The paper proposes a computationally efficient artificial neural network model for on-line system identification of nonlinear systems under the fuzzy closed-loop control system. The proposed backprogagation (BP) algorithm with adaptive learning rate (BPLAR) was tested for both off-line and on-line identification, comparing with traditional backpropagation learning algorithm on nonlinear ship model. The disadvantages of conventional BP algorithm are slower convergence and longer training times. The learning rate is adjusted at each iteration for the on-line weight and bias adaptation to reduce the training time. Simulation results indicate a superior convergence speed and better control performance in the case of adaptive BP method

In this paper, Pade's rational functions have been simulated for approximating several characteristic values of time delay regarding the plant time constant. Several representative plants were tested in order to show in which cases Pade’s function approximates time-delay block well. Only if the ratio of time delay versus time constant of the plant is rather great, or the plant contains emphasized numerator dynamics; approximation capabilities get poorer. The convergence rate of n-order Pade’s function has been also analyzed by using Taylor series and phase-frequency characteristics.

Ncural networks and fuzzy systcnis havc bccn applicd very succcssfiilly iii tlic idcntification and control of dynamic systcms. This papcr prcscnts combination of the fuzzy logic controllcr and ncural nctwork identification structurc, intcgra~cd into robotic systcm, to providc cxtcnsivc capabilitics. Wc first discuss tiic fuzzy logic controllcr (FLC), dcscribc its maiii compoticnts such as fuzzificr, fuzzy rule basc, fuzzy infcrcncc cnginc and dcfuzzilicr. Wc then lbcus on thc ncural nctwork (NN) plant modcl, traincd on-linc using tllc backpropagation training optimization algorithni with an adaptivc tcamjiig ntc. Thc optimization algorithm is pcrformcd at cadi satnplc time to computc thc optimat control input. The rcsults confirm thc cffcctivcricss o f the proposcd idcoti lication and control arcliitccturcs. Robotic manipulator systcrns arc notihncar, high couplcd, and timc varying. Robots havc to lac, many unccrtaintics in thcir dynamics, in particular structurcd unccrtaintics, which arc causcd by iniprccisioo in the manipulator link propcrlics. unknown loads, and unstructurcd otic, such as nonlincar friction, disturbanccs, and thc high-frcqucncy part of the dynaniics [ I]. Tlic coiitrol pcrforniancc of thc robotic manipulator is inlliicnccd by lhc mcntioncd unccrtaintics of the plant. A convcntional approach to solvc tlic robotic control problem is to iisc [tic coniputcd torqiic algorithm 121. Tlic computcd torque algorithm amounts to tranafomling tlic highly nonlincar robot dynamics into cquivalcnt Lincar systcm. Thcn lincar control tlicory can bc applicd to synthcsizc thc controllcr to mcct the dcsircd specifications. Thc theory of furzy and ncural control S C C ~ S to bc a suitablc tool for both modclling and control of coinpicx, nonlincar systcnis. Fusion fuzzy systcms and ncural nctwork providcs human-like knowlcdgc processing capabilitics. Thc using of FLC for controlling a robot manipulator is justificd liom Ihc followiiig rcasons: thc dynamics of robot is niodclcd by nonlincnr and couplcd diCfcrcntiaI cquations and FLC givcs high flcxibility, that is it lias many dcgrccs 01' liccdoin (shapc and number of mcnibcrship functions, aggregation mcthods, fuzzilkation and dcrtizzification mcthods, ctc.). Fuzzy systcms arc suitnblc for uncertain and approximatc rcasoning, cspccially for tlrc systcm with a miithcmntical iiiodcl that is diflicult to dcrivc [I], [3]-[ 5 ] .

In this paper a switching force/position control system of robot manipulator by using fuzzy logic is proposed. This system contains a fuzzy control system. The first aim of this paper is achievement of a good end-effector trajectory tracking performance when the manipulator moves in free space. The second is considering the system behavior when contact forces arise. The objective is to control the contact force and enable the robot actively accommodate the reactive force of environment instead of rejecting it. Performance of the force control schemes for different values of compliant environment are considered and analyzed.

In this paper a comparative analysis of Mamdani and Sugeno type fuzzy autopilots for ships is given. Both autopilots have two inputs: the heading signal and the yaw rate signal, and only one output: command rudder angle. Input variable fuzzification, fuzzy associative memory (FAM) rules, and output set defuzzification are described. The control system for the course-keeping composed of the nonlinear ship, the steering gear servo system, fuzzy autopilot and wave disturbance is described. Both autopilots are compared in three different sailing conditions: without wave disturbance, with wave disturbance and with wave disturbance and wave filtering. The notch filter was used for filtering of second-order wave disturbances. The influence of wave disturbance on course-keeping performance was analyzed. Even though the obtained performance in simulations are very similar, authors prefer Sugeno type fuzzy autopilot, due to less strain of the steering gear servo system and faster and more accurate heading obtained.