AI-driven closed-loop extrusion-based bioprinting system: from dynamic regulation to medical translation

An augmented and minimum realization state space models of a two input two output plant are proposed for direct derivation of the discrete-time linear-quadratic regulator (DLQR) with measured not all the state variables but only the plant outputs. Both the models are related by means of original transformation based on the system modes. Using this transformation it is shown that the resulting closed-loop (CL) system with dynamic output feedback regulator (DOFR) has the same stable roots of its characteristic equation as the CL with state feedback regulator. It is shown that the observer appearing in DOFR is optimal and dead-beat. It is also shown that the CL system with DOFR realizes the optimal LQ control of the system with feedback from the augmented state.

Extrusion-based bio-printing has great potential as a technique for manipulating biomaterials and living cells to create three-dimensional (3D) scaffolds for damaged tissue repair and function restoration. Over the last two decades, advances in both engineering techniques and life sciences have evolved extrusion-based bio-printing from a simple technique to one able to create diverse tissue scaffolds from a wide range of biomaterials and cell types. However, the complexities associated with synthesis of materials for bio-printing and manipulation of multiple materials and cells in bio-printing pose many challenges for scaffold fabrication. This paper presents an overview of extrusion-based bio-printing for scaffold fabrication, focusing on the prior-printing considerations (such as scaffold design and materials/cell synthesis), working principles, comparison to other techniques, and to-date achievements. This paper also briefly reviews the recent development of strategies with regard to hydrogel synthesis, multi-materials/cells manipulation, and process-induced cell damage in extrusion-based bio-printing. The key issue and challenges for extrusion-based bio-printing are also identified and discussed along with recommendations for future, aimed at developing novel biomaterials and bio-printing systems, creating patterned vascular networks within scaffolds, and preserving the cell viability and functions in scaffold bio-printing. The address of these challenges will significantly enhance the capability of extrusion-based bio-printing.

Z.W. received a postdoctoral fellowship from Eli Lilly Canada Inc. and a postdoctoral fellowship from Fonds de Recherche du Québec-Santé (FRQS). M.L. and N.T. have no conflicts of interest. L.L., V.M. and R.R.L. received purchase fees from Eli Lilly & Co. L.L. received consulting fees from Abbott, Dexcom and Insulet, and received (to institution) research funding from Merck and AstraZeneca. R.R.L. has received research grants from AstraZeneca, Eli Lilly, Merck, Novo- Nordisk, Sanofi-Aventis and Vertex Pharmaceutical, has been a consultant or member on advisory panels for Abbott, Amgen, Astra-Zeneca, Bayer, Carlina Technology, Dexcom, Eli Lilly, HSL Therapeutics, Insulet, Janssen, Medtronic, Merck, Novo-Nordisk, Pfizer and Sanofi-Aventis, and has received honoraria for conferences by Abbott, Astra-Zeneca, CMS Canadian Medical & Surgical Knowledge Translation Research group, CPD Network, Dexcom, Eli Lilly, Janssen, Medtronic, Merck, Novo-Nordisk, Sanofi-Aventis and Vertex Pharmaceutical. R.R.L. also benefits from unrestricted grants for clinical and educational activities from Abbott, Eli Lilly, Medtronic, Merck, Novo Nordisk and Sanofi-Aventis, and holds intellectual property in the field of type 2 diabetes risk biomarkers, catheter life and the closed-loop system. The peer review history for this article is available at https://publons.com/publon/10.1111/dom.14850. The data that support the findings of this study are available from the corresponding author upon reasonable request Figure S1. Illustration of the pooled analysis and studies included Figure S2. Histogram (mean) of glucagon delivery profile during dual-hormone automated insulin delivery intervention in children and adolescents with type 1 diabetes (N = 123 nights) Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Bioprinting is the technology that combines the use of living matter and biomaterials to manufacture biological models, tissues, and structures layer by layer for applications in regenerative medicine, drug testing, and tissue engineering. Among bioprinting techniques, extrusion-based methods are the most widely used because of their relative simplicity, affordability, and ability to handle as wide range of biomaterials, including those with high viscosities. However, achieving consistent print quality remains a challenge, as the rheological properties of bioinks are highly variable and sensitive to environmental factors such as temperature. A critical aspect of print quality is maintaining a consistent and predictable line width, as pre-programmed trajectories and design fidelity rely on this parameter being well controlled. This work introduces a closed-loop control system for Extrusion-Based Bioprinting (EBB), utilizing real-time computer vision. The system employs a camera that is placed to monitor the line width immediately after extrusion, enabling real-time feedback to adjust the feedrate of the extrusion mechanism. This approach ensures consistent line widths across a wide range of materials and conditions, addressing the variability that traditionally hampers EBB. The method was validated using a Pluronic hydrogel, achieving closed-loop control over a wide range of target line widths. These findings demonstrate the potential for automated, robust bioprinting with improved reproducibility and precision, advancing the reliability of this technology for biomedical applications.

Three-dimensional (3D) bioprinting is an emerging technology, which turned out to be an optimal tool for tissue engineering approaches. To date, different printing systems have been developed. Among them, the extrusion-based approach demonstrated to be the most suitable for skeletal muscle tissue engineering, due to its ability to produce and deposit printing fibers in a parallel pattern that well mimic the native skeletal muscle tissue architecture. In tissue bioengineering, a key role is played by biomaterials, which must possess the key requisite of ‘printability’. Nevertheless, this feature is not often well correlated with cell requirements, such as motives for cellular adhesion and/or absorbability. To overcome this hurdle, several efforts have been made to obtain an effective bioink by combining two different biomaterials in order to reach a good printability besides a suitable biological activity. However, despite being efficient, this strategy reveals several outcomes limitations. We report here the development and characterization of a novel extrusion-based 3D bioprinting system, and its application for correction of volumetric muscle loss (VML) injury in a mouse model. The developed bioprinting system is based on the use of PEG-Fibrinogen, a unique biomaterial with excellent biocompatibility, well-suited for skeletal muscle tissue engineering. With this approach, we obtained highly organized 3D constructs, in which murine muscle progenitors were able to differentiate into muscle fibers arranged in aligned bundles and capable of spontaneously contracting when cultured in vitro. Furthermore, to evaluate the potential of the developed system in future regenerative medicine applications, bioprinted constructs laden with either murine or human muscle progenitors were transplanted to regenerate the Tibialis Anterior muscle of a VML murine model, one month after grafting.

The integration of real-time electroencephalogram (EEG) workload indices into the man-machine interface could greatly enhance performance of complex tasks, transforming traditionally passive human-system interaction (HSI) into an active exchange where physiological indicators adjust the interaction to suit a user’s engagement level. The envisioned outcome is a closed-loop system that utilizes EEG and other physiological indices for dynamic regulation and optimization of HSI in real-time. As a first step towards a closed-loop system, five individuals performed as identification supervisors (IDSs) in an Aegis command and control (C2) simulated environment, a combat system with advanced, automatic detect-and-track, multi-function phased array radar. The Aegis task involved monitoring multiple data sources (i.e., missile-tracks, alerts, queries, resources), detecting required actions, responding appropriately, and ensuring system status remains within desired parameters. During task operation, a preliminary workload measure calculated in real-time for each second of EEG and was used to manipulate the Aegis task demands. In post-hoc analysis, the use of a five-level workload measure to detect cognitively challenging events was evaluated. Events in decreasing order of difficulty were track selection-identification, alert-responses, hooking-tracks, and queries. High/extreme EEG-workload occurred during high cognitive-load tasks with a detection efficiency approaching 100% for selection-identification and alert-responses, 77% for hooking-tracks and 70% for queries. Over 95% of high/extreme EEG-workload across participants occurred during high-difficulty events (false positive rate < 5%). The high/extreme workload occurred between 25-30% of time. These results suggest an intelligent closed-loop system incorporating EEG-workload measures could be designed to re-allocate tasks and aid in efficiently streamlining a user’s cognitive workload. Such an approach could ensure the operator remains uninterrupted during high/extreme workload periods, thereby resulting in increased productivity and reduced errors.

This study investigates dynamic energy price regulation by a closed-loop fractional-order PI control system and presents a possible application for the automated energy balancing in smart grid energy markets. A persistent balance of energy demand and generation is a substantial problem for future smart grids due to the uncertainty and high fluctuation in the generation of distributed renewable energy sources and elastic demand conditions. Dynamic energy pricing is an effective strategy to balance flexible energy markets and it can provide the best energy price that balances energy demand and generation. We numerically demonstrate that closed-loop generation control by using dynamic pricing can provide persistent settling to the best price point of demand and supply curves, when the energy balance error is defined as the difference between instant demand and generation potentials. We analyse energy market management performance of a fractional-order PI controller in the case of communication and operation delays in a multi-source energy market model. Market simulation results are discussed to demonstrate the possible advantages of the fractional-order PI controller for smart grid energy market managements.

Two state space models are proposed for direct implementation of linear-quadratic regulator with measured not all the state components but only the output of the plant. Using this approach the closed-loop (CL) system with dynamic output feedback regulator (DOFR) is designed. A theorem is proved relating the solutions obtained from the two models. It is shown that, resulting from the second model, the CL systems with the state feedback and with DOFR have the same dynamics and strong robustness property. It is also shown that, by means of appropriate choice of the state weighting matrix in the performance index of the second model, it is possible to obtain a partial pole placement. The internal model of disturbances included to the augmented plant during the design and to the regulator during control implementation, improves the quality of the control.

This paper investigates the problem of model matching with strong stability for hybrid linear systems subject to periodic state jumps. The system and the model share the same hybrid time domain: i.e., they exhibit a continuous-time behavior except at certain isolated time instants, where their state shows abrupt discontinuities. The set of the admissible jump time sequences consists of the sequences of equally-spaced time instants whose period is no smaller than a positive real constant. The compensation scheme includes a hybrid dynamic regulator, defined over the same time domain, and a static output-feedback gain. The problem is to determine the hybrid regulator, the feedback gain and the positive time constant in such a way that: i) the forced response of the closed-loop hybrid system matches that of the hybrid model for all the admissible inputs and for all the admissible jump time sequences; ii) both the closed-loop hybrid system and the hybrid regulator are globally asymptotically stable for all the admissible jump time sequences. The existence of a solution to the stated problem is characterized by a necessary and sufficient condition.

In this paper, from a nonlinear model for a pendulum on a cart system, which considers viscous friction, a two rules Takagi-Sugeno (T-S) fuzzy model is proposed using local approximation in fuzzy partition spaces approach. A Parallel Distributed Compensator (PDC), where feedback gains for the local linear controllers are obtained via Linear Matrix Inequalities (LMI) technique, guaranteeing global stability in the closed-loop system is also proposed. Moreover, a T-S fuzzy observer, using the separation principle from linear systems theory, is designed in order to estimate both cart and pendulum velocities. The so-called swing-up technique is used to swing the pendulum up from its pendant position to upright position. Numerical simulation and real-time experiments validate the effectiveness of our control scheme.

The negative impedance characteristics of a constant power load (CPL) can easily lead to the instability of the DC bus voltage. To improve the stability of the DC bus voltage, an adaptive backstepping sliding mode control strategy for a boost converter with the CPL in DC microgrid is proposed. First, to carry out the backstepping control, the zero dynamic stability of the system under different output functions is studied by using input-output exact feedback linearization theory. The model is transformed into a linear system in Brunovsky canonical form, which solves the nonlinear problem caused by the CPL and the non-minimum phase problem of the boost converter. Then, under the premise of ensuring large signal stability, an adaptive mechanism is introduced into the design of the backstepping sliding mode control. The adaptive backstepping sliding mode controller is designed by adaptively updating the switching gain in real time. Furthermore, the Lyapunov theory is used to prove the global asymptotic stability of the overall closed-loop system. Finally, the numerical simulation and experimental results show that the proposed control strategy has better dynamic regulation performance and stronger robustness compared with the conventional double closed-loop PI control method.

This chapter investigates output regulation problem of state-coupled linear certain and uncertain multi-agent systems with globally reachable topologies. Distributed dynamic state feedback control law is introduced to realize the regulator problem, and a general global method for error regulation is established. The Jordan canonical form is used to stabilize the closed-loop control system. Sylvester equation and internal model theory are adopted to achieve the objectives of output regulation for every initial condition in the state space.

The paper presents the method of synergetic synthesis of a nonlinear discrete control system for induction motors. The proposed approach is based on using the methods of the synergetic control theory and nonlinear mathematical models of induction motors. This approach allows to create nonlinear closed-loop piecewise-continuous control systems which guarantee the asymptotic stability of the controlled induction motors, accomplishment of the determined technological and electromagnetic invariants and selective invariance to the unknown external disturbances acting on the system. The invariance of the designed systems is ensured due to the usage of a dynamic discrete regulator which increases the astatism of the synthesized system.

Optimal efficiency dynamic mode regulation in pulse-frequency modulation in voltage converters is considered in this work. Time-delay feedback control is used for solving this problem. The subject relevance is due to the fact that a large number of works have been devoted to using time-delay feedback control in electrical energy conversion systems with pulse-width modulation, and no attention has been paid to the systems with pulse-frequency modulation. Closed-loop automatic control system of buck converter with stabilization desired dynamic modes which increases quality of output voltage is presented. A mathematical description has been developed for calculating electromagnetic processes in the system under consideration in the form of the Poincare mapping. The results of mathematical modeling which shows efficiency of proposed technical solution are presented. Time-delay feedback control system application allowed to exclude undesired dynamic modes under changing system parameters which provided high quality of output voltage.The results presented in the article were obtained for the first time and are important for practice.

This paper presents an alternative technique for the adaptive control of power electronic converter circuits. Specific attention is given to the adaptive control of a dc-dc converter. The proposed technique is based on a simple adaptive filter method and uses a one-tap finite impulse response (FIR) prediction error filter (PEF). The method is computationally efficient and based around a dichotomous coordinate descent (DCD) algorithm. The DCD-recursive least squares (RLS) algorithm has been employed as the adaptive PEF to reduce the computational complexity of existing RLS algorithms for efficient hardware implementation. Results show that the DCD-RLS is able to improve the dynamic performance and convergence rate of the adaptive gains (filter taps) within the controller. In turn, this yields a significant improvement in the overall dynamic performance of the closed-loop control system, particularly in the event of abrupt parameter changes. The proposed controller uses an adaptive proportional-derivative+integral (PD +I) structure which, alongside the DCD algorithm, offers an effective substitute to a conventional proportional-integral-derivative (PID) controller. The nonadaptive integral controller (+I), introduced in the feedback loop, increases the excitation of the filter tap weight and ensures good regulation. The approach results in a fast adaptive controller with self-loop compensation. This is required to minimize the prediction error signal and, in turn, minimize the voltage error signal in the loop by automatically calculating the optimal pole locations. The prediction error signal is further minimized through a second-stage FIR filter (adaptation gain stage). This ensures that the adaptive gains converge to their optimal value. This paper presents detailed simulation analysis and experimental validation on a prototype synchronous dc-dc buck converter. The experimental results clearly demonstrate the superior dynamic performance and voltage regulation compared to conventional PID and adaptive LMS control schemes, with only a modest increase in the computational burden to the microprocessor.