Adaptive neural complementary sliding-mode control via functional-linked wavelet neural network

[[abstract]]Chaos control can be applied in the vast areas of physics and engineering systems, but the parameters of chaotic system are inevitably perturbed by external inartificial factors and cannot be exactly known. This paper proposes an adaptive neural complementary sliding-mode control (ANCSC) system, which is composed of a neural controller and a robust compensator, for a chaotic system. The neural controller uses a functional-linked wavelet neural network (FWNN) to approximate an ideal complementary sliding-mode controller. Since the output weights of FWNN are equipped with a functional-linked type form, the FWNN offers good learning accuracy. The robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Without requiring preliminary offline learning, the parameter learning algorithm can online tune the controller parameters of the proposed ANCSC system to ensure system stable. Finally, it shows by the simulation results that favorable control performance can be achieved for a chaotic system by the proposed ANCSC scheme.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Adaptivno slijedno upravljanje u kliznom režimu rada s valićnom neuronskom mrežom za sustav s PMSM elektromotornim pogonom

This paper presents a wavelet neural network backstepping sliding mode controller (WNNBSSM) for permanent-magnet synchronous motor (PMSM) position servo control system. Backstepping sliding mode (BSSM) is utilized to guarantee favorable tracking performance and stability of the whole system, meanwhile, wavelet neural network (WNN) is used for approximating nonlinear uncertainties. The designed controller combined the merits of the backstepping sliding mode control with robust characteristics and the WNN owning the capability of artificial neural networks for online learning and the capability of wavelet decomposition for identification. An observed error compensator is developed to compensate the estimated error of the WNN and the adaptive law is derived according to Lyapunov theorem. The effectiveness of the proposed controller is investigated in simulation under different operating conditions. The simulation results demonstrate the proposed WNNBSSM controller can provide precise tracking performance and robust characteristics despite unknown parameter uncertainties and load disturbance. Moreover, an implemental wavemaker system is established to verify the effectiveness of the proposed control algorithm.U radu je predstavljen bacsktepping regulator u kliznom režimu rada za sustav upravljanja servo pozicioniranja motoromom s permanentnim magnetima (PMSM) zasnovan na valićnim neuronskim mrežama (WNNBSSM). Backstepping klizni režim rada (BSSM) korišten je za jamčenje željenih performansi slijeđenja i stabilnost cijelog sustava, dok je valićna neuronska mreža (WNN) korištena za aproksimaciju nelinearnih nesigurnosti. Sintetizirani regulator povezuje prednosti i robusne karakteristike backstepping kliznog režima upravljanja te WNN sa sposobnostima umjetnih neuronskih mreža za online učenje i mogućnost valićne dekompozicije za identifikaciju. Razvijen je kompenzator pogreške estimacije WNN i adaptivni upravljački zakon prema Ljapunovljevom teoremu. Djelotvornost predloženog regulatora istražena je kroz simulacije u različitim uvjetima rada. Rezultati simulacija pokazuju da predloženi WNNBSSM može osigurati precizna svojstva slijeđenja i robusne karakteristike unatoč nepoznatim nesigurnostima parametara i poremećaja tereta. Uz to, razvijen je izvedbeni sustav generatora valova za provjeru djelotvornosti predloženog upravljačkog algoritma

Adaptive PI Hermite neural control for MIMO uncertain nonlinear systems

[[abstract]]This paper presents an adaptive PI Hermite neural control (APIHNC) system for multi-input multi-output (MIMO) uncertain nonlinear systems. The proposed APIHNC system is composed of a neural controller and a robust compensator. The neural controller uses a three-layer Hermite neural network (HNN) to online mimic an ideal controller and the robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Moreover, a proportional–integral learning algorithm is derived to speed up the convergence of the tracking error. Finally, the proposed APIHNC system is applied to an inverted double pendulums and a two-link robotic manipulator. Simulation results verify that the proposed APIHNC system can achieve high-precision tracking performance. It should be emphasized that the proposed APIHNC system is clearly and easily used for real-time applications.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Intelligent nonsingular terminal sliding-mode control via perturbed fuzzy neural network

[[abstract]]In this paper, an intelligent nonsingular terminal sliding-mode control (INTSMC) system, which is composed of a terminal neural controller and a robust compensator, is proposed for an unknown nonlinear system. The terminal neural controller including a perturbed fuzzy neural network (PFNN) is the main controller and the robust compensator is designed to eliminate the effect of the approximation error introduced by the PFNN upon the system stability. The PFNN is used to approximate an unknown nonlinear term of the system dynamics and perturbed asymmetric membership functions are used to handle rule uncertainties when it is hard to exactly determine the grade of membership functions. In additional, Lyapunov stability theory is used to discuss the parameter learning and system stability of the INTSMC system. Finally, the proposed INTSMC system is applied to an inverted pendulum and a voice coil motor actuator. The simulation and experimental results show that the proposed INTSMC system can achieve favorable tracking performance and is robust against parameter variations in the plant

Adaptive TSK-type self-evolving neural control for unknown nonlinear systems

[[abstract]]In this paper, a real-time approximator using a TSK-type self-evolving neural network (TSNN) is studied. The learning algorithm of the proposed TSNN not only automatically online generates and prunes the hidden neurons but also online adjusts the network parameters.[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20120918~20120922[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Japan,Toky

Adaptive TSK-type self-evolving neural control for unknown nonlinear systems

[[abstract]]In this paper, a real-time approximator using a TSK-type self-evolving neural network (TSNN) is studied. The learning algorithm of the proposed TSNN not only automatically online generates and prunes the hidden neurons but also online adjusts the network parameters.[[conferencetype]]國際[[conferencedate]]20120918~20120921[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tokyo, Japa

Neural network-based compensation control of mobile robots with partially known structure

This study proposes an inverse non-linear controller combined with an adaptive neural network proportional integral (PI) sliding mode using an on-line learning algorithm. The neural network acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations on their dynamics and kinematics. Also, the proposed controller can reduce the steady-state error of a non-linear inverse controller using the on-line adaptive technique based on Lyapunov’s theory. Experimental results show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.Fil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Multiobjective Trajectory Optimization and Adaptive Backstepping Control for Rubber Unstacking Robot Based on RFWNN Method

Multiobjective trajectory optimization and adaptive backstepping control method based on recursive fuzzy wavelet neural network (RFWNN) are proposed to solve the problem of dynamic modeling uncertainties and strong external disturbance of the rubber unstacking robot during recycling process. First, according to the rubber viscoelastic properties, the Hunt-Crossley nonlinear model is used to construct the robot dynamics model. Then, combined with the dynamic model and the recycling process characteristics, the multiobjective trajectory optimization of the rubber unstacking robot is carried out for the operational efficiency, the running trajectory smoothness, and the energy consumption. Based on the trajectory optimization results, the adaptive backstepping control method based on RFWNN is adopted. The RFWNN method is applied in the main controller to cope with time-varying uncertainties of the robot dynamic system. Simultaneously, an adaptive robust control law is developed to eliminate inevitable approximation errors and unknown disturbances and relax the requirement for prior knowledge of the controlled system. Finally, the validity of the proposed control strategy is verified by experiment

Adaptive Fuzzy Dynamic Surface Sliding Mode Position Control for a Robot Manipulator with Friction and Deadzone

Precise tracking positioning performance in the presence of both the deadzone and friction of a robot manipulator actuator is difficult to achieve by traditional control methodology without proper nonlinear compensation schemes. In this paper, we present a dynamic surface sliding mode control scheme combined with an adaptive fuzzy system, state observer, and parameter estimator to estimate the uncertainty, friction, and deadzone nonlinearities of a robot manipulator system. We design a dynamic surface sliding mode basic controller by systematic recursive design steps that yields several adaptive laws for the compensation of nonlinear friction, deadzone, and other unknown nonlinear dynamics. The boundedness and convergence of this closed-loop system are guaranteed by the Lyapunov stability theorem. Experiments on the Scorbot robot manipulator demonstrate the validity and effectiveness of the proposed control scheme