Browsing by Author "Alli-Oke, Razak Olusegun"

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    A Reference-Optimizing Antiwindup Control for Input-Constrained Systems
    (IEEE Conference on Control, Decision and Information Technologies, 2019) Alli-Oke, Razak Olusegun
    Control limits due to saturation constraints may result in the directionality problem in addition to the controller windup effect. In solving the directionality problem, this paper explores the concept of modifying the reference signal to be an element of the maximal output admissible set of the closedloop dynamics. This is achieved by an online constrained optimization of a tracking-related cost function associated with the closed-loop system. The design procedure for this reference-optimizing directional compensator is applicable to most existing antiwindup schemes. The effectiveness of the proposed control structure is demonstrated via simulation of benchmark case study examples.
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    ROBUSTNESS and OPTIMIZATION IN ANTI-WINDUP CONTROL
    (2014) Alli-Oke, Razak Olusegun
    This thesis is broadly concerned with online-optimizing anti-windup control. These are control structures that implement some online-optimization routines to compensate for the windup effects in constrained control systems. The first part of this thesis examines a general framework for analyzing robust preservation in anti-windup control systems. This framework - the robust Kalman conjecture - is defined for the robust Lur’e problem. This part of the thesis verifies this conjecture for first-order plants perturbed by various norm-bounded unstruc- tured uncertainties. Integral quadratic constraint theory is exploited to classify the appropriate stability multipliers required for verification in these cases. The remaining part of the thesis focusses on accelerated gradient methods. In particular, tight complexity-certificates can be obtained for the Nesterov gradient method, which makes it attractive for implementation of online-optimizing anti-windup control. This part of the thesis presents a proposed algorithm that extends the classical Nesterov gradient method by using available secant inform- ation. Numerical results demonstrating the efficiency of the proposed algorithm are analysed with the aid of performance profiles. As the objective function be- comes more ill-conditioned, the proposed algorithm becomes significantly more efficient than the classical Nesterov gradient method. The improved performance bodes well for online-optimization anti-windup control since ill-conditioning is common place in constrained control systems. In addition, this thesis explores another subcategory of accelerated gradient methods known as Barzilai-Borwein gradient methods. Here, two algorithms that modify the Barzilai-Borwein gradient method are proposed. Global conver- gence of the proposed algorithms for all convex functions are established by using discrete Lyapunov theorems.