作者：
D.E. RiveraS.V. GaikwadDepartment of Chemical
Bio and Materials Engineering and Control Systems Engineering Laboratory Computer-Integrated Manufacturing Systems Research Center Arizona State University Tempe AZ USA
The use of prefiltered ARX (autoregressive with exogenous input) estimation to obtain reduced-order models that satisfy the Prett-Garcia (1988) digital PID (proportional plus integral plus derivative) tuning rules is ...
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The use of prefiltered ARX (autoregressive with exogenous input) estimation to obtain reduced-order models that satisfy the Prett-Garcia (1988) digital PID (proportional plus integral plus derivative) tuning rules is described. The design of the prefilter is performed systematically using the engineer's desired control requirements and the setpoint/disturbance characteristics of the problem. The benefits of this method are shown for a fourth-order system.< >
Many processes operate only around a limited number of operation points. In order to have adequate control around each operation point, an adaptive controller could be used. Then, if the operation point changes often,...
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Many processes operate only around a limited number of operation points. In order to have adequate control around each operation point, an adaptive controller could be used. Then, if the operation point changes often, a large number of parameters would have to be adapted over and over again. This prohibits application of conventional adaptive control, which is more suited for processes with slowly changing parameters. Furthermore, continuous adaptation is not always needed or desired. An extension of adaptive control is presented, in which for each operation point the process behaviour can be stored in a memory, retrieved from it and evaluated. These functions are coordinated by a "supervisor". This concept is referred to as supervisory control. It leads to an adaptive control structure which, after a learning phase, quickly adjusts the controller parameters based on retrieval of old information, without the need to fully relcam each time. This approach has been tested on an experimental set-up of a flexible beam, but it is directly applicable to processes in e.g. the (petro)chemical industry as well.
作者：
Daniel E. RiveraSujit V. GaikwadDepartment of Chemical
Bio and Materials Engineering and Control Systems Engineering Laboratory Computer-Integrated Manufacturing Systems Research Center Arizona State University Tempe AZ USA
This paper presents control-relevant parameter estimation as a means to address the problem of modeling requirements for a combined feedback-feedforward control system. The key element in the estimation procedure is p...
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This paper presents control-relevant parameter estimation as a means to address the problem of modeling requirements for a combined feedback-feedforward control system. The key element in the estimation procedure is prefiltering of the input and output time series obtained from the plant. This insures that the estimated model retains those plant characteristics that are most significant with regards to the user's control requirements. An example model reduction problem is presented which uses the IMC design procedure to design a reduced-order feedback-feedforward control system. Its performance is compared to both full-order IMC and model-predictive control via DMC.
The design of binary hypothesis tests in the absence of any statistical information, based only on a set of available observations is studied. A Structured Adaptive Network (SAN) configuration for the design of such t...
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The design of binary hypothesis tests in the absence of any statistical information, based only on a set of available observations is studied. A Structured Adaptive Network (SAN) configuration for the design of such tests based on several criteria of optimality, is presented and certain key asymptotic properties of these criteria are established.
A method for model reference adaptive control (MRAC) with improved transient performance is introduced. It is shown that with this method the zero-state output error can be made arbitrarily small. For the linear syste...
A method for model reference adaptive control (MRAC) with improved transient performance is introduced. It is shown that with this method the zero-state output error can be made arbitrarily small. For the linear system case, the value of the high frequency gain, k/sub p/, of the plant does not have to be known a priori, i.e., some uncertainty on k/sub p/ is allowed. The structure of the proposed controller allows for existing convergence results such as exponential convergence of output and parameter errors in the presence of sufficiently rich reference inputs to remain valid. It is also shown that the proposed controller demonstrates improved robustness in the presence of bounded disturbances and/or modeled dynamics, as well as in the case in which adaptation is switched off.< >
This paper analyzes the robustness properties and modeling requirements for model-predictive control via Horizon Predictive control (HPC). The theory of Structured Singular Values is used to determine optimal values f...
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This paper analyzes the robustness properties and modeling requirements for model-predictive control via Horizon Predictive control (HPC). The theory of Structured Singular Values is used to determine optimal values for the correction horizon in HPC given user-provided uncertainty intervals and performance weights. Regarding system identification, control-relevant identification principles are used to provide guidelines for input signal design, prefiltered estimation, and uncertainty modeling. These results are tested experimentally using data from a methanol-isopropanol distillation column.
An iteration method is presented for determining the largest singular value (2-norm) of a matrix, and its corresponding singular vectors. Connections with the power method and Bernoulli's method are presented. A f...
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An iteration method is presented for determining the largest singular value (2-norm) of a matrix, and its corresponding singular vectors. Connections with the power method and Bernoulli's method are presented. A formula is derived which describes the relationship between a matrix perturbation and the perturbation of its singular values.< >
A number of robust stability problems take the following form: A polynomial has real coefficients wvhich are multiaffine in real parameters that are confined to a box in parameter space. An efficient method is require...
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A number of robust stability problems take the following form: A polynomial has real coefficients wvhich are multiaffine in real parameters that are confined to a box in parameter space. An efficient method is required for checking the stability of this set of polynomials. We present two sufficient conditions in this paper. They involve: checking certain properties at the corners and edges of the parameter space box.
The authors present a general approach to determining the number of sinusoids present in measurements corrupted by additive white Gaussian and nonGaussian noise. The approach involves the simultaneous application of m...
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The authors present a general approach to determining the number of sinusoids present in measurements corrupted by additive white Gaussian and nonGaussian noise. The approach involves the simultaneous application of maximum a posteriori detection and nonlinear estimation using either the extended Kalman filter when the noise is Gaussian, or the extended high order filter when the noise is nonGaussian. The problem is formulated as a multiple hypothesis testing problem with assumed known a priori probabilities for each hypothesis. The advantage of the approach lies in the potential to accommodate time-varying as well as time-invariant parameters in the measurement model. Experimental evaluation of the approach demonstrates excellent performance in selecting the correct model order and estimating the system parameters even for signal-to-noise ratios as low as -5 dB.< >
A nonlinear adaptive detector/estimator is introduced for single and multiple radar data processing. The problem of target detection from returns of monostatic radar(s) is formulated as a nonlinear joint detection/est...
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A nonlinear adaptive detector/estimator is introduced for single and multiple radar data processing. The problem of target detection from returns of monostatic radar(s) is formulated as a nonlinear joint detection/estimation problem on the unknown parameters in the signal return. The problems of detecting the target and estimating its parameters are considered jointly. A bank of spatially and temporally localized nonlinear filters is used to estimate the a posteriori likelihood of the existence of the target in a given space-time resolution cell. Within a given cell, the localized filters are used to produce refined spatial estimates of the target parameters. A decision logic is used to decide on the existence of a target within any given resolution cell based on the a posteriori estimates obtained from the likelihood functions. Simulation results show excellent detection capabilities and excellent resolution in target parameter estimation for both single and multiple sensor data.< >
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