作者：
R. TennoUniversity
School of Electrical Engineering PO Box 15500 Aalto Finland
The propagation of local source noises in space-time and their effect on the concentration field of species in the diffusion layer are analysed in this paper. The source noises are incited by several categories of unc...
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The propagation of local source noises in space-time and their effect on the concentration field of species in the diffusion layer are analysed in this paper. The source noises are incited by several categories of uncertainties from a realistic control system; they are devised by process physics and a control system structure. In process control, these noises provoke a widespread control error. This paper demonstrates that the control error is limited and attenuates spatially, even if a relatively simple boundary control is applied. The covariance structure of the control errors is found and analysed numerically.
Modelling and control of the extruder temperature field is discussed, a process governed by a nonlinear partial differential equation. Utilizing finite element approximation a discretized input/output representation o...
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Modelling and control of the extruder temperature field is discussed, a process governed by a nonlinear partial differential equation. Utilizing finite element approximation a discretized input/output representation of the system is created with the inputs being powers of heaters and output the extruder temperature field. Local linearization is applied at the operating point and the controller is designed based on the lumped-input and distributed-parameter-output systems approach, using the time-space decoupling of system's dynamics.
This paper discusses the (inverse) optimality and practical usage of passivity-based controls for distributed port-Hamiltonian systems. We first clarify that passivity-based controls, damping assignment and potential ...
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This paper discusses the (inverse) optimality and practical usage of passivity-based controls for distributed port-Hamiltonian systems. We first clarify that passivity-based controls, damping assignment and potential shaping can be derived from a linear quadratic type optimal control problem. Next, we describe the limitation of passivity-based boundary controls and propose a practical usage of the methods in terms of discretization. Finally, we illustrate numerical results having a similar property to the strain feedback methods derived from semigroup theory for stabilizing and stiffness controlling flexible beams.
The flow of fluids in a petroleum reservoir can be modeled using a set of nonlinear second order partial differential equations. To produce the fluids, especially oil, a number of production wells are drilled through ...
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The flow of fluids in a petroleum reservoir can be modeled using a set of nonlinear second order partial differential equations. To produce the fluids, especially oil, a number of production wells are drilled through the reservoir formations. The well model, which describes the interaction between the well and the reservoir, can be considered as the boundary conditions for the flow equations. The amount of produced fluid is usually controlled by changing the bottom hole pressures via control valves. Because the reservoir could contain undesirable fluids, e.g., water or gas, setting the bottom hole pressures to the maximum production limit could cause high water or gas production, which would decrease the oil revenue. The present paper describes an optimal control method for maximizing the oil revenue in petroleum reservoir systems. The flow equation is discretized in a space variable to yield a state space representation model. The valve openings are considered as the control variables and are written as piecewise constant functions. Furthermore, the gradient of the oil revenue with respect to the control variable is computed using the adjoint method and the optimal control setting is obtained using a line search method. A numerical example of a water-flooding case is presented to illustrate the application of the method.
Industrial noise can be successfully mitigated with the combined use of passive and Active Noise Control (ANC) strategies. In a noisy area, a practical solution for noise attenuation may include both the use of baffle...
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Industrial noise can be successfully mitigated with the combined use of passive and Active Noise Control (ANC) strategies. In a noisy area, a practical solution for noise attenuation may include both the use of baffles and ANC. When the operator is required to stay in movement in a delimited spatial area, conventional ANC is usually not able to adequately cancel the noise over the whole area. New control strategies need to be devised to achieve acceptable spatial coverage. A three-dimensional actuator model is proposed in this paper. Active Noise Control (ANC) usually requires a feedback noise measurement for the proper response of the loop controller. In some situations, especially where the real-time tridimensional positioning of a feedback transducer is unfeasible, the availability of a 3D precise noise level estimator is indispensable. In our previous works [1,2], using a vibrating signal of the primary source of noise as an input reference for spatial noise level prediction proved to be a very good choice. Another interesting aspect observed in those previous works was the need for a variable-structure linear model, which is equivalent to a sort of a nonlinear model, with unknown analytical equivalence until now. To overcome this in this paper we propose a model structure based on an Artificial Neural Network (ANN) as a nonlinear black-box model to capture the dynamic nonlinear behaveior of the investigated process. This can be used in a future closed loop noise cancelling strategy. We devise an ANN architecture and a corresponding training methodology to cope with the problem, and a MISO (Multi-Input Single-Output) model structure is used in the identification of the system dynamics. A metric is established to compare the obtained results with other works elsewhere. The results show that the obtained model is consistent and it adequately describes the main dynamics of the studied phenomenon, showing that the MISO approach using an ANN is appropriate for the si
Recently the problem of estimating the initial state of some linear infinite-dimensional systems from measurements on a finite interval was solved by using the sequence of forward and backward observers [14]. In the p...
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Recently the problem of estimating the initial state of some linear infinite-dimensional systems from measurements on a finite interval was solved by using the sequence of forward and backward observers [14]. In the present paper, we introduce a direct Lyapunov approach to the problem and extend the results to the class of semilinear systems governed by 1-d wave equations with boundary measurements from a finite interval. We first design forward observers and derive Linear Matrix Inequalities (LMIs) for the exponential stability of the estimation errors. Further we find LMIs for an upper bound T * on the minimal time, that guarantees the convergence of the sequence of forward and backward observers on [0, T * ] for the initial state recovering. For observation times bigger than T * , these LMIs give upper bounds on the convergence rate of the iterative algorithm in the norm defined by the Lyapunov functions. The efficiency of the results are illustrated by a numerical example.
Most engineering practical systems are distributed parameter systems. Sensor location optimal strategy is distinctive to distributed parameter systems, which has a significant effect on the precision of parameter iden...
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Most engineering practical systems are distributed parameter systems. Sensor location optimal strategy is distinctive to distributed parameter systems, which has a significant effect on the precision of parameter identification. The optimal sensor location is one of the pivotal problems to achieve optimal control of distributed parameter systems. Sensor optimal locations not only relate with boundary con ditions, but also relate with many factors such as inputs, system noise, measure noise and process dynamic characteristic. Moreover, these factors have different effect on the optimal sensor location. The states of distributed parameter systems have infinite freedoms in space, but measurements are usually put only on limited points in distributed spaces, and the observed values are polluted by the noise. So it is significant to choose sensor optimal locations for distributed parameter systems. Based on orthogonal function approximation theory, a kind of optimal algorithm was put forward via wavelets transform and their operational matrixes in this paper. The simulation result shows the efficiency of the proposed method, which is significant for the choice of optimal sensor location in the distributed parameter systems.
This contribution is devoted to the accessibility analysis of distributed parameter systems. A formal system theoretical approach is proposed by means of differential geometry, which allows an intrinsic representation...
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This contribution is devoted to the accessibility analysis of distributed parameter systems. A formal system theoretical approach is proposed by means of differential geometry, which allows an intrinsic representation for the class of infinite dimensional systems. Beginning with the introduction of a convenient representation form, in particular, the accessibility along a trajectory is discussed generally. In addition, the derivation of (local) (non-)accessibility criteria via utilizing transformation groups is shown. In order to illustrate the developed theory the proposed method is applied to an example.
Exponential stability analysis via Lyapunov-Krasovskii method is extended to linear time-delay systems in a Hilbert space. The operator acting on the delayed state is supposed to be bounded. The system delay is admitt...
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Exponential stability analysis via Lyapunov-Krasovskii method is extended to linear time-delay systems in a Hilbert space. The operator acting on the delayed state is supposed to be bounded. The system delay is admitted to be unknown and time-varying with an a priori given upper bound on the delay. Sufficient delay-dependent conditions for exponential stability are derived in the form of Linear Operator Inequalities (LOIs), where the decision variables are operators in the Hilbert space. Being applied to a heat equation and to a wave equation, these conditions are represented in terms of standard Linear Matrix Inequalities (LMIs). The proposed method is expected to provide effective tools for robust control of distributed parameter systems with time-delay.
In this paper we discuss fast implementation of the model based centralized controllers using fractional Fourier transform for large scale plant models coming from spatial discretization of a certain type of linear sp...
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In this paper we discuss fast implementation of the model based centralized controllers using fractional Fourier transform for large scale plant models coming from spatial discretization of a certain type of linear spatially-varying distributed parameter systems. This fast implementation reduces the computational time delay significantly when the dimension of the system is higher than 512 = 2 9 . Compared to direct implementation, the proposed method allows faster sampling. If the control design objectives are demanding fast closed loop modes, then slower sampling required by direct implementation leads to instability. The results are illustrated by an example.
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