The paper deals with modeling and fault-tolerant control of the real battery assembly system, which is under implementation in the RAFI GmbH Company (one of the leading electronic manufacturing service provider in Ger...
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The paper deals with robust fault estimation problem for non-linear discrete-time Lipschitz systems and robust state observer design. Proposed fault estimation strategy is based on Unknown Input Observer and H ∞ gen...
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The paper deals with robust fault estimation problem for non-linear discrete-time Lipschitz systems and robust state observer design. Proposed fault estimation strategy is based on Unknown Input Observer and H ∞ general frameworks, which are used to formulate robust fault and state observer problem. The prescribed disturbance attenuation level is achieved, while guaranteeing convergence of the observer by solving H ∞ robust observer problem. The DMVT is proposed to deal with nonlinearities by assuring that non-linear function is outer-bounded. The overall problem is described by set of linear matrix inequalities, which are efficiently handled by freely available solvers. In the final part illustrative example is presented in order to validate proposed approach on three-tank system.
In the paper a task scheduling problem on two parallel identical processors is considered. The objective is to find the schedule of minimum makespan. We assume that tasks are independent and can be preempted but preem...
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In the paper a task scheduling problem on two parallel identical processors is considered. The objective is to find the schedule of minimum makespan. We assume that tasks are independent and can be preempted but preemption is only allowed if a task is continuously processed for at least k units of time. We propose two heuristic scheduling algorithms for such a problem and compare their effectiveness with other algorithms known from the literature.
Modern digital microscopy systems allow imaging of biological material with very high accuracy. Paradoxically, this gives rise to many problems because huge amounts of raw data significantly increase the time required...
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This paper discusses the possibility of using a Jordan neural network as a model of dynamic systems and it presents a Model Predictive control (MPC) algorithm in which such a network is used for prediction. The Jordan...
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This paper discusses the possibility of using a Jordan neural network as a model of dynamic systems and it presents a Model Predictive control (MPC) algorithm in which such a network is used for prediction. The Jordan network is a simple recurrent neural structure in which only one value of the process input signal (from the previous sampling instant) and only one value of the delayed output signal of the model (from the previous sampling instant) are used as the inputs of the network. In order to obtain a computationally simple MPC algorithm, the nonlinear Jordan neural model is repeatedly linearised on-line around an operating point, which leads to a quadratic optimisation problem. Effectiveness of the described MPC algorithm is compared with that of the truly nonlinear MPC scheme with on-line nonlinear optimisation performed at each sampling instant.
This paper is concerned with a Model Predictive control (MPC) algorithm for dynamic systems described by nonlinear state-space models. A unique feature of the algorithm is the fact that the current value of the manipu...
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This paper is concerned with a Model Predictive control (MPC) algorithm for dynamic systems described by nonlinear state-space models. A unique feature of the algorithm is the fact that the current value of the manipulated variable (i.e. the decision variable of MPC) is not calculated from an optimisation problem, but from an analytical linear control law. The coefficients of the control law, due to a nonlinear nature of the process, are time-varying. They are found on-line by an approximator (a neural network is used for this purpose). The approximator is trained off-line in such a way that the resulting MPC algorithm mimics the suboptimal MPC technique with online model linearisation. Thanks to such an approach, successive on-line model linearisation is not successively performed and some other calculations are not necessary. For a polymerisation reactor off-line training of the approximator is described and the approximate algorithm is compared with the classical MPC algorithms with on-line model linearisation.
This paper describes a Model Predictive control (MPC) algorithm in which a Radial Basis Function (RBF) neural network is used as a dynamic model of the controlled process and it reports training and selection of the R...
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This paper describes a Model Predictive control (MPC) algorithm in which a Radial Basis Function (RBF) neural network is used as a dynamic model of the controlled process and it reports training and selection of the RBF model of the benchmark system for MPC. In order to obtain a computationally uncomplicated control scheme, the RBF model is successively linearised on-line, which leads to an easy to solve quadratic optimisation problem, nonlinear optimisation is not necessary. Efficacy of the MPC algorithm is shown for a neutralisation system, which is a significantly nonlinear dynamic process. It is shown that the described MPC algorithm with on-line model linearisation gives trajectories very similar to those obtained in a truly nonlinear MPC scheme, in which the full nonlinear RBF model is used for prediction.
The paper deals with the problem of robust unknown input observer design for the neural-network based models of non-linear discrete-time systems. Authors review the recent development in the area of robust observers f...
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Nowadays, the research on robot on-map localization while using landmarks is more intensively dealing with visual code recognition. One of the most popular landmarks of this type is the QR-code. This paper is devoted ...
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In this paper a method is introduced that combines Inertial Measurement Unit (IMU) readouts with low accuracy and temporarily unavailable velocity measurements (e.g., based on kinematics or GPS) to produce high accura...
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ISBN:
(纸本)9781479987023
In this paper a method is introduced that combines Inertial Measurement Unit (IMU) readouts with low accuracy and temporarily unavailable velocity measurements (e.g., based on kinematics or GPS) to produce high accuracy estimates of velocity and orientation with respect to gravity. The method is computationally cheap enough to be readily implementable in sensors. The main area of application of the introduced method is mobile robotics.
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