Time filter

Source Type

The paper proposes a solution to the problem of observer-based adaptive fuzzy control for MIMO nonlinear dynamical systems (e.g. robotic manipulators). An adaptive fuzzy controller is designed for a class of nonlinear systems, under the constraint that only the system's output is measured and that the system's model is unknown. The control algorithm aims at satisfying the H ∞ tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the MIMO system into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neuro-fuzzy networks. Moreover, since only the system's output is measurable the complete state vector has to be reconstructed with the use of a state observer. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis, it is proven that the proposed observer-based adaptive fuzzy control scheme results in H ∞ tracking performance. © 2014 Springer Science+Business Media Dordrecht. Source

Rigatos G.G.,Industrial Systems Institute
International Journal of Advanced Robotic Systems

This paper presents an approach to distributed state estimation-based control of nonlinear MIMO systems, capable of incorporating delayed measurements in the estimation algorithm while also being robust to packet losses. First, the paper examines the problem of distributed nonlinear filtering over a communication/sensors network, and the use of the estimated state vector in a control loop. As a possible filtering approach, an extended information filter (EIF) is proposed. The extended information filter requires the computation of Jacobians which in the case of high order nonlinear dynamical systems can be a cumbersome procedure, while it also introduces cumulative errors to the state estimation due to the approximative linearization performed in the Taylor series expansion of the system's nonlinear model. To overcome the aforementioned weaknesses of the extended information filter, a derivative-free approach to extended information filtering has been proposed. Distributed filtering is now based on a derivative-free implementation of Kalman filtering which is shown to be applicable to MIMO nonlinear dynamical systems. In the proposed derivativefree extended information filtering, the system is first subject to a linearization transformation that makes use of the differential flatness theory. It is shown how the proposed distributed filtering method can succeed in compensation of random delays and packet drops which may appear during the transmission of measurements and of state vector estimates, thus assuring a reliable performance of the distributed filtering-based control scheme. Evaluation tests are carried out on benchmark MIMO nonlinear systems, such as multi-DOF robotic manipulators. © 2013 Rigatos. Source

Rigatos G.G.,Industrial Systems Institute
Neural Computing and Applications

With the field-oriented method, the dynamic behavior of the induction motor is rather similar to that of a separately excited DC motor. However, the control performance of the induction motor is still influenced by the unmodelled dynamics or external disturbances, and to compensate for these uncertainties, adaptive fuzzy control is proposed. The overall control signal consists of two elements, (1) the equivalent control which is used for linearization of the induction motor's model through feedback of the state vector. The equivalent control includes neurofuzzy approximators of the unknown parts of the induction motor model (2) the supervisory control which consists of an H ∞ term and compensates for parametric uncertainties of the induction motor model and external disturbances. The performance of the proposed adaptive fuzzy H ∞ controller is compared to backstepping nonlinear control through simulation tests. © 2011 Springer-Verlag London Limited. Source

The paper analyzes wave-type partial differential equations that describe the transmission of neural signals and proposes filtering for estimating the spatiotemporal variations of voltage in the neurons' membrane. It is shown that in specific neuron models the spatiotemporal variations of the membrane's voltage follow partial differential equations (PDEs) of the wave type while in other models such variations are associated with the propagation of solitary waves in the membrane. To compute the dynamics of the membrane PDE model without knowledge of initial conditions and through the processing of noisy measurements, a new filtering method, under the name Derivative-free nonlinear Kalman Filtering, is proposed. The PDE of the membrane is decomposed into a set of nonlinear ordinary differential equations with respect to time. Next, each one of the local models associated with the ordinary differential equations is transformed into a model of the linear canonical (Brunovsky) form through a change of coordinates (diffeomorphism) which is based on differential flatness theory. This transformation provides an extended model of the nonlinear dynamics of the membrane for which state estimation is possible by applying the standard Kalman Filter recursion. The proposed filtering method is tested through numerical simulation tests. © 2013 Elsevier B.V. Source

Rigatos G.G.,Industrial Systems Institute
Mathematics and Computers in Simulation

Motion control of mobile robots and efficient trajectory tracking is usually based on prior estimation of the robots' state vector. To this end Gaussian and nonparametric filters (state estimators from position measurements) have been developed. In this paper the Extended Kalman Filter which assumes Gaussian measurement noise is compared to the Particle Filter which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of a mobile robot is used, when measurements are available from both odometric and sonar sensors. It is shown that in this kind of sensor fusion problem the Particle Filter has better performance than the Extended Kalman Filter, at the cost of more demanding computations. © 2010 IMACS. Source

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