Slijeđenje reference s ograničenjima zasnovano na homotetičnim 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 slijeđenja reference s ograničenjima za nelinearne dinamičke sustave. Polazna je pretpostavka da postojeći zakon upravljanja zadovoljava ograničenja u pripadnom invarijantom skupu sustava. Uz takvu pretpostavku u radu se predlaže primjena homotetične transformacije invarijantnih skupova kako bi se izmjenio postojeći zakon upravljanja. Predloženi pristup se može primjeniti u sklopu modelskog prediktivnog upravljanja za slijeđenje reference ili samostalno za oblikovanje reference. Dani su simulacijski i eksperimentalni rezultati koji pokazuju primjenjivost predložene metode za klasu nelinearnih sustava

Primjena proširenog 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 između automobilskog kotača i podloge ima veliki značaj u sustavima sigurnosti kod suvremenih automobila kao što su sustav kontrole proklizavanja (ABS), sustav upravljanja vučnom silom (TC) i sl. U svrhu estimacije sile trenja u ovom radu se koristi prošireni Kalmanov filtar kojem je dodan integralni član. Predložen je postupak odabira optimalnog pojačanja integralnog člana. Predloženi estimator je uspoređen 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 proširenog 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 između automobilskog kotača i podloge ima veliki značaj u sustavima sigurnosti kod suvremenih automobila kao što su sustav kontrole proklizavanja (ABS), sustav upravljanja vučnom silom (TC) i sl. U svrhu estimacije sile trenja u ovom radu se koristi prošireni Kalmanov filtar kojem je dodan integralni član. Predložen je postupak odabira optimalnog pojačanja integralnog člana. Predloženi estimator je uspoređen 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 dinamičkih sustava s neodređenostima s posebnim naglaskom na dvije vrste neodređenosti: modelske i stohastičke. Dan je pregled najvažnijih postupaka procjene stanja sustava s neodređenostima. Predložen je postupak procjene stanja sustava s modelskim neodređenostima zasnovan na neuronskim mrežama koji je primjenjiv na široku klasu nelinearnih sustava. Za predloženi je procjenitelj stanja dokazana stabilnost i konvergencija procjene korištenjem Lyapunovljeve analize stabilnosti. Također su napravljena dodatna strukturna pojednostavljena procjenitelja stanja za dva posebna slučaja. Kvaliteta predloženog procjenitelja stanja sustava simulacijski je provjerena na primjeru procjene sile trenja između automobilskog kotača i podloge. Nadalje, u radu je predložen i višekriterijski postupak adaptacije broja čestica čestičnog filtra kojim se znatno smanjuju numerički i memorijski zahtjevi postupka procjene. Pored adaptacije broja čestica predloženi postupak omogućuje oporavak postupka procjene u slučajevima degeneracije samog postupka. Predloženi postupak adaptacije broja čestica filtra primijenjen je na rješavanje problema lokalizacije mobilnog robota. Kroz simulacijske i eksperimentalne provjere potvrđena je primjenjivost i robusnost predloženog postupka pri globalnoj lokalizaciji i rješavanju problema tzv. "otetog" robota. Prilikom izrade ovog rada korišteni 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 dinamičkih sustava s neodređenostima s posebnim naglaskom na dvije vrste neodređenosti: modelske i stohastičke. Dan je pregled najvažnijih postupaka procjene stanja sustava s neodređenostima. Predložen je postupak procjene stanja sustava s modelskim neodređenostima zasnovan na neuronskim mrežama koji je primjenjiv na široku klasu nelinearnih sustava. Za predloženi je procjenitelj stanja dokazana stabilnost i konvergencija procjene korištenjem Lyapunovljeve analize stabilnosti. Također su napravljena dodatna strukturna pojednostavljena procjenitelja stanja za dva posebna slučaja. Kvaliteta predloženog procjenitelja stanja sustava simulacijski je provjerena na primjeru procjene sile trenja između automobilskog kotača i podloge. Nadalje, u radu je predložen i višekriterijski postupak adaptacije broja čestica čestičnog filtra kojim se znatno smanjuju numerički i memorijski zahtjevi postupka procjene. Pored adaptacije broja čestica predloženi postupak omogućuje oporavak postupka procjene u slučajevima degeneracije samog postupka. Predloženi postupak adaptacije broja čestica filtra primijenjen je na rješavanje problema lokalizacije mobilnog robota. Kroz simulacijske i eksperimentalne provjere potvrđena je primjenjivost i robusnost predloženog postupka pri globalnoj lokalizaciji i rješavanju problema tzv. "otetog" robota. Prilikom izrade ovog rada korišteni 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 površini automobilskog kotača 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 proširenja LuGreovog modela kako bi dobio kompaktan model koji je valjan u širokom 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 korištenjem 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