Windup refers to the phenomenon where a control system operates in a nonlinear region when the controller's output exceeds the input limits of the plant being controlled. Windup can lead to performance degradation in terms of overshoot, settling time and even system stability. Many anti‐windup strategies involve switching and manipulating the integral control component in various ways when saturation occurs aiming to bring control back into the linear region. For better insight into windup, the proportional–integral (PI) plane is now used as a means to explain the phenomenon in terms of the controller's signals. A PI controller with a built‐in closed‐loop integral controller that has a reference set based on the input command and external torque is proposed. The performance for this proposed method is compared against existing conditional integration, tracking back calculation and integral state prediction schemes on second and third order systems using MATLAB/SIMULINK simulations of an induction motor and a DC motor respectively. The proposed controller showed promising potential with its ability to eliminate overshoot in both no load and full load conditions due to the decoupling of its parameters from its response and has the shortest settling time when compared against existing schemes, even in the presence of noise.

The output of the controller is said to exceed the input limits of the plant being controlled when a control system operates in a non-linear region. This process is called the windup phenomenon. The windup phenomenon is not preferable in the control system because it leads to performance degradation, such as overshoot and system instability. Many anti-windup strategies involve switching, where the integral component differently operates between the linear and the non-linear states. The range of state for the non-overshoot performance is better illustrated by the boundary integral error plane than the proportional-integral (PI) plane in windup inspection. This study proposes a PI controller with a separate closed-loop integral controller and reference value set with respect to the input command and external torque. The PI controller is compared with existing conventional proportional integral, conditional integration, tracking back calculation, and integral state prediction schemes by using ScicosLab simulations. The controller is also experimentally verified on a direct current motor under no-load and loading conditions. The proposed controller shows a promising potential with its ability to eliminate overshoot with short settling time using the decoupling mode in both conditions.

A working example of relative solvent accessibility (RSA) prediction for proteins is presented. Novel logistic regression models with various qualitative descriptors that include amino acid type and quantitative descriptors that include 20-and six-term sequence entropy have been built and validated. A domain-complete learning set of over 1300 proteins is used to fit initial models with various sequence homology descriptors as well as query residue qualitative descriptors. Homology descriptors are derived from BLASTp sequence alignments, whereas the RSA values are determined directly from the crystal structure. The logistic regression models are fitted using dichotomous responses indicating buried or accessible solvent, with binary classifications obtained from the RSA values. The fitted models determine binary predictions of residue solvent accessibility with accuracies comparable to other less computationally intensive methods using the standard RSA threshold criteria 20 and 25% as solvent accessible. When an additional non-homology descriptor describing Lobanov-Galzitskaya residue disorder propensity is included, incremental improvements in accuracy are achieved with 25% threshold accuracies of 76.12 and 74.79% for the Manesh-215 and CASP(8+9) test sets, respectively. Moreover, the described software and the accompanying learning and validation sets allow students and researchers to explore the utility of RSA prediction with simple, physically intuitive models in any number of related applications.

No abstract

No abstract