Conference Paper

Checking if controllers are stabilizing using closed-loop data

Res. Sch. of Inf. Sci. & Eng., Australian Nat. Univ., Canberra, ACT
DOI: 10.1109/CDC.2006.377549 Conference: Decision and Control, 2006 45th IEEE Conference on
Source: IEEE Xplore

ABSTRACT Suppose an unknown plant is stabilized by a known controller. Suppose also that some knowledge of the closed-loop system is available and on the basis of that knowledge, the use of a new controller appears attractive, as may arise in iterative control and identification algorithms, and multiple-model adaptive control. The paper presents tests using a limited amount of experimental data obtained with the existing known controller for verifying that introduction of the new controller will stabilize the plant

0 Bookmarks
 · 
81 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents a new control design approach for unknown SISO systems by using measurements. In control approaches existing in the literature, controllers are usually designed on the basis of mathematical models obtained by either using physical laws or via identification system using a set of measured data. However, due to the complex dynamics of real systems, such parametric models are only an approximation obtained after some simplifying assumptions. Therefore, the design of controllers based on a simplified model leads to a degradation in the expected performance for the closed-loop system. Our proposed approach is based on measurements to directly design controllers without going through the use of mathematical models. The principle of the proposed control methodology is to design fixed-structure controllers for which the error modulus between the closed-loop frequency response and a desired frequency response is bounded by given quantity. This problem is formulated as a nonlinear programming problem based on inequality constraints. The main advantage of our proposed approach is that the controller design is based only on a set of measurements, which allows to avoid errors associated with the identification process. Moreover, with such a proposed control method, it is guaranteed that the error between the computed and desired closed-loop frequency responses is less than a small quantity. Another feature of the proposed technique is that the structure of the controller can be selected a priori, which allows to design low-order controllers. A simulation application to measurement-based controller design for DC servomotors is presented to validate and illustrate the efficacy of the proposed approach.
    American Control Conference (ACC), 2013; 06/2013
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper deals with fixed-structure controller design for stable linear systems by using measurements. Most control design approaches developed in the literature are generally based on a mathematical model which can be obtained via identification system by using a set of measured data. However, an identified model, which is often built on the basis of some assumptions, cannot perfectly describe complex behaviors characterizing physical systems. Thus, the performance expected for the closed-loop system will be limited by the quality of such models used in the control design process. Hence, data-based controller design methods can be viewed as a possible alternative to model-based methods. In this paper, we propose to directly utilize frequency response data in the controller design. The principle is to design fixed-structure controllers for which the closed-loop frequency response fits a desired frequency response. This problem is formulated as an error minimization problem. The main feature of our proposed approach is that controller can be designed free of any mathematical model, which allows to avoid errors associated with identification process. Moreover, it enables to select low-order controllers, which are suitable for embedded systems. A simulation example is given to illustrate and validate the efficacy the proposed approach.
    Control Conference (ASCC), 2013 9th Asian; 01/2013
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper proposes the data-driven performance improvement of low-cost control systems (CSs) for vertical three-tank systems. The MIMO CSs dedicated to two tanks of the three-tank systems consist of two SISO control loops with separately tuned PI controllers. The Modulus Optimum method is applied to initially tune the PI controllers. Optimization problems are defined on the basis of an original objective function which depends on the controller tuning parameters and is expressed as the sum of squared output errors multiplied by variable weights. The performance improvement is achieved by a new convergent Iterative Feedback Tuning (IFT) algorithm which aims the parameter tuning of PI controllers by the experiment-based solving of the optimization problems. The convergence is ensured by the formulation of the parameter update laws in the IFT algorithm as a nonlinear dynamical feedback system in the parameter space and iteration domain and by setting the step sizes to fulfill inequality-type convergence conditions derived from Popov's hyperstability theory. The experimental results for a laboratory vertical three-tank system show the convincing CS performance improvement by few experiments.
    Human System Interaction (HSI), 2013 The 6th International Conference on; 01/2013

Full-text

Download
0 Downloads
Available from