Abstract
The paper presents a control scheme for the real-time tracking problem of nonlinear systems subjected to hard nonlinearities. The proposed tracking controller introduces a refining component in the control input designed for the nominal plant model. The refining component compensates for tracking performance degradation caused by modelling uncertainties and external disturbances. The refining component is modelled as a random signal, the probability density function is expressed as a combination of finite weights typical of particle methods. The weights are updated based on sequential tracking error data. The proposed algorithm is simulated for an inverted pendulum affected by Coulomb friction. Comparison with existing techniques exhibits remarkably superior tracking performance.
Highlights
T RACKING of reference trajectory by a dynamic plant is one of the fundamental challenges in control design
Instead of replicating the behaviour of the nominal controller, we propose control refinement based on the difference between measured system output and an ideal trajectory
The main contributions are summarized as: 1) A combination of classical and particle methods and a joint mechanism of controller and observer has led to a remarkable performance that may exhibit an order of magnitude reduction in tracking error
Summary
T RACKING of reference trajectory by a dynamic plant is one of the fundamental challenges in control design. Another significant contribution in tracking control of Lipschitz nonlinear systems is based on extended order high gain observer [5] In this case, the control law includes estimating an additional extended state of the system, representing uncertainties of modelling and disturbances. Whereas our proposed scheme jointly presents controller and observer behaviour that are not distinct entities anymore This idea of disturbance compensation based on tracking error is unique among the existing techniques and has not been a focus of literature. The main contributions are summarized as: 1) A combination of classical and particle methods and a joint mechanism of controller and observer has led to a remarkable performance that may exhibit an order of magnitude reduction in tracking error.
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