The problem of either elimination or extraction of an unknown number of nonstationary complex sinusoidal signals burried in noise is considered. We derive a class of adaptive notch filtering algorithms which can be ef...
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The problem of either elimination or extraction of an unknown number of nonstationary complex sinusoidal signals burried in noise is considered. We derive a class of adaptive notch filtering algorithms which can be efficiently (almost transientless) started, or restarted, using the results of local nonparametric, periodogram-based signal analysis.
The optimization problems of nonlinear model predictive control are generally non-convex and their convergence to global optima can hardly be assured. In this paper, interval analysis is applied to such non-convex opt...
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The optimization problems of nonlinear model predictive control are generally non-convex and their convergence to global optima can hardly be assured. In this paper, interval analysis is applied to such non-convex optimization problems by taking advantage of its guaranteed numerical nature of global optimization and constraint satisfaction. Simulation result demonstrates the feasibility of applying interval analysis to discrete-time nonlinear model predictive control.
This paper revisits the Arimoto-algorithm in the discrete-time case. It is shown that if a plant satisfies a positivity condition, there always exists a learning gain so that the algorithm converges monotonically to z...
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The main objective of this paper is to show how one can benefit from using Iterative Learning control instead of conventional feedback control. As a main result it is shown that even if the nominal plant satisfies a g...
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The paper presents the requirements of measurement system for electric arc furnace parameters measurement and for working characteristic calculating. On this basis measurement signals were specified and measurement al...
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The paper presents the requirements of measurement system for electric arc furnace parameters measurement and for working characteristic calculating. On this basis measurement signals were specified and measurement algorithms were developed. For data acquisition a special system was constructed. The system consists of portable computer with acquisition card and conditioning system with differential amplifiers, antialiasing filters and sample and hold circuits. The system is connected with interface build-up permanent in arc furnace. The system software makes possible on-line observation of measured signals values and save data to hard disk. Further calculations may be performed off-line using special developed algorithms. For system software development the open source Python language and GCC compiler for Windows were used.
This paper discusses predictive motion control of a MiRoSoT robot. The dynamic model of the robot is deduced by taking into account the whole process - robot, vision, control and transmission systems. Based on the obt...
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This paper discusses predictive motion control of a MiRoSoT robot. The dynamic model of the robot is deduced by taking into account the whole process - robot, vision, control and transmission systems. Based on the obtained dynamic model, an integrated predictive control algorithm is proposed to position precisely with either stationary or moving obstacle avoidance. This objective is achieved automatically by introducing distant constraints into the open-loop optimization of control inputs. Simulation results demonstrate the feasibility of such control strategy for the deduced dynamic model
A novel local receding horizon control with contractive constraints is proposed in this paper. The main feature of the proposed control algorithm is to form open-loop optimizations in MPC locally. Thus the length of c...
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A novel local receding horizon control with contractive constraints is proposed in this paper. The main feature of the proposed control algorithm is to form open-loop optimizations in MPC locally. Thus the length of control horizons can be set relatively small, which is favorable for real-time applications. Moreover, the total and contractive coefficients in the proposed algorithm correspond to the prediction and control horizons in traditional MPC and they can be adjusted accordingly with clearer physical insights.
Repetitive processes are a distinct class of 2D systems (i.e. information propagation in two independent directions) of both systems theoretic and applications interest. They cannot be controlled by direct extension o...
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Repetitive processes are a distinct class of 2D systems (i.e. information propagation in two independent directions) of both systems theoretic and applications interest. They cannot be controlled by direct extension of existing techniques from either standard (termed 1D here) or 2D systems theory. Here we give new results on the relatively open problem of the design of physically based control laws. These results are for the sub-class of so-called discrete linear repetitive processes, which arise in applications areas such as iterative learning control.
Repetitive processes are a distinct class of two-dimensional systems (i.e., information propagation in two independent directions) of both systems theoretic and applications interest. They cannot be controlled by dire...
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Repetitive processes are a distinct class of two-dimensional systems (i.e., information propagation in two independent directions) of both systems theoretic and applications interest. They cannot be controlled by direct extension of existing techniques from either standard (termed 1D here) or two-dimensional (2D) systems theory. Here, we give new results on the relatively open problem of the design of physically based control laws using an H/sub /spl infin// setting. These results are for the sub-class of so-called differential linear repetitive processes, which arise in application areas such as iterative learning control.
This paper revisits the Arimoto-algorithm in the discrete-time case. It is shown that if a plant satisfies a positivity condition, there always exists a learning gain so that the algorithm converges monotonically to z...
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This paper revisits the Arimoto-algorithm in the discrete-time case. It is shown that if a plant satisfies a positivity condition, there always exists a learning gain so that the algorithm converges monotonically to zero tracking error. If the plant does not satisfy the positivity condition, a linear LQ tracker can be used to condition the plant so that it satisfies the positivity condition. The overall structure results in a novel combination of Arimoto ILC and LQ optimal control, that drives the tracking error monotonically to zero for an arbitrary discrete-time LTI plant. This is a very strong property for any ILC algorithm.
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