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1. A hybrid predictive control algorithm for tracking in a single-phase DC/AC inverter

   https://doi.org/10.1109/CCTA.2017.8062574  

   Torquati, Luca; Sanfelice, Ricardo G.; Zaccarian, Luca (August 2017, Proceedings of 2017 IEEE Conference on Control Technology and Applications (CCTA))

   In this paper we design a hybrid predictive controller for the tracking of a sinusoidal reference signal. The stability and forward invariance of a set of points around the reference state, named the tracking ellipse, is established by using tools for hybrid dynamical systems. Moreover, prediction of solutions for a finite number of switching events is used to minimize the number of switches. The control algorithm is shown to be robust to small perturbations and input disturbances. Simulations illustrating the main results are included. 

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2. Distributed Finite-Time Event-Triggered Frequency and Voltage Control of AC Microgrids

   https://doi.org/10.1109/TPWRS.2021.3110263  

   Choi, Jeewon; Habibi, Seyed Iman; Bidram, Ali (September 2021, IEEE Transactions on Power Systems)

   null (Ed.)

   This paper proposes a finite-time event-triggered secondary frequency and voltage control for islanded AC microgrids (MGs) in a distributed fashion. The proposed control strategy can effectively perform frequency restoration and voltage regulations, while sharing the active and reactive power among the distributed generators (DGs) based on their power ratings. The finite-time control enables a system to reach consensus in a finite period of time enhanced from the asymptotic convergence. The event-triggered communication is utilized to reduce the communication burden among the DG controllers by transmitting data among DGs if an event-triggering condition is satisfied. The performance of the proposed finite-time event-triggered frequency control is verified utilizing a hardware-in-the-loop experimental testbed which simulates an AC MG in Opal-RT. 

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3. Hybrid GFL-GFM Control for Converter-Dominated Power Systems: A Systematic Survey and Open-Source Implementation

   Zhuang, Zilin; Cui, Hantao (August 2026, PowerUp Conference)

   With the large-scale grid integration of inverter-based resources (IBRs), power systems are rapidly transitioning toward a converter-dominated paradigm. This trend motivates hybrid grid-following (GFL) and grid-forming (GFM) control to combine fast power regulation with improved voltage and frequency support. This paper reviews classical GFL-GFM foundations and provides a structured taxonomy of hybrid GFL-GFM strategies for both converters and systems, including switching-type, dual-source, loop-augmented, and system-level categories. A parallel-branch hybrid architecture, which was recently approved by the Western Electricity Coordinating Council, is then introduced. In this model, the GFL and GFM branches operate concurrently under a coordinating plant controller. Both the REGFMC1 converter control model and the REPCGFM_C1 plant control model have been implemented and made available in the open-source ANDES simulator. Case studies on the Single-Machine Infinite-Bus system and the IEEE 14-bus system validate the effectiveness of the implementation by demonstrating rapid reference tracking and effective voltage and reactive power responses to disturbances. 

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4. Internal models in control, bioengineering, and neuroscience

   Bin.M.; Huang, J.; Isidori, A.; Marconi, L.; Mischiati, M.; E. D. Sontag, E.D. (October 2021, Annual review of control robotics and autonomous systems)

   Cells respond to biochemical and physical internal as well as external signals. These signals can be broadly classified into two categories: (a) ``actionable'' or ``reference'' inputs that should elicit appropriate biological or physical responses such as gene expression or motility, and (b) ``disturbances'' or ``perturbations'' that should be ignored or actively filtered-out. These disturbances might be exogenous, such as binding of nonspecific ligands, or endogenous, such as variations in enzyme concentrations or gene copy numbers. In this context, the term robustness describes the capability to produce appropriate responses to reference inputs while at the same time being insensitive to disturbances. These two objectives often conflict with each other and require delicate design trade-offs. Indeed, natural biological systems use complicated and still poorly understood control strategies in order to finely balance the goals of responsiveness and robustness. A better understanding of such natural strategies remains an important scientific goal in itself and will play a role in the construction of synthetic circuits for therapeutic and biosensing applications. A prototype problem in robustly responding to inputs is that of ``robust tracking'', defined by the requirement that some designated internal quantity (for example, the level of expression of a reporter protein) should faithfully follow an input signal while being insensitive to an appropriate class of perturbations. Control theory predicts that a certain type of motif, called integral feedback, will help achieve this goal, and this motif is, in fact, a necessary feature of any system that exhibits robust tracking. Indeed, integral feedback has always been a key component of electrical and mechanical control systems, at least since the 18th century when James Watt employed the centrifugal governor to regulate steam engines. Motivated by this knowledge, biological engineers have proposed various designs for biomolecular integral feedback control mechanisms. However, practical and quantitatively predictable implementations have proved challenging, in part due to the difficulty in obtaining accurate models of transcription, translation, and resource competition in living cells, and the stochasticity inherent in cellular reactions. These challenges prevent first-principles rational design and parameter optimization. In this work, we exploit the versatility of an Escherichia coli cell-free transcription-translation (TXTL) to accurately design, model and then build, a synthetic biomolecular integral controller that precisely controls the expression of a target gene. To our knowledge, this is the first design of a functioning gene network that achieves the goal of making gene expression track an externally imposed reference level, achieves this goal even in the presence of disturbances, and whose performance quantitatively agrees with mathematical predictions. 

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5. Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow

   https://doi.org/10.23919/ACC45564.2020.9147814  

   Chen, Yue; Bernstein, Andrey; Devraj, Adithya; Meyn, Sean (January 2020, American Control Conference)

   This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit knowledge of the system model. The problem is modeled as a dynamic optimization problem with time-varying performance objectives and engineering constraints. The design of the algorithms leverages the online zero-order primal-dual projected-gradient method. In particular, the primal step that involves the gradient of the objective function (and hence requires a networked systems model) is replaced by its zero-order approximation with two function evaluations using a deterministic perturbation signal. The evaluations are performed using the measurements of the system output, hence giving rise to a feedback interconnection, with the optimization algorithm serving as a feedback controller. The paper provides some insights on the stability and tracking properties of this interconnection. Finally, the paper applies this methodology to a real-time optimal power flow problem in power systems, and shows its efficacy on the IEEE 37-node distribution test feeder for reference power tracking and voltage regulation. 

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