Differential flatness theory has been widely used for both tracking control and stateestimation of nonlinear systems. Recently, the concept of flat inputs has been proved to be a valuable tool for control design if t...
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Differential flatness theory has been widely used for both tracking control and stateestimation of nonlinear systems. Recently, the concept of flat inputs has been proved to be a valuable tool for control design if the system is non-differentially flat. However, flat inputs have not been used yet to solve stateestimation problems for these systems. By constructing flat inputs, we introduced a novel state estimation-based control strategy for both observable non-differentially flat nonlinear systems and differentially flat nonlinear systems whose flat output vector is not a measurable variable, as long as the internal dynamics of the system are stable. The efficiency of the proposed approach is illustrated through two numerical examples. Our findings indicated that we increased the class of systems to which the derivative-free nonlinear Kalman filtering based on differential flatness theory can be applied.
The Kalman filter is one of the most widely used methods for stateestimation and control purposes. However, it requires correct knowledge of noise statistics, which are unknown or not known perfectly in real-life app...
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The Kalman filter is one of the most widely used methods for stateestimation and control purposes. However, it requires correct knowledge of noise statistics, which are unknown or not known perfectly in real-life applications and then they need to be identified. Considering such background, this paper introduces a new adaptive Kalman filter algorithm in order to handle the unknown process noise covariance for linear discrete-time closed-loop systems. From the closed-loop joint analysis of the system and the a priori recursive form of the Kalman filter, we adaptively estimate the process noise covariance by relating it to the observation vector covariance. The latter is then obtained from an exponential moving average technique. Lastly, we also extend our adaptive methodology for a special class of nonlinear systems. The performance of the proposed adaptive method is demonstrated through numerical examples and it has been compared to other types of adaptive filtering algorithms.
control of hot-steel rolling mills aims at raising the levels of quality of the related industrial production and at minimising the cost of the electric energy consumed by such industrial units. This paper proposes a ...
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control of hot-steel rolling mills aims at raising the levels of quality of the related industrial production and at minimising the cost of the electric energy consumed by such industrial units. This paper proposes a non-linear optimal control approach for the hot-steel rolling mill system. The non-linear dynamic model of the hot-steel rolling mill undergoes approximate linearisation around a temporary operating point which is recomputed at each iteration of the control method. The linearisation relies on Taylor series expansion and on the calculation of the system's Jacobian matrices. For the approximately linearised model of the hot-steel rolling process, an H-infinity feedback controller is designed. This controller provides the solution of the non-linear optimal control problem for the system under model uncertainty and external perturbations. For the computation of the controller's feedback gain, an algebraic Riccati equation is iteratively solved at each time-step of the control method. The global asymptotic stability properties of the control method are proven through Lyapunov analysis. Finally, to implement state estimation-based control for this system, the H-infinity Kalman filter is proposed as a robust state estimator.
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 loss...
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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 stateestimation 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 derivative-free 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-basedcontrol scheme. Evaluation tests are carried out on benchmark MIMO nonlinear systems, such as multi-DOF robotic manipulators.
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