Showing papers on "MIMO published in 1985"
01 Dec 1985
TL;DR: In this paper, an improvement of the Quantitative Feedback Theory (QFT) for MIMO systems is presented, which makes the design much easier and even more economical in terms of cost of feedback.
Abstract: An improvement of the Quantitative Feedback Theory (QFT) of Horowitz[1] for MIMO systems is presented. The advantages of this approach are: (a) In the 'improved method' the fundamental design relation (for the ith free function li)has the form | 1 + li|> ?(buv, quv) where buvare related to the performance tolerances of the closed loop, and quv) to the plant parameters. We show that the right side can be replaced by a constant. This makes the design much easier and even more economical in terms of cost of feedback. (b) The SISO systems that replace the original MIMO problem are now defined by induction. This gives a better insight for the understanding of the tradeoffs between the loop transmission and make it easier to be implemented in the computer. The attractive properties of this design method are: (1) The problem is reduced to successive single loop designs with no interaction between them and no iteration necessary. (2) Stability over the range of parameter uncertainty is automatically guaranteed. (3) There is insight to the tradeoff between the loop transmissions. (4) The synthesis technique can handle the attenuation of plant disturbances. (5) This technique can be applied to all the plants P such that all the elements of p-1 have no (RHP) poles. This new technique has been applied successfully to many examples, one of which is presented here.
107 citations
TL;DR: In this article, the authors derived the correlation method in the frequency domain for linear time invariant MIMO closed loop systems and showed that in contrast to the indirect method, the direct method and the joint process method the solutions for the frequency response matrices and the spectral density matrices which can be determined from the measured signals only are summarized.
5 citations
19 Jun 1985
TL;DR: It is shown that using the multi-input multi-output weighted one-step-ahead (MIMO W.O.S.A.) adaptive controller reduces the magnitude of fluctuations of the control signals which result in improved control of the boiler outputs when they are controlled by the adaptive controller.
Abstract: The Multi-Input Multi-Output One-Step-Ahead (MIMO O.S.A.) Adaptive Controller [1,2] is applied to a power plant boiler to control three outputs using three input variables. The power plant boiler is the same system to which a multivariable self-tuning controller was previously applied in [3]. This paper shows the improved performance of the MIMO O.S.A. adaptive controller over that of the multivariable self-tuning controller shown in [3]. The reason for this improvement is that a sampling period smaller than the largest time delay in the system is not allowed in the design of multivariable self-tuning controller. The MIMO O.S.A. adaptive controller does not impose this constraint on the sampling period. This paper also shows that using the multi-input multi-output weighted one-step-ahead (MIMO W.O.S.A.) adaptive controller reduces the magnitude of fluctuations of the control signals which result in improved control of the boiler outputs. Comparison of the performance of the MIMO W.O.S.A. adaptive controller with the performance of the existing PI controllers, when the system goes through a transient mode, shows that the outputs stay closer to their set points when they are controlled by the adaptive controller.
3 citations
TL;DR: It is demonstrated that one of the configurations considered is better than all the others and is extended to the non-linear multi-input multi-output case.
Abstract: The paper compares several feedback configurations that have appeared in the literature (eg unity-feedback, model-reference, etc) It first considers the linear time-invariant multi-input multi-output case For each configuration, we specify the stability conditions, the set of all achievable I/O maps and the set of all achievable disturbance-to-output maps The effects of various subsystem perturbations on the system performance are studied In terms of these considerations, it is demonstrated that one of the configurations considered is better than all the others The results are then extended to the non-linear multi-input multi-output case
2 citations
TL;DR: In this article, the structure identification and parameter estimation of MIMO linear discrete stochastic systems are discussed using Luenberger's observable canonical form with steady-state Kalman filtering representation.
01 Dec 1985
TL;DR: Based on a new generalized minimum variance performance index, a self-tuning controller of multi-input multi-output systems (GMVSTC-MIMO) is proposed in this article to give engineers another alternative of controlling multivariable stochastic processes.
Abstract: Based on a new generalized minimum variance performance index, a kind of generalized minimum variance self-tuning controller of multi-input multi-output systems (GMVSTC-MIMO) is proposed in this paper to give engineers another alternative of controlling multivariable stochastic processes. The design method of the controller is derived and the connections between the controller and the inverse Nyquist array (INA) method are also discussed.
01 Dec 1985
TL;DR: The adaptive controller is a MIMO version of Sampson's LQ controller for SISO discrete-time systems and the feedback gains are computed using an asymptotic-synthesis procedure based on the Riccati difference equation.
Abstract: In this paper we propose an adaptive LQ controller for a MIMO linear time-invariant discrete system. It is assumed that the state-space observer form of the system is stabilizable (not necessarily reachable). The adaptive controller is a MIMO version of Sampson's LQ controller for SISO discrete-time systems [1,2]. The feedback gains are computed using an asymptotic-synthesis procedure based on the Riccati difference equation.