Embedded Model Control calls for disturbance modeling and rejection

Robust control design is mainly devoted to guaranteeing the closed-loop stability of a model-based control law in the presence of parametric uncertainties. The control law is usually a static feedback law which is derived from a (nonlinear) model using different methodologies. From this standpoint, stability can only be guaranteed by introducing some ignorance coefficients and restricting the feedback control effort with respect to the model-based design. Embedded Model Control shows that, the model-based control law must and can be kept intact in the case of uncertainty, if, under certain conditions, the controllable dynamics is complemented by suitable disturbance dynamics capable of real-time encoding the different uncertainties affecting the ‘embedded model', i.e. the model which is both the design source and the core of the control unit. To be real-time updated the disturbance state is driven by an unpredictable input vector, the noise, which can only be estimated from the model error. The uncertainty-based (or plant-based) design concerns the noise estimator, so as to prevent the model error from conveying uncertainty components (parametric, cross-coupling, neglected dynamics) which are command-dependent and thus prone to destabilizing the controlled plant, into the embedded model. Separation of the components in the low and high frequency domain by the noise estimator itself allows stability recovery and guarantee, and the rejection of low frequency uncertainty components. Two simple case studies endowed with simulated and experimental runs will help to understand the key assets of the methodolog

Robusno adaptivno upravljanje istosmjernim servomotorom s nelinearnom širokom zračnosti

In this paper, the problem of driving angular position of a direct current servomotor system with unmodeled wide backlash nonlinearity is addressed. In order to tackle this problem, a control scheme based on an adaptive super twisting algorithm is proposed. In order to implement the proposed controller, information about angular velocity is estimated by means of a robust differentiator. Based on a simplified model of the system, the proposed scheme increases robustness against unmodeled dynamics as backlash, as not all the parameters of the system nor the bounds of the perturbations are required to be known. Experimental results considering a wide backlash angle near to 2*PI, illustrate the feasibility and performance of the proposed control methodology.U ovom radu bavi se problemom kutnog pozicioniranja istosmjernog sevomotora s nemodeliranom nelinearnošću široke zračnosti. Za rješenje tog problema predlaže se korištenje upravljačke sheme bazirane na algoritmu adaptivnog uvijanja. Kako bi se implementiralo predloženo upravljanje, kutna brzina estimira se korištenjem robusnog diferencijatora. Bazirana na pojednostavljenom modelu sustava, predložena shema povećava robustnost u odnosu na nemodeliranu dinamiku kao što je zračnost. Pritom nije potrebno poznavanje svih parametara sustava niti očekivane granice smetnji. Eksperimetalni rezultati, koji uzimaju u obzir široki kut zračnosti od skoro pi$, ilustriraju izvodljivost i učinkovitost predloženog algoritma upravljanja

Adaptive neural network control of a robotic manipulator with unknown backlash-like hysteresis

This study proposes an adaptive neural network controller for a 3-DOF robotic manipulator that is subject to backlashlike hysteresis and friction. Two neural networks are used to approximate the dynamics and the hysteresis non-linearity. A neural network, which utilises a radial basis function approximates the robot's dynamics. The other neural network, which employs a hyperbolic tangent activation function, is used to approximate the unknown backlash-like hysteresis. The authors also consider two cases: full state and output feedback control. For output feedback, where system states are unknown, a high gain observer is employed to estimate the states. The proposed controllers ensure the boundedness of the control signals. Simulations are also performed to show the effectiveness of the controllers

Uncertainty and disturbance estimator-based control of a flapping-wing aerial vehicle withwith unknown backlash-like hysteresis

Robust and accurate control of a flapping-wing aerial vehicle (FWAV) system is a challenging problem due to the existence of backlash-like hysteresis nonlinearity. This paper proposes uncertainty and disturbance estimator (UDE)-based control with output feedback for FWAV systems. The approach enables the acquisition of the approximate plant model with only a partial knowledge of system parameters. For the design of the controller, only the bandwidth information of the unknown plant model is needed, which is available through the UDE filter. The stability analysis of the closed-loop system with the UDE-based controller is presented. It is shown that the proposed control scheme can ensure the boundedness of the control signals. A number of numerical simulations are carried out to demonstrate the satisfactory trajectory tracking performance of the proposed method