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1. Fast and Secure Operation of Voltage Source Inverter based DERs using Model Predictive Droop Control

   https://doi.org/10.1109/ECCE.2018.8558323  

   Alhosaini, Waleed; Mahmud, Mohammad Hazzaz; Zhao, Yue (September 2018, Fast and Secure Operation of VSI based DERs using Model Predictive Droop Control)

   Reliability of the power grid can be improved by the use of microgrids (MGs) concept, which regulates the voltage and frequency at the point of common coupling (PCC) during normal and/or faulty conditions. Droop characteristics based hierarchical control strategies are commonly used in MGs, where power converters can operate in parallel. However, the need of multiple control loops not only adds complexity to the controller design, but also reduces the dynamic response of the system. In the future power system, grid-tied converters with fast dynamic response are desired to handle the uncertainties induced by high penetration of distributed energy resources. Therefore, this paper presents a novel model predictive control to ensure fast dynamic response of high power three-level converters in stand-alone operating mode as well as grid-tied operating mode. The proposed controller is applied to a MG which consists of a solar inverter connected in parallel with an energy storage system to the PCC, where a local load is tied. Both simulation and experimental results are presented to demonstrate robustness and the high dynamic performance of the proposed controller under rapidly changing atmospheric conditions and different grid operating modes. 

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2. A hybrid stochastic-deterministic approach to explore multiple infection and evolution in HIV

   https://doi.org/10.1371/journal.pcbi.1009713  

   Kreger, Jesse; Komarova, Natalia L.; Wodarz, Dominik (December 2021, PLOS Computational Biology)

   Pascual, Mercedes (Ed.)

   To study viral evolutionary processes within patients, mathematical models have been instrumental. Yet, the need for stochastic simulations of minority mutant dynamics can pose computational challenges, especially in heterogeneous systems where very large and very small sub-populations coexist. Here, we describe a hybrid stochastic-deterministic algorithm to simulate mutant evolution in large viral populations, such as acute HIV-1 infection, and further include the multiple infection of cells. We demonstrate that the hybrid method can approximate the fully stochastic dynamics with sufficient accuracy at a fraction of the computational time, and quantify evolutionary end points that cannot be expressed by deterministic models, such as the mutant distribution or the probability of mutant existence at a given infected cell population size. We apply this method to study the role of multiple infection and intracellular interactions among different virus strains (such as complementation and interference) for mutant evolution. Multiple infection is predicted to increase the number of mutants at a given infected cell population size, due to a larger number of infection events. We further find that viral complementation can significantly enhance the spread of disadvantageous mutants, but only in select circumstances: it requires the occurrence of direct cell-to-cell transmission through virological synapses, as well as a substantial fitness disadvantage of the mutant, most likely corresponding to defective virus particles. This, however, likely has strong biological consequences because defective viruses can carry genetic diversity that can be incorporated into functional virus genomes via recombination. Through this mechanism, synaptic transmission in HIV might promote virus evolvability. 

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3. Adaptive Particle Filtering for Fault Detection in Partially-Observed Boolean Dynamical Systems

   https://doi.org/10.1109/TCBB.2018.2880234  

   Bahadorinejad, Arghavan; Imani, Mahdi; Braga-Neto, Ulisses (November 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics)

   We propose a novel methodology for fault detection and diagnosis in partially-observed Boolean dynamical systems (POBDS). These are stochastic, highly nonlinear, and derivative- less systems, rendering difficult the application of classical fault detection and diagnosis methods. The methodology comprises two main approaches. The first addresses the case when the normal mode of operation is known but not the fault modes. It applies an innovations filter (IF) to detect deviations from the nominal normal mode of operation. The second approach is applicable when the set of possible fault models is finite and known, in which case we employ a multiple model adaptive estimation (MMAE) approach based on a likelihood-ratio (LR) statistic. Unknown system parameters are estimated by an adaptive expectation- maximization (EM) algorithm. Particle filtering techniques are used to reduce the computational complexity in the case of systems with large state-spaces. The efficacy of the proposed methodology is demonstrated by numerical experiments with a large gene regulatory network (GRN) with stuck-at faults observed through a single noisy time series of RNA-seq gene expression measurements. 

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4. Robust walking control of a lower limb rehabilitation exoskeleton coupled with a musculoskeletal model via deep reinforcement learning

   https://doi.org/10.1186/s12984-023-01147-2  

   Luo, Shuzhen; Androwis, Ghaith; Adamovich, Sergei; Nunez, Erick; Su, Hao; Zhou, Xianlian (December 2023, Journal of NeuroEngineering and Rehabilitation)

   Abstract Background Few studies have systematically investigated robust controllers for lower limb rehabilitation exoskeletons (LLREs) that can safely and effectively assist users with a variety of neuromuscular disorders to walk with full autonomy. One of the key challenges for developing such a robust controller is to handle different degrees of uncertain human-exoskeleton interaction forces from the patients. Consequently, conventional walking controllers either are patient-condition specific or involve tuning of many control parameters, which could behave unreliably and even fail to maintain balance. Methods We present a novel, deep neural network, reinforcement learning-based robust controller for a LLRE based on a decoupled offline human-exoskeleton simulation training with three independent networks, which aims to provide reliable walking assistance against various and uncertain human-exoskeleton interaction forces. The exoskeleton controller is driven by a neural network control policy that acts on a stream of the LLRE’s proprioceptive signals, including joint kinematic states, and subsequently predicts real-time position control targets for the actuated joints. To handle uncertain human interaction forces, the control policy is trained intentionally with an integrated human musculoskeletal model and realistic human-exoskeleton interaction forces. Two other neural networks are connected with the control policy network to predict the interaction forces and muscle coordination. To further increase the robustness of the control policy to different human conditions, we employ domain randomization during training that includes not only randomization of exoskeleton dynamics properties but, more importantly, randomization of human muscle strength to simulate the variability of the patient’s disability. Through this decoupled deep reinforcement learning framework, the trained controller of LLREs is able to provide reliable walking assistance to patients with different degrees of neuromuscular disorders without any control parameter tuning. Results and conclusion A universal, RL-based walking controller is trained and virtually tested on a LLRE system to verify its effectiveness and robustness in assisting users with different disabilities such as passive muscles (quadriplegic), muscle weakness, or hemiplegic conditions without any control parameter tuning. Analysis of the RMSE for joint tracking, CoP-based stability, and gait symmetry shows the effectiveness of the controller. An ablation study also demonstrates the strong robustness of the control policy under large exoskeleton dynamic property ranges and various human-exoskeleton interaction forces. The decoupled network structure allows us to isolate the LLRE control policy network for testing and sim-to-real transfer since it uses only proprioception information of the LLRE (joint sensory state) as the input. Furthermore, the controller is shown to be able to handle different patient conditions without the need for patient-specific control parameter tuning. 

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5. New Compound Fractional Sliding Mode Control and Super-Twisting Control of a MEMS Gyroscope

   https://doi.org/10.1115/1.4055878  

   Rahmani, Mehran; Redkar, Sangram (October 2022, ASME Letters in Dynamic Systems and Control)

   Abstract This research proposes a new compound fractional sliding mode control (FOSMC) and super-twisting control (FOSMC + STC) to control a microelectromechanical systems gyroscope. A new sliding mode surface has been defined to design the proposed new sliding mode controller. The main advantages of a FOSMC are its high tracking performance and robustness against external perturbation, but creating a chattering phenomenon is its main drawback. By applying a super-twisting control (STC) method with FOSMC, the chattering phenomenon is eliminated, the singularity problem is solved, and systems robustness has significantly improved. Simulation results validate the effectiveness of the proposed control approach. 

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