Abstract
This paper combines system identification, direct data-driven control, and optimization algorithm to design two controllers for one cascade control system, that is, the inner controller and the outer controller. More specifically, when these two controllers in the cascade control system are parameterized by two unknown parameter vectors, respectively, the problem of controller design is changed to parameter identification. To avoid the modeling process for the unknown plants in the cascade control system, a direct data-driven control scheme is proposed to identify those two parameter vectors through minimizing two optimization problems, which do not need any knowledge of the unknown plants. Furthermore, the detailed first-order gradient algorithm is applied to solve our constructed optimization problems, and its convergence property is also analyzed. To extend the above idea to design a nonlinear controller in the cascade control system, a direct data-driven scheme is proposed to get one optimal nonlinear controller, by using some spectral knowledge. Finally, one simulation example of flight simulation is used to prove the efficiency of our proposed direct data-driven control for the cascade control system.
Highlights
E common property among them is that the measured data are used to achieve our main goals; it means some useful information is extracted from these measured data
In case of the unknown but bounded noise, one bounded error identification is proposed to identify the unknown systems with timevarying parameters. en, one feasible parameter set is constructed to include the unknown parameter with a given probability level
In [1], the feasible parameter set is replaced by one confidence interval, as this confidence interval can accurately describe the actual probability that the future predictor will fall into the constructed confidence interval
Summary
Θ0nT. e set of measured data DN u(t), yi(t), y0(t)Nt 1, where u(t) is the control variable, yi(t) is the output of the inner loop, y0(t) is the output of the outer loop, and N is the amount of measured data. Two reference signals corresponding to the inner loop and outer loop are ri(t) and r0(t). In that cascade control system (Figure 1), two reference muo(dt)e,lys iM(t)i(, yq)0(atn)dNt M1 0w(iqth) are given, and set of data u(t) being measured by DN some sensors; the problem is to design those two parameterized controllers Ci(q, θi), C0(q, θ0), while guaranteeing equation (1) holds in case of two unknown plants G(q) and
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