21 research outputs found
SlijeÄenje reference s ograniÄenjima zasnovano na homotetiÄnim skupovima
In this paper, we consider the problem of constrained tracking of piecewise constant references for nonlinear dynamical systems. In the considered problem we assume that an existing controller satisfies constraints in a corresponding positive-invariant set of the system. To solve the problem we propose the use of homothetic transformations of the positive-invariant set to modify the existing control law. The proposed approach can be implemented as a tracking model predictive control or as a reference governor. Simulation and experimental results are provided, showing the applicability of the proposed approach to a class of nonlinear systems.U radu se razmatra problem slijeÄenja reference s ograniÄenjima za nelinearne dinamiÄke sustave. Polazna je pretpostavka da postojeÄi zakon upravljanja zadovoljava ograniÄenja u pripadnom invarijantom skupu sustava. Uz takvu pretpostavku u radu se predlaže primjena homotetiÄne transformacije invarijantnih skupova kako bi se izmjenio postojeÄi zakon upravljanja. Predloženi pristup se može primjeniti u sklopu modelskog prediktivnog upravljanja za slijeÄenje reference ili samostalno za oblikovanje reference. Dani su simulacijski i eksperimentalni rezultati koji pokazuju primjenjivost predložene metode za klasu nelinearnih sustava
Primjena proŔirenog Kalmanovog filtra za estimaciju stanja na podlozi
High quality estimation of tire-road friction forces has important role in many automotive control systems like anti-lock brake systems (ABS), traction control systems etc. For this purpose an extended Kalman filter augmented with integral term has been employed. A procedure for selecting appropriate integral gain has been proposed. The proposed estimator has been compared to the well-known passivity based state estimator.Kvalitetna estimacija sile trenja izmeÄu automobilskog kotaÄa i podloge ima veliki znaÄaj u sustavima sigurnosti kod suvremenih automobila kao Å”to su sustav kontrole proklizavanja (ABS), sustav upravljanja vuÄnom silom (TC) i sl. U svrhu estimacije sile trenja u ovom radu se koristi proÅ”ireni Kalmanov filtar kojem je dodan integralni Älan. Predložen je postupak odabira optimalnog pojaÄanja integralnog Älana. Predloženi estimator je usporeÄen s estimatorom stanja na podlozi zasnovanim na teoriji pasivnosti
Constrained field-oriented control of permanent magnet synchronous machine with field-weakening utilizing a reference governor
This paper presents a complete solution for constrained control of a permanent magnet synchronous machine. It utilizes field-oriented control with proportional-integral current controllers tuned to obtain a fast transient response and zero steady-state error. To ensure constraint satisfaction in the steady state, a novel field-weakening algorithm which is robust to flux linkage uncertainty is introduced. Field weakening problem is formulated as an optimization problem which is solved online using projected fast gradient method. To ensure constraint satisfaction
during current transients, an additional device called current reference governor is added to the existing control loops. The constraint satisfaction is achieved by altering the reference signal. The reference governor is formulated as a simple optimization problem whose objective is to minimize the difference between the true reference and a modified one. The proposed method is implemented on Texas instruments F28343 200 MHz microcontroller and experimentally verified on a surface mounted permanent magnet synchronous machine
Primjena proŔirenog Kalmanovog filtra za estimaciju stanja na podlozi
High quality estimation of tire-road friction forces has important role in many automotive control systems like anti-lock brake systems (ABS), traction control systems etc. For this purpose an extended Kalman filter augmented with integral term has been employed. A procedure for selecting appropriate integral gain has been proposed. The proposed estimator has been compared to the well-known passivity based state estimator.Kvalitetna estimacija sile trenja izmeÄu automobilskog kotaÄa i podloge ima veliki znaÄaj u sustavima sigurnosti kod suvremenih automobila kao Å”to su sustav kontrole proklizavanja (ABS), sustav upravljanja vuÄnom silom (TC) i sl. U svrhu estimacije sile trenja u ovom radu se koristi proÅ”ireni Kalmanov filtar kojem je dodan integralni Älan. Predložen je postupak odabira optimalnog pojaÄanja integralnog Älana. Predloženi estimator je usporeÄen s estimatorom stanja na podlozi zasnovanim na teoriji pasivnosti
State estimation of nonlinear dynamic systems with uncertainties
U ovom je radu razmotren problem procjene stanja dinamiÄkih sustava s neodreÄenostima s posebnim naglaskom na dvije vrste neodreÄenosti: modelske i stohastiÄke. Dan je pregled najvažnijih postupaka procjene stanja sustava s neodreÄenostima. Predložen je postupak procjene stanja sustava s modelskim neodreÄenostima zasnovan na neuronskim mrežama koji je primjenjiv na Å”iroku klasu nelinearnih sustava. Za predloženi je procjenitelj stanja dokazana stabilnost i konvergencija procjene koriÅ”tenjem Lyapunovljeve analize stabilnosti. TakoÄer su napravljena dodatna strukturna pojednostavljena procjenitelja stanja za dva posebna sluÄaja. Kvaliteta predloženog procjenitelja stanja sustava simulacijski je provjerena na primjeru procjene sile trenja izmeÄu automobilskog kotaÄa i podloge. Nadalje, u radu je predložen i viÅ”ekriterijski postupak adaptacije broja Äestica ÄestiÄnog filtra kojim se znatno smanjuju numeriÄki i memorijski zahtjevi postupka procjene. Pored adaptacije broja Äestica predloženi postupak omoguÄuje oporavak postupka procjene u sluÄajevima degeneracije samog postupka. Predloženi postupak adaptacije broja Äestica filtra primijenjen je na rjeÅ”avanje problema lokalizacije mobilnog robota. Kroz simulacijske i eksperimentalne provjere potvrÄena je primjenjivost i robusnost predloženog postupka pri globalnoj lokalizaciji i rjeÅ”avanju problema tzv. "otetog" robota. Prilikom izrade ovog rada koriÅ”teni su Matlab/Simulink i Player/Stage razvojna okruženja.In this thesis the problem of state estimation of nonlinear dynamic systems with uncertainties is considered. Special attention is given to the two particular types of uncertainties: model and stochastic uncertainties. Overview of the existing approaches to the state estimation of nonlinear systems with uncertainties is presented. A novel approach is proposed for the state estimation based on neural networks. This approach is applicable to the wide class of nonlinear systems. Stability and convergence of the proposed neural network based state estimator is proven via Lyapunov stability analysis. Additional structural simplifications of the proposed estimator are given for two special cases of nonlinear systems. The quality of the proposed state estimation procedure is tested on the problem of tire/road friction force estimation. A new multi-criterion procedure is proposed for adaptation of the particle filter sample size. This new approach leads to the significant reduction in the particle filter numerical and memory requirements. The proposed adaptation procedure is used in the problem of mobile robot localization. The robustness and applicability of the adaptation procedure for both global localization and "kidnapped" robot problems is confirmed via simulation and experimental tests. The developed algorithms were tested within Matlab/Simulink and Player/Stage programming environments
State estimation of nonlinear dynamic systems with uncertainties
U ovom je radu razmotren problem procjene stanja dinamiÄkih sustava s neodreÄenostima s posebnim naglaskom na dvije vrste neodreÄenosti: modelske i stohastiÄke. Dan je pregled najvažnijih postupaka procjene stanja sustava s neodreÄenostima. Predložen je postupak procjene stanja sustava s modelskim neodreÄenostima zasnovan na neuronskim mrežama koji je primjenjiv na Å”iroku klasu nelinearnih sustava. Za predloženi je procjenitelj stanja dokazana stabilnost i konvergencija procjene koriÅ”tenjem Lyapunovljeve analize stabilnosti. TakoÄer su napravljena dodatna strukturna pojednostavljena procjenitelja stanja za dva posebna sluÄaja. Kvaliteta predloženog procjenitelja stanja sustava simulacijski je provjerena na primjeru procjene sile trenja izmeÄu automobilskog kotaÄa i podloge. Nadalje, u radu je predložen i viÅ”ekriterijski postupak adaptacije broja Äestica ÄestiÄnog filtra kojim se znatno smanjuju numeriÄki i memorijski zahtjevi postupka procjene. Pored adaptacije broja Äestica predloženi postupak omoguÄuje oporavak postupka procjene u sluÄajevima degeneracije samog postupka. Predloženi postupak adaptacije broja Äestica filtra primijenjen je na rjeÅ”avanje problema lokalizacije mobilnog robota. Kroz simulacijske i eksperimentalne provjere potvrÄena je primjenjivost i robusnost predloženog postupka pri globalnoj lokalizaciji i rjeÅ”avanju problema tzv. "otetog" robota. Prilikom izrade ovog rada koriÅ”teni su Matlab/Simulink i Player/Stage razvojna okruženja.In this thesis the problem of state estimation of nonlinear dynamic systems with uncertainties is considered. Special attention is given to the two particular types of uncertainties: model and stochastic uncertainties. Overview of the existing approaches to the state estimation of nonlinear systems with uncertainties is presented. A novel approach is proposed for the state estimation based on neural networks. This approach is applicable to the wide class of nonlinear systems. Stability and convergence of the proposed neural network based state estimator is proven via Lyapunov stability analysis. Additional structural simplifications of the proposed estimator are given for two special cases of nonlinear systems. The quality of the proposed state estimation procedure is tested on the problem of tire/road friction force estimation. A new multi-criterion procedure is proposed for adaptation of the particle filter sample size. This new approach leads to the significant reduction in the particle filter numerical and memory requirements. The proposed adaptation procedure is used in the problem of mobile robot localization. The robustness and applicability of the adaptation procedure for both global localization and "kidnapped" robot problems is confirmed via simulation and experimental tests. The developed algorithms were tested within Matlab/Simulink and Player/Stage programming environments
Modeliranje procesa i estimacija stanja u kontaktnoj povrÅ”ini automobilskog kotaÄa i podloge
U ovom radu su analizirane složene dinamicke pojave u kontaktu izmeu automobilskog kotaca i podloge. Dan je pregled postojecih modela trenja u kontaktu s posebnim naglaskom na dinamicke modele. Predložena su empirijska proŔirenja LuGreovog modela kako bi dobio kompaktan model koji je valjan u Ŕirokom rasponu vrijednosti normalne sile. Nadalje, dan pregled metoda estimacije sile trenja izmeu kotaca i podloge. Predložena su dva algoritma estimacije sile trenja. Prvi algoritam je zasnovan na modificiranom Kalmanovu filtru, dok drugi algoritam koristi neuronsku mrežu kao "univerzalni aproksimator" kako bi se osigurala robusnost estimacije. Pokazana je stabilnost za estimator zasnovan na neuronskoj mreži. Provjera predloženih metoda estimacije je provedena simulacijski na pojednostavljenom modelu automobilskog kotaca koriŔtenjem programskog paketa Matlab/Simulink.In this thesis, complex dynamic effects in tire-road contact surface are analyzed. An overview of existing tire-road friction models, with special attention given to dynamic models, is presented. Some empirical extensions of LuGre tire/road friction model are proposed, in order to obtain a compact model, that is valid in a wide range of normal forces. In addition, an overview of existing methods for tire-road friction force estimation is given. Two algorithms for for the tire/road friction estimation are proposed. The first algorithm is based on modified Kalman filter, while the second one represents a fully nonlinear estimator which uses a neural network as a "universal approximator", in order to ensure estimation robustness. A verification of the proposed estimation methods is performed on a simple one-wheel model using Matlab/Simulink