The authors consider the design of a discrete time model reference adaptive control system (MRACS) for motion controlsystems with unknown nonlinear friction. From a control system viewpoint, the static and Coulomb fr...
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The authors consider the design of a discrete time model reference adaptive control system (MRACS) for motion controlsystems with unknown nonlinear friction. From a control system viewpoint, the static and Coulomb frictions are characterized by a nonlinear dead-zone element and constant value disturbance for the control input signal, respectively. The discrete MRACS is considered for a nonlinear system which is a cascade combination of an unknown dead-zone block and a linear dynamic block with disturbance. In order to verify the validity of the theory, the design method described is applied to a servo system influenced by friction. Experimental results show the high control performance of the constructed MRACS and the effectiveness of the proposed method.< >
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.< >
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.
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.
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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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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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.
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.< >
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