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1. Model reference adaptive anti-windup compensation

   Sofrony, Jorge ; Turner, Matthew ; Richards, Christopher ( December 2022 , Proceedings of the IEEE Conference on Decision Control)

   This paper proposes an anti-windup mechanism for a model reference adaptive control (MRAC) scheme subject to actuator saturation constraints. The proposed compensator has the same architecture as well known non-adaptive schemes, which rely on the assumption that the system model is known fairly accurately. This is in contrast to the adaptive nature of the controller, which assumes that the system (or parts of it) is unknown. The approach proposed here uses of an “estimate” of the system matrices for the anti-windup compensator formulation and modifies the adaptation laws that update the controller gains. It will be observed that if the (unknown) ideal control gain is reached, a type of “model recovery antiwindup” formulation is obtained. In addition, it is shown that if the ideal control signal eventually lies within the control constraints, then, under certain conditions, the system states will converge to those of the reference model as desired. The paper highlights the main challenges involved in the design of anti-windup compensators for model-reference adaptive control systems and demonstrates its success via a simulation of a flight control application. 

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2. Anti-windup Compensation for Stable and Unstable Systems with Quantized and Saturated Inputs

   https://doi.org/10.23919/ACC55779.2023.10156408  

   Richards, Christopher M. ; Turner, Matthew C. ( May 2023 , Proceedings of the American Control Conference)

   It is well known that actuator saturation can cause destabilization and degradation in performance; similar problems are faced when actuation is quantized. This paper proposes the design of an anti-windup compensator for systems with actuators that are limited to a finite number of quantization levels. This combination of discrete level actuation and saturation poses a unique anti-windup problem that has not yet been solved. To surmount this combined issue, an anti-windup compensator is proposed which provides ultimateboundedness of the system state within a prescribed region, and also guarantees that the state does not stray outside a larger compact set. A numerical simulation example illustrates the effectiveness on a rigid-body system which inspired this work. 

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3. Working Memory Augmentation for Improved Learning in Neural Adaptive Control

   https://doi.org/10.1109/CDC40024.2019.9029549  

   Muthirayan, Deepan ; Khargonekar, Pramod P. ( December 2019 , 2019 IEEE Conference on Decision and Control)

   In this paper, we propose a novel control architecture, inspired from neuroscience, for adaptive control of continuous time systems. The objective here is to design control architectures and algorithms that can learn and adapt quickly to changes that are even abrupt. The proposed architecture, in the setting of standard neural network (NN) based adaptive control, augments an external working memory to the NN. The learning system stores, in its external working memory, recently observed feature vectors from the hidden layer of the NN that are relevant and forgets the older irrelevant values. It retrieves relevant vectors from the working memory to modify the final control signal generated by the controller. The use of external working memory improves the context inducing the learning system to search in a particular direction. This directed learning allows the learning system to find a good approximation of the unknown function even after abrupt changes quickly. We consider two classes of controllers for illustration of our ideas (i) a model reference NN adaptive controller for linear systems with matched uncertainty (ii) backstepping NN controller for strict feedback systems. Through extensive simulations and specific metrics we show that memory augmentation improves learning significantly even when the system undergoes sudden changes. Importantly, we also provide evidence for the proposed mechanism by which this specific memory augmentation improves learning. 

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4. Frequency regulation for microgrid using genetic algorithm and particle swarm optimization tuned STATCOM

   https://doi.org/10.1002/cta.3319  

   Saxena, Nitin Kumar ; Gao, Wenzhong D. ; Kumar, Ashwani ; Mekhilef, Saad ; Gupta, Varun ( May 2022 , International Journal of Circuit Theory and Applications)

   This paper presents the genetic algorithm (GA) and particle swarm optimization (PSO) based frequency regulation for a wind‐based microgrid (MG) using reactive power balance loop. MG, operating from squirrel cage induction generator (SCIG), is employed for exporting the electrical power from wind turbines, and it needs reactive power which may be imported from the grid. Additional reactive power is also required from the grid for the load, directly coupled with such a distributed generator (DG) plant. However, guidelines issued by electric authorities encourage MGs to arrange their own reactive power because such reactive power procurement is defined as a local area problem for power system studies. Despite the higher cost of compensation, static synchronous compensator (STATCOM) is a fast‐acting FACTs device for attending to these reactive power mismatches. Reactive power control can be achieved by controlling reactive current through the STATCOM. This can be achieved with modification in current controller scheme of STATCOM. STATCOM current controller is designed with reactive power load balance for the proposed microgrid in this paper. Further, gain values of the PI controller, required in the STATCOM model, are selected first with classical methods. In this classical method, iterative procedures which are based on integral square error (ISE), integral absolute error (IAE), and integral square of time error (ISTE) criteria are developed using MATLAB programs. System performances are further investigated with GA and PSO based control techniques and their acceptability over classical methods is diagnosed. Results in terms of converter frequency deviation show how the frequency remains under the operating boundaries as allowed by IEEE standards 1159:1995 and 1250:2011 for integrating renewable‐based microgrid with grid. Real and reactive power management and load current total harmonic distortions verify the STATCOM performance in MG. The results are further validated with the help of recent papers in which frequency regulation is investigated for almost similar power system models. The compendium for this work is as following: (i) modelling of wind generator‐based microgrid using MATLAB simulink library, (ii) designing of STATCOM current controller with PI controller, (iii) gain constants estimation using classical, GA and PSO algorithm through a developed m codes and their interfacing with proposed simulink model, (v) dynamic frequency responses for proposed grid connected microgrid during starting and load perturbations, (vi) verification of system performance with the help of obtained real and reactive power management between STATCOM and grid, and (vii) validation of results with available literature.

    

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5. Adaptive NN-Based Boundary Control for Output Tracking of A Wave Equation with Matched and Unmatched Boundary Uncertainties

   https://doi.org/10.23919/ACC53348.2022.9867897  

   Zhang, Jingting ; Stegagno, Paolo ; Zeng, Wei ; Yuan, Chengzhi ( June 2022 , Proceedings of the American Control Conference)

   This paper is focused on the output tracking control problem of a wave equation with both matched and unmatched boundary uncertainties. An adaptive boundary feedback control scheme is proposed by utilizing radial basis function neural networks (RBF NNs) to deal with the effect of system uncertainties. Specifically, two RBF NN models are first developed to approximate the matched and unmatched system uncertain dynamics respectively. Based on this, an adaptive NN control scheme is derived, which consists of: (i) an adaptive boundary feedback controller embedded by the NN model approximating the matched uncertainty, for rendering stable and accurate tracking control; and (ii) a reference model embedded by the NN model approximating the unmatched uncertainty, for generating a prescribed reference trajectory. Rigorous analysis is performed using the Lyapunov theory and the C0-semigroup theory to prove that our proposed control scheme can guarantee closed-loop stability and wellposedness. Simulation study has been conducted to demonstrate effectiveness of the proposed approach. 

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