Output tracking control for class of fuzzy time-delay systems

Dimirovski, Georgi M. (Dogus Author)The output tracking control problem for fuzzy time-delay systems in presence of parameter perturbations has been solved via fuzzy T-S system models and variable-structure control approach. Following the reaching condition, a variable-structure fuzzy control method is proposed accordingly, when the time delay is known and available and when unknown and unavailable. The method guarantees the system operation arrives to the sliding surface in finite time interval and be kept there thereafter while tracking the desired trajectory. The sufficient condition for globally bounded state is derived by using the ISS theory and the LMI method. A simulation example demonstrates the validity and effectiveness of the proposed method.16th Triennial World Congress of International, Federation of Automatic Control, IFAC 200

Artificial Tune of Fuel Ratio: Design a Novel SISO Fuzzy Backstepping Adaptive Variable Structure Control

This paper examines single input single output (SISO) chattering free variable structure control (VSC) which controller coefficient is on-line tuned by fuzzy backstepping algorithm. VSC methodology is selected as a framework to construct the control law and address the stability and robustness of the close loop system based on Lyapunove formulation. The main goal is to guarantee acceptable fuel ratio result and adjust. The proposed approach effectively combines the design technique from variable structure controller is based on Lyapunov and fuzzy estimator to estimate the nonlinearity of undefined system dynamic in backstepping controller. The input represents the function between variable structure function, error and the rate of error. The outputs represent fuel ratio, respectively. The fuzzy backstepping methodology is on-line tune the variable structure function based on adaptive methodology. The performance of the SISO VSC which controller coefficient is on-line tuned by fuzzy backstepping algorithm (FBSAVSC) is validated through comparison with VSC and proposed method. Simulation results signify good performance of trajectory in presence of uncertainty torque load. DOI:http://dx.doi.org/10.11591/ijece.v3i2.209

Design of stable fuzzy controllers for an AGV

Fuzzy logic control is a relatively new technology and hence it needs rigorous comparative analyses with other well-established conventional control schemes. Further, fuzzy controller stability analysis is a major hindrance for its popularity among control engineers. This paper shows how stable fuzzy controllers may be synthesized for a typical AGV from the perspective of variable structure systems (VSS) theory. VSS or sliding model control (SMC) is an established robust non-linear control methodology. The AGV is characterized by highly non-linear, coupled and configuration dependent dynamics, with uncertainty in model parameters. Similarity in performance of the fuzzy controllers to the SMC controller is demonstrated through experimental results obtained for steer control of the AGV

USPOREDBA EFIKASNOSTI SLIJEĐENJA TRAJEKTORIJE ROBOTA PRI UPOTREBI RAZLIČITIH METODA NELINEARNOG UPRAVLJANJA

The Denavit-Hartenberg algorithm and the Lagrange-Euler method are used to derive realistic kinematics and dynamic models of a three-axis electric driven articulated planar robot with viscous, dynamic and static frictions. These robot models are further used for testing the following presented nonlinear robot control methods: fuzzy control, variable-structure control and model-reference variable-structure control. In the fuzzy-logic control method seven fuzzy sets are defined for two input variables. Triangular input membership functions and the 7x7 fuzzy rule table are chosen. The fuzzy controller output value is calculated according to the centre of gravity principle. The same fuzzy control algorithm is used in all robot servo control loops with a proper scaling of the linguistic variables. To eliminate the chattering of the variable-structure control signal and to reduce energy consumption, sign function in the original variable-structure control law is replaced with the following functions: a continuous, saturation and exponential function, all of them with a very thin boundary layer. The same modifications are also made in the original model-reference variable-structure control method. In all presented control methods controller parameters are chosen according to the principle of maximal allowed tracking error and a minimum of energy consumption. These control methods are tested by computer simulations in C programming language in the case of moving the tool of the chosen robot arm. The simulation results proved similar efficiencies of all mentioned modified nonlinear robot control methods, although modified variable structure control algorithms are the most suitable because of their simplicity and lower number of controller parameters.Denavit-Hartenbergov algoritam i Lagrange-Eulerova metoda upotrijebljeni su za izradu realnog kinematičkog i dinamičkog modela troosnog rotacijskog ravninskog robota s električnim motorima i viskoznim, dinamičkim i statičkim trenjem. Ti su modeli robota kasnije korišteni za provjeru sljedećih predstavljenih nelinearnih postupaka upravljanja robotom: neizrazitog upravljanja, upravljanja s promjenjivom strukturom te upravljanja s referentnim modelom i promjenjivom strukturom. U metodi upravljanja s neizrazitom logikom definirano je sedam neizrazitih skupova za dvije ulazne varijable. Izabrane su trokutaste ulazne funkcije pripadnosti i tablica neizrazitih pravila veličine 7 x 7. Vrijednost izlaza neizrazitog regulatora izračunata je po principu težišta neizrazitog skupa. Isti neizraziti upravljački algoritam upotrijebljen je u svim petljama slijednog upravljanja robotom, uz odgovarajuće skaliranje jezičnih varijabli. Za uklanjanje trešnje iz upravljačkog signala s promjenjivom strukturom i zbog smanjenja potrošnje energije, funkcija predznaka je u prvobitnom zakonu upravljanja s promjenjivom strukturom zamijenjena sljedećim funkcijama: neprekidnom, funkcijom zasićenja i eksponencijalnom funkcijom, s vrlo tankim graničnim slojem u svima. Iste su promjene također napravljene i u originalnoj metodi upravljanja s referentnim modelom i promjenjivom strukturom. U svim su predstavljenim postupcima upravljanja parametri regulatora izabrani po principu najveće dozvoljene pogreške slijeđenja i najmanje potrošnje energije. Ove su metode upravljanja provjerene računalnim simulacijama u programskom jeziku C na primjeru kretanja alata izabrane robotske ruke. Rezultati simulacija dokazali su sličnu efikasnost svih spomenutih promijenjenih nelinearnih postupaka upravljanja robotom, iako su modificirani upravljački algoritmi s promjenjivom strukturom najprimjenjiviji zbog svoje jednostavnosti i manjeg broja parametara regulatora

Independent modal variable structure fuzzy active vibration control of thin plates laminated with photostrictive actuators

AbstractPhotostrictive actuators can produce photodeformation strains under illumination of ultraviolet lights. They can realize non-contact micro-actuation and vibration control for elastic plate structures. Considering the switching actuation and nonlinear dynamic characteristics of photostrictive actuators, a variable structure fuzzy active control scheme is presented to control the light intensity applied to the actuators. Firstly, independent modal vibration control equations of photoelectric laminated plates are established based on modal analysis techniques. Then, the optimal light switching function is derived to increase the range of sliding modal area, and the light intensity self-adjusting fuzzy active controller is designed. Meanwhile, a continuous function is applied to replace a sign function to reduce the variable structure control (VSC) chattering. Finally, numerical simulation is carried out, and simulation results indicate that the proposed control strategy provides better performance and control effect to plate actuation and control than velocity feedback control, and suppresses vibration effectively

Fuzzy variable structure control for uncertain systems with disturbance

This paper focuses on the fuzzy variable structure control for uncertain systems with distrubance. Specifically, the fuzzy control is introduced to estimate the control disturbance, the switching control is included to compensate for the approximation error, and they possess the characteristic of simpleness in design and effectiveness in attenuating the control chattering. Some typical numerical examples are presented to demonstrate the effectiveness and advantage of the fuzzy variable structure controller proposed.Bo Wang, Peng Shi, Hamid Reza Karimi, Jun Wang and Yongduan Son

A comparative performance analysis based on artificial intelligence techniques applied to three-phase induction motor drives

In this work, we introduced a new robust hybrid control to an induction motor (IM), based on the theory of fuzzy logic and variable structure with sliding-mode control (SMC). As the variations of both control system parameters and operating conditions occur, the conventional control methods may not be satisfied further. Fuzzy tuning schemes are employed to improve control performance and to reduce chattering in the sliding mode. The combination of these two theories has given high performance and fast dynamic response with no overshoot. As it is very robust, it is insensitive to process parameters variation and external disturbances

Induction motor modelling using fuzzy logic

Fuzzy logic has been widely used in many engineering applications since this can overcome the limitations of conventional method of data analysis, modelling and system identification, and control system. The capability of dealing with highly non-linear system modelling that is so complex that require absolute analytical design make these mathematical model architecture more popular in the engineering field. This project is addressed on the modelling of induction motor Auto-Regressive with exogenous input (ARX) model structure using fuzzy logic. In this case fuzzy logic is combined with neural network of said Neuro Fuzzy (ANFIS) is applied and has functioned as estimator of the ARX model parameters. The ARX model of induction motor is estimated from its input output data. Input variable is voltage and output variable is speed. The experimental results show that the best model responses have similarly trend with the motor actual responses, final prediction error is 0.00873, loss function is 0.00807, and fit to working data is 67.22%. It means the model produce from system identification able adopt the motor dynamic and can use for replacing real motor for analysis and control design