Adaptive Control for Robotic Manipulators base on RBF Neural Network

An adaptive neural network controller is brought forward by the paper to solve trajectory tracking problems of robotic manipulators with uncertainties.  The  first  scheme consists of  a PD feedback  and  a  dynamic  compensator  which is  composed by  neural  network controller and  variable  structure controller.  Neutral network controller is designed to adaptive learn and compensate the unknown uncertainties, variable   structure controller is designed to eliminate approach errors of neutral network. The adaptive weight learning algorithm of neural network is designed to ensure online real-time adjustment, offline learning phase is not need; Global asymptotic stability (GAS) of system base on Lyapunov theory is analysised to ensure the convergence of the algorithm. The simulation results show that the kind of the control scheme is effective and has good robustness

Adaptive Neural Network Feedforward Control for Dynamically Substructured Systems

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An adaptive variable structure controller for the trajectory tracking of a nonholonomic mobile robot with uncertainties and disturbances

In this paper, a trajectory tracking control for a nonholonomic mobile robot subjected to uncertainties and disturbances in the kinematic model is proposed. An adaptive variable structure controller based on the sliding mode theory is used, and applied to compensate these uncertainties and disturbances. To minimize the problems found in practical implementation using classical variable structure controllers, and eliminate the chattering phenomenon as well as compensate disturbances a neural compensator is used, which is nonlinear and continuous, in lieu of the discontinuous portion of the control signals present in classical forms. The proposed neural compensator is designed by a modeling technique of Gaussian radial basis function neural networks and does not require the time-consuming training process. Stability analysis is guaranteed with basis on the Lyapunov method. Simulation results are provided to show the effectiveness of the proposed approach.Facultad de Informátic

Process control of a laboratory combustor using neural networks

Active feedback and feedforward-feedback control systems based on static-trained feedforward multi-layer-perceptron (FMLP) neural networks were designed and demonstrated, by experiment and simulation, for selected species from a laboratory two stage combustor. These virtual controllers functioned through a Visual Basic platform. A proportional neural network controller (PNNC) was developed for a monotonic control problem - the variation of outlet oxygen level with overall equivalence ratio (Φ0). The FMLP neural network maps the control variable to the manipulated variable. This information is in turn transferred to a proportional controller, through the variable control bias value. The proposed feedback control methodology is robust and effective to improve control performance of the conventional control system without drastic changes in the control structure. A detailed case study in which two clusters of FMLP neural networks were applied to a non-monotonic control problem - the variation of outlet nitric oxide level with first-stage equivalence ratio (Φ0) - was demonstrated. The two clusters were used in the feedforward-feedback control scheme. The key novelty is the functionalities of these two network clusters. The first cluster is a neural network-based model-predictive controller (NMPC). It identifies the process disturbance and adjusts the manipulated variables. The second cluster is a neural network-based Smith time-delay compensator (NSTC) and is used to reduce the impact of the long sampling/analysis lags in the process. Unlike other neural network controllers reported in the control field, NMPC and NSTC are efficiently simple in terms of the network structure and training algorithm. With the pre-filtered steady-state training data, the neural networks converged rapidly. The network transient response was originally designed and enabled here using additional tools \u27and mathematical functions in the Visual Basic program. The controller based on NMPC/NSTC showed a superior performance over the conventional proportional-integral derivative (PID) controller. The control systems developed in this study are not limited to the combustion process. With sufficient steady-state training data, the proposed control systems can be applied to control applications in other engineering fields

Fuzzy second order sliding mode control of a unified power flow controller

Purpose. This paper presents an advanced control scheme based on fuzzy logic and second order sliding mode of a unified power flow controller. This controller offers advantages in terms of static and dynamic operation of the power system such as the control law is synthesized using three types of controllers: proportional integral, and sliding mode controller and Fuzzy logic second order sliding mode controller. Their respective performances are compared in terms of reference tracking, sensitivity to perturbations and robustness. We have to study the problem of controlling power in electric system by UPFC. The simulation results show the effectiveness of the proposed method especiallyin chattering-free behavior, response to sudden load variations and robustness. All the simulations for the above work have been carried out using MATLAB / Simulink. Various simulations have given very satisfactory results and we have successfully improved the real and reactive power flows on a transmission lineas well as to regulate voltage at the bus where it is connected, the studies and illustrate the effectiveness and capability of UPFC in improving power.В настоящей статье представлена усовершенствованная схема управления, основанная на нечеткой логике и режиме скольжения второго порядка унифицированного контроллера потока мощности. Данный контроллер обладает преимуществами с точки зрения статической и динамической работы энергосистемы, например, закон управления синтезируется с использованием трех типов контроллеров: пропорционально-интегрального, контроллера скользящего режима и контроллера скользящего режима нечеткой логики второго порядка. Их соответствующие характеристики сравниваются с точки зрения отслеживания эталонов, чувствительности к возмущениям и надежности. Необходимо изучить проблему управления мощностью в энергосистеме с помощью унифицированного контроллера потока мощности (UPFC). Результаты моделирования показывают эффективность предложенного метода, особенно в отношении отсутствия вибрации, реакции на внезапные изменения нагрузки и устойчивости. Все расчеты для вышеуказанной работы были выполнены с использованием MATLAB/Simulink. Различные расчетные исследования дали весьма удовлетворительные результаты, и мы успешно улучшили потоки реальной и реактивной мощности на линии электропередачи, а также регулирование напряжения на шине, к которой она подключена, что позволяет изучить и проиллюстрировать эффективность и возможности UPFC для увеличения мощности

Optimal control design for robust fuzzy friction compensation in a robot joint

This paper presents a methodology for the compensation of nonlinear friction in a robot joint structure based on a fuzzy local modeling technique. To enhance the tracking performance of the robot joint, a dynamic model is derived from the local physical properties of friction. The model is the basis of a precompensator taking into account the dynamics of the overall corrected system by means of a minor loop. The proposed structure does not claim to faithfully reproduce complex phenomena driven by friction. However, the linearity of the local models simplifies the design and implementation of the observer, and its estimation capabilities are improved by the nonlinear integral gain. The controller can then be robustly synthesized using linear matrix inequalities to cancel the effects of inexact friction compensation. Experimental tests conducted on a robot joint with a high level of friction demonstrate the effectiveness of the proposed fuzzy observer-based control strategy for tracking system trajectories when operating in zero-velocity regions and during motion reversals

Design and assessment of a multiple sensor fault tolerant robust control system

This paper presents an enhanced robust control design structure to realise fault tolerance towards sensor faults suitable for multi-input-multi-output (MIMO) systems implementation. The proposed design permits fault detection and controller elements to be designed with considerations to stability and robustness towards uncertainties besides multiple faults environment on a common mathematical platform. This framework can also cater to systems requiring fast responses. A design example is illustrated with a fast, multivariable and unstable system, that is, the double inverted pendulum system. Results indicate the potential of this design framework to handle fast systems with multiple sensor faults