H-infinity Control of DC Motor Part II : Controller Implementation

From H-infinity Control of DC Motor Part I : Plant Modeling and Controller Synthesis, we discuss essential steps required to achieve a controller for DC motor plant, from estimating a plant model by least-square identification, adjusting weighting functions, formulating a generalized plant, synthesizing, and performing controller model reduction. Data captured from real DC motor is provided in the Scilab script dcm_lsid.sce. Before we move on to implementation phase in this part, if you have not done so, I encourage you to try step 1 – 5 in Part I in Scilab by running the scripts in this order: dcm_lsid.sce, kreduced.sci, dcm_hinf_st.sce. You may leave everything untouched on first attempt. When you get the idea how things work, start playing around with weighting functions, adjusting gamma variable, or using the iterative h_inf function in place of hinf.
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H-infinity Control of DC Motor Part I : Plant Modeling and Controller Synthesis

H_\infty is a recent linear control scheme that often sounds intimidating to a new control engineering student. And she might have in her mind already “Geez, this beast must require expensive control design software and run only on state-of-the-art laboratory hardware beyond my reach.” That is a total misconception indeed. I have been using Scilab functions related to robust control for some time and found them professional enough for real applications. I can analyze, synthesize, plot, simulate, and do whatever necessary with this open-source software to make sure that the resulting controller has the desired stability and performance properties. After satisfied, I implement my controller on a hand-wired prototype board that costs less than 50 bucks total. In this 2-part article, I am sharing my experience with you.
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Offline Least-Square System Identification

In certain applications where tight specifications are needed, simple model-free control such as PID may not be adequate. More sophisticated schemes can be customized to the system under control. Linear controllers such as H_2/H_{\infty} are classified as model-based because the design process requires a mathematical model of the plant. Though achieving a perfect model of a real system is not feasible, to be a good enough representation, the model should capture all dominant dynamics, especially the “troublemakers” that might be present.
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Advanced PID Controller Implementation

In this digital era, PID controllers have evolved from basic textbook structure to more sophisticated algorithms. Features such as setpoint/derivative weightings and anti-windup scheme are often added to improve the closed-loop response. In our previous article A Decorated PID Controller, we consider a PID structure with modification and additional functions as follows
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