Piano and E. Uai (ed.). Modern Sliding Mode Control Theory. NewPerpective 1 Accelerationignalha beenetimatedbaed onthe poitionignal meaurement by numerical differentiation and low-pa filtering with the 4th-order Butterworth digital filter of 6 Hz paband. e1 m].1.1.2.3.4 t ] 2 4 6 8 1 Fig. 2. Poition error (experiment E1) e2 m/].1.5 t ] 2 4 6 8 1 Fig. 3. Velocity (experiment E1) â m/ 2 ].2.1.1.2 t ] 2 4 6 8 1 Fig. 4. Filtered acceleration (experiment E1).1.5.5 t ].1 2 4 6 8 1 Fig. 5. Sliding variable (experiment E1) and Application, Serie LNCIS, vol. 375, Springer- Verlag, 28. A. Bartozewicz. Time-varying liding mode for econdorder ytem. IEE Proceeding of Control Theory and Application, 143:455 462, 1996. A. Bartozewicz, O. Kaynak and V. Utkin (ed.). Sliding mode control in indutrial application. Special ection in IEEE Tranaction on Indutrial Electronic, 55: 385 413, 28. A. Bartozewicz and A. Nowacka-Leverton. Time-Varying Sliding Mode for Second and Third Order Sytem. Serie LNCIS, vol. 382, Springer-Verlag, 29. A. Bartozewicz and A. Nowacka-Leverton. ITAE optimal liding mode for third-order ytem with input ignal and tate contraint. IEEE Tranaction on Automatic Control, 55:1928 1932, 21. 111

.1.1 e1 m].1.2 e1 m].1.2.3.3.4 t ].4 t ] 2 4 6 8 1 Fig. 6. Poition error (experiment E2) 2 4 6 8 1 Fig. 1. Poition error (experiment E3).1.1 e2 m/].5 e2 m/].5 t ] t ] 2 4 6 8 1 Fig. 7. Velocity (experiment E2) 2 4 6 8 1 Fig. 11. Velocity (experiment E3).2.2.1.1 â m/ 2 ] â m/ 2 ].1.1.2 t ].2 t ] 2 4 6 8 1 Fig. 8. Filtered acceleration (experiment E2) 2 4 6 8 1 Fig. 12. Filtered acceleration (experiment E3).1.1.5.5.5 t ].1 2 4 6 8 1 Fig. 9. Sliding variable (experiment E2).5 t ].1 2 4 6 8 1 Fig. 13. Sliding variable (experiment E3) F. Betin, D. Pinchon, and G. Capolino. A time-varying liding urface for robut poition control of a DC motor drive. IEEE Tranaction on Indutrial Electronic, 49: 462 473, 22. C. Chang. Adaptive fuzzy controller of the overhead crane with nonlinear diturbance. IEEE Tranaction on Indutrial Informatic, 3:164 172, 27. R.S. DeCarlo, S. Zak and G. Mathew. Variable tructure control of nonlinear multivariable ytem: a tutorial. Proceeding of the IEEE, 76:212 232, 1988. J.Y. Hung, W. Gao and J.C. Hung. Variable tructure control: a urvey. IEEE Tranaction on Indutrial Electronic, 4:2 22, 1998. J. Neupert, E. Arnold, K. Schneider, and O.Sawodny. Trackingandanti-waycontrolforboomcrane. Control Engineering Practice, 18:31 44, 21. T. Or lowka-kowalka, M. Dybkowki and K. Szabat. Adaptive liding-mode neuro-fuzzy control of the twoma induction motor drive without mechanical enor. IEEE Tranaction on Indutrial Electronic 57:553 564, 21. J. Stergiopoulo, G. Kontantopoulo, A. Tze. Experimental verification of an adaptive input haping cheme for hoiting crane 17th Mediterranean Conference on Control and Automation, MED 9 73 735, 29. S. Tokat, I. Ekin, and M. Guzelkaya. A new deign method for liding mode controller uing a linear timevarying liding urface IMechE, 216:455 466, 22. V. Utkin. Variable tructure ytem with liding mode. IEEE Tran. on Automatic Control, 22:212 222, 1977. 1111