Modeling Control of Supercoiling Dynamics and Transcription Using DNA-Binding Proteins.

Nearly all natural and synthetic gene networks rely on the fundamental process of transcription to enact biological feedback, genetic programs, and living circuitry. In this work, we investigate the efficacy of controlling transcription using a new biophysical mechanism, control of localized supercoiling near a gene of interest. We postulate a basic reaction network model for describing the general phenomenon of transcription and introduce a separate set of equations to describe the dynamics of supercoiling. We show that supercoiling and transcription introduce a shared reaction flux term in the model dynamics and illustrate how the modulation of supercoiling can be used to control transcription rates. We show the supercoiling-transcription model can be written as a nonlinear state-space model, with a radial basis function nonlinearity to capture the empirical relationship between supercoiling and transcription rates. We show the system admits a single, globally exponentially stable equilibrium point. Notably, we show that mRNA steady-state levels can be controlled directly by increasing a length-scale parameter for genetic spacing. Finally, we build a mathematical model to explore the use of a DNA binding protein, to define programmable boundary conditions on supercoiling propagation, which we show can be used to control transcriptional bursting or pulsatile transcriptional response. We show there exists a stabilizing control law for mRNA tracking, using the method of control Lyapunov functions and illustrate these results with numerical simulations.

Research on design and control methods of a lightweight upper limb joint isokinetic rehabilitation training equipment.

Introduction: Isokinetic exercise can improve joint muscle strength and stability, making it suitable for early rehabilitation of stroke patients. However, traditional isokinetic equipment is bulky and costly, and cannot effectively avoid external environmental interference. Methods: This paper designed a lightweight upper limb joint isokinetic rehabilitation training equipment, with a control system that includes a speed planning strategy and speed control with disturbance rejection. Based on the established human-machine kinematic closed-loop model between the equipment and the user, a dynamic evaluation method of torque at the joint level was proposed. Results: To validate the effectiveness of the equipment, experiments were conducted by manually applying random disturbances to the equipment operated at an isokinetic speed. The results showed that the root mean square error between the observed torque curve of the second-order linear extended state observer used in this paper and the actual disturbance curve was 0.52, and the maximum speed tracking error of the speed control algorithm was 1.27%. In fast and slow sinusoidal speed curve tracking experiments, the root mean square errors of the speed tracking results for this algorithm were 9.65 and 5.27, respectively, while the tracking errors for the PID speed control algorithm under the same environment were 19.94 and 12.11. Discussion: The research results indicate that compared with traditional PID control method, the proposed control strategy demonstrates superior performance in achieving isokinetic control and suppressing external disturbances, thereby exhibiting significant potential in promoting upper limb rehabilitation among patients.

An improve nonlinear robust control approach for robotic manipulators with PSO-based global optimization strategy.

During the trajectory tracking of robotic manipulators, many factors including dead zones, saturation, and uncertain dynamics, greatly increase the modeling and control difficulty. Aiming for this issue, a nonlinear active disturbance rejection control (NADRC)-based control strategy is proposed for robotic manipulators. In this controller, an extended state observer is introduced on basis of the dynamic model, to observe the extend state of model uncertainties and external disturbances. Then, in combination with the nonlinear feedback control structure, the robust trajectory tracking of robotic manipulators is achieved. Furthermore, to optimize the key parameters of the controller, an improved particle swarm optimization algorithm (IPSO) is designed using chaos theory, which improves the tracking accuracy of the proposed NDRC strategy effectively. Finally, using comparative studies, the effectiveness of the proposed control strategy is demonstrated by comparing with several commonly used controllers.

Design of a stand-alone hybrid dispersed generation network unified by passivity-based control.

In this article, we propose a model for a stand-alone hybrid distributed generation system. In this model, the input sources are distributed DC sources like solar panels or batteries. The idea behind this network framework is to introduce a hybrid DC/AC network, feasible for small and remotely located areas with stand-alone DC grids, in the vicinity of larger towns requiring a functional AC connection. The behaviour of the system in the steady state is analysed, and the network is mathematically represented with port-controlled Hamiltonian modelling. Stabilization to the desired voltage, both AC as well as DC, is attained with nonlinear passivity-based control taking into consideration not only the energy characteristics but also the inherent physical structure.

Deep Learning for Neuromuscular Control of Vocal Source for Voice Production.

A computational neuromuscular control system that generates lung pressure and three intrinsic laryngeal muscle activations (cricothyroid, thyroarytenoid, and lateral cricoarytenoid) to control the vocal source was developed. In the current study, LeTalker, a biophysical computational model of the vocal system was used as the physical plant. In the LeTalker, a three-mass vocal fold model was used to simulate self-sustained vocal fold oscillation. A constant/ǝ/vowel was used for the vocal tract shape. The trachea was modeled after MRI measurements. The neuromuscular control system generates control parameters to achieve four acoustic targets (fundamental frequency, sound pressure level, normalized spectral centroid, and signal-to-noise ratio) and four somatosensory targets (vocal fold length, and longitudinal fiber stress in the three vocal fold layers). The deep-learning-based control system comprises one acoustic feedforward controller and two feedback (acoustic and somatosensory) controllers. Fifty thousand steady speech signals were generated using the LeTalker for training the control system. The results demonstrated that the control system was able to generate the lung pressure and the three muscle activations such that the four acoustic and four somatosensory targets were reached with high accuracy. After training, the motor command corrections from the feedback controllers were minimal compared to the feedforward controller except for thyroarytenoid muscle activation.

Survey of Advanced Nonlinear Control Strategies for UAVs: Integration of Sensors and Hybrid Techniques.

This survey paper explores advanced nonlinear control strategies for Unmanned Aerial Vehicles (UAVs), including systems such as the Twin Rotor MIMO system (TRMS) and quadrotors. UAVs, with their high nonlinearity and significant coupling effects, serve as crucial benchmarks for testing control algorithms. Integration of sophisticated sensors enhances UAV versatility, making traditional linear control techniques less effective. Advanced nonlinear strategies, including sensor-based adaptive controls and AI, are increasingly essential. Recent years have seen the development of diverse sliding surface-based, sensor-driven, and hybrid control strategies for UAVs, offering superior performance over linear methods. This paper reviews the significance of these strategies, emphasizing their role in addressing UAV complexities and outlining future research directions.

Universe-inspired algorithms for control engineering: A review.

Control algorithms have been proposed based on knowledge related to nature-inspired mechanisms, including those based on the behavior of living beings. This paper presents a review focused on major breakthroughs carried out in the scope of applied control inspired by the gravitational attraction between bodies. A control approach focused on Artificial Potential Fields was identified, as well as four optimization metaheuristics: Gravitational Search Algorithm, Black-Hole algorithm, Multi-Verse Optimizer, and Galactic Swarm Optimization. A thorough analysis of ninety-one relevant papers was carried out to highlight their performance and to identify the gravitational and attraction foundations, as well as the universe laws supporting them. Included are their standard formulations, as well as their improved, modified, hybrid, cascade, fuzzy, chaotic and adaptive versions. Moreover, this review also deeply delves into the impact of universe-inspired algorithms on control problems of dynamic systems, providing an extensive list of control-related applications, and their inherent advantages and limitations. Strong evidence suggests that gravitation-inspired and black-hole dynamic-driven algorithms can outperform other well-known algorithms in control engineering, even though they have not been designed according to realistic astrophysical phenomena and formulated according to astrophysics laws. Even so, they support future research directions towards the development of high-sophisticated control laws inspired by Newtonian/Einsteinian physics, such that effective control-astrophysics bridges can be established and applied in a wide range of applications.

Model predictive control (MPC) applied to a simplified model, magnetic nanoparticle hyperthermia (MNPH) treatment process.

Magnetic nanoparticle hyperthermia (MNPH) has emerged as a promising cancer treatment that complements conventional ionizing radiation and chemotherapy. MNPH involves injecting iron-oxide nanoparticles into the tumor and exposing it to an alternating magnetic field (AMF). Iron oxide nanoparticles produce heat when exposed to radiofrequency AMF due to hysteresis loss. Minimizing the non-specific heating in human tissues caused by exposure to AMF is crucial. A pulse-width-modulated AMF has been shown to minimize eddy-current heating in superficial tissues. This project developed a control strategy based on a simplified mathematical model in MATLAB SIMULINK®to minimize eddy current heating while maintaining a therapeutic temperature in the tumor. A minimum tumor temperature of 43 [°C] is required for at least 30 [min] for effective hyperthermia, while maintaining the surrounding healthy tissues below 39 [°C]. A model predictive control (MPC) algorithm was used to reach the target temperature within approximately 100 [s]. As a constrained MPC approach, a maximum AMF amplitude of 36 [kA/m] and increment of 5 [kA/m/s] were applied. MPC utilized the AMF amplitude as an input and incorporated the open-loop response of the eddy current heating in its dynamic matrix. A conventional proportional integral (PI) controller was implemented and compared with the MPC performance. The results showed that MPC had a faster response (30 [s]) with minimal overshoot (1.4 [%]) than PI controller (115 [s] and 5.7 [%]) response. In addition, the MPC method performed better than the structured PI controller in its ability to handle constraints and changes in process parameters.

Application of optimized Kalman filtering in target tracking based on improved Gray Wolf algorithm.

High precision is a very important index in target tracking. In order to improve the prediction accuracy of target tracking, an optimized Kalman filter approach based on improved Gray Wolf algorithm (IGWO-OKF) is proposed in this paper. Since the convergence speed of traditional Gray Wolf algorithm is slow, meanwhile, the number of gray wolves and the choice of the maximum number of iterations has a great influence on the algorithm, a nonlinear control parameter combination adjustment strategy is proposed. An improved Grey Wolf Optimization algorithm (IGWO) is formed by determining the best combination of adjustment parameters through the fastest iteration speed of the algorithm. The improved Grey Wolf Optimization algorithm (IGWO) is formed, and the process noise covariance matrix and observation noise covariance matrix in Kalman filter are optimized by IGWO. The proposed approach is applied into. The experiment results show that the proposed IGWO-OKF approach has low error, high accuracy and good prediction effect.

An adaptive predefined time sliding mode control for uncertain nonlinear cyber-physical servo system under cyber attacks.

Malicious attacks are often inevitable in cyber-physical systems (CPS). Accuracy in Cyber physical system for position tracking of servos is the major concern now a days. In high precision industrial automation, it is very hard to achieve accuracy in tracking especially under malicious cyber-attacks, control saturations, parametric perturbations and external disturbances. In this paper, we have designed a novel predefined time (PDT) convergence sliding mode adaptive controller (PTCSMAC) for such kind of cyber physical control system. Main key feature of our control is to cope these challenges that are posed by CPS systems such as parameter perturbation, control saturation, and cyber-attacks and the whole system then upgrade to a third-order system to facilitate adaptive control law. Then, we present an adaptive controller based on the novel PDT convergent sliding mode surface (SMS) combined with a modified weight updated Extreme Learning Machine (ELM) which is used to approximate the uncertain part of the system. Another significant advantage of our proposed control approach is that it does not require detailed model information, guaranteeing robust performance even when the system model is uncertain. Additionally, our proposed PTCSMAC controller is nonsingular regardless of initial conditions, and is capable of eradicating the possibility of singularity problems, which are frequently a concern in numerous CPS control systems. Finally, we have verified our designed PTCSMAC control law through rigorous simulations on CPS seeker servo positioning system and compared the robustness and performance of different existing techniques.

Model of strategy control for delayed panic spread in emergencies.

In emergencies similar to virus spreading in an epidemic model, panic can spread in groups, which brings serious bad effects to society. To explore the transmission mechanism and decision-making behavior of panic, a government strategy was proposed in this paper to control the spread of panic. First, based on the SEIR epidemiological model, considering the delay effect between susceptible and exposed individuals and taking the infection rate of panic as a time-varying variable, a SEIR delayed panic spread model was established and the basic regeneration number of the proposed model was calculated. Second, the control strategy was expressed as a state delayed feedback and solved using the exact linearization method of nonlinear control system; the control law for the system was determined, and its stability was proven. The aim was to eradicate panic from the group so that the recovered group tracks the whole group asymptotically. Finally, we simulated the proposed strategy of controlling the spread of panic to illustrate our theoretical results.

Robotic mirror therapy for stroke rehabilitation through virtual activities of daily living.

Mirror therapy is a standard technique of rehabilitation for recovering motor and vision abilities of stroke patients, especially in the case of asymmetric limb function. To enhance traditional mirror therapy, robotic mirror therapy (RMT) has been proposed over the past decade, allowing for assisted bimanual coordination of paretic (affected) and contralateral (healthy) limbs. However, state-of-the-art RMT platforms predominantly target mirrored motions of trajectories, largely limited to 2-D motions. In this paper, an RMT platform is proposed, which can facilitate the patient to practice virtual activities of daily living (ADL) and thus enhance their independence. Two similar (but mirrored) 3D virtual environments are created in which the patients operate robots with both their limbs to complete ADL (such as writing and eating) with the assistance of the therapist. The recovery level of the patient is continuously assessed by monitoring their ability to track assigned trajectories. The patient's robots are programmed to assist the patient in following these trajectories based on this recovery level. In this paper, the framework to dynamically monitor recovery level and accordingly provide assistance is developed along with the nonlinear controller design to ensure position tracking, force control, and stability. Proof-of-concept studies are conducted with both 3D trajectory tracking and ADL. The results demonstrate the potential use of the proposed system to enhance the recovery of the patients.

Predefined-time fractional-order time-varying sliding mode control for arbitrary order systems with uncertain disturbances.

This paper proposes a fractional-order time-varying sliding mode control method with predefined-time convergence for a class of arbitrary-order nonlinear control systems with compound disturbances. The method has global robustness and strongly predefined-time stability. All state errors of the system can converge to zero at a desired time, which can be set arbitrarily with a simple parameter. The strongly predefined-time convergence of the system is clearly demonstrated by the analytic expression of state error, which is obtained by solving fractional-order differential equations corresponding to the sliding mode function. The simulation results show that the proposed method still has good control performance in the presence of input saturation and external interference.

Nonlinear control designs and their application to cancer differentiation therapy.

We designed three new controllers: a sigmoid-based controller, a polynomial dynamic inversion-based controller, and a proportional-integral-derivative (PID) impulsive controller for cancer differentiation therapy. We compared these three controllers to existing control strategies to show the improvement in performance and compare their robustness. The sigmoid-based controller adds a sigmoid term associated with the error of the controlled state and a selected observed state. The sigmoid term is multiplied by a control gain, thereby decreasing the control effort for state transition. The polynomial dynamic inversion-based controller adds a cubic error term in the error dynamic aiming to achieve a shorter convergence time to the desired value of the controlled state. The PID impulsive controller considers the accumulated controlled state error and the rate of change of the controlled state error, thereby forcing the controlled state to converge to the desired value and alleviating the damping effect in the steady state. For the considered cancer network, the 3 new cancer control strategies exhibit superior and robust performance. The PID impulsive controller has a significant improvement in robustness compared to the impulsive controller and has greater potential for cancer differentiation therapy.

Robust positive control of tumour growth using angiogenic inhibition.

In practice, many physical systems, including physiological ones, can be considered whose input can take only positive quantities. However, most of the conventional control methods do not support the positivity of the main input data to the system. Furthermore, the parameters of these systems, similar to other non-linear systems, are either not accurately identified or may change over time. Therefore, it is reasonable to design a controller that is robust against system uncertainties. A robust positive-input control method is proposed for the automatic treatment of targeted anti-angiogenic therapy implementing a recently published tumour growth model based on experiments conducted on mouse models. The backstepping (BS) approach is applied to design the positive input controller using sensory data of tumour volume as feedback. Unlike previous studies, the proposed controller only requires the measurement of tumour volume and does not require the measurement of inhibitor level. The exponential stability of the controlled system is proved mathematically using the Lyapunov theorem. As a result, the convergence rate of the tumour volume can be controlled, which is an important issue in cancer treatment. Moreover, the robustness of the system against parametric uncertainties is verified mathematically using the Lyapunov theorem. The real-time simulation results-based (OPAL-RT) and comparisons with previous studies confirm the theoretical findings and effectiveness of the proposed method.

Projective Synchronization of Delayed Uncertain Coupled Memristive Neural Networks and Their Application.

In this article, the authors analyzed the nonlinear effects of projective synchronization between coupled memristive neural networks (MNNs) and their applications. Since the complete signal transmission is difficult under parameter mismatch and different projective factors, the delays, which are time-varying, and uncertainties have been taken to realize the projective synchronization of MNNs with multi-links under the nonlinear control method. Through the extended comparison principle and a new approach to dealing with the mismatched parameters, sufficient criteria have been determined under different types of projective factors and the framework of the Lyapunov-Krasovskii functional (LKF) for projective convergence of the coupled MNNs. Instead of the classical treatment for secure communication, the concept of error of synchronization between the drive and response systems has been applied to solve the signal encryption/decryption problem. Finally, the simulations in numerical form have been demonstrated graphically to confirm the adaptability of the theoretical results.

Path following of under-actuated ships based on backstepping and predictive control method.

To deal with the sideslip angle caused by the current disturbances or transverse motion for path following of under-actuated ships, a nonlinear observer established by an exponential function is introduced in the backstepping approach which converts the path following into heading control. Then, the model predictive control (MPC) method is used as a heading controller, addressing the rudder optimization. A linear extended state observer technology was exploited to estimate yaw rate, external disturbances, and internal uncertainties, which could avoid measuring the high-order state used in the MPC controller and promote the accuracy of the MPC internal model. Moreover, an inverse tangent function is applied to develop a new method for switching the reference heading angle to reduce rudder amplitude when the ship is choosing the next waypoint. Finally, the validity and reliability of the design method were verified through comparative computer simulation experiments.

Switched step integral backstepping control for nonlinear motion systems with application to a laboratory helicopter.

In this paper, the energy efficiency of the widespread application of backstepping control to a class of nonlinear motion systems is investigated. A Switched Step Integral Backstepping Control (SSIBC) scheme is introduced to improve immunity to measurement noise and to increase the energy efficiency of conventional backstepping in practice. The SSIBC is realized by switching between two candidate controllers obtained at different steps of the iterative backstepping design process. A bi-state dependent hysteresis rule is developed to supervise stable switching between the different regimes in the presence of noise. The proposed method is experimentally verified on a MIMO twin rotor laboratory helicopter involving coupled nonlinear dynamics, inaccessible states and uncertainties. Experimental results show that in addition to a reduction in power consumption, the SSIBC reduces saturation of the control signal and visible motor jerking in contrast with conventional backstepping. Additional comparisons with a previously proposed optimized decoupling PID controller also show significant improvement in precision achieved with higher energy efficiency. Experimental results obtained with the introduction of an external disturbance into the system also show the robustness of the proposed SSIBC.

Nonlinear swing-down control of the Acrobot: Analysis and optimal gain design.

In this paper, we address the swing-down control of the Acrobot, a two-link planar robot operating in a vertical plane with only the second joint being actuated. The control objective is to rapidly stabilize the Acrobot around the downward equilibrium point, with both links in the downward position, from almost all initial states. Under the conditions of no friction and measurability of only the angle and angular velocity of the actuated joint, we present a sinusoidal-derivative (SD) controller. This controller consists of a linear feedback of the sinusoidal function of the angle of the actuated joint and a linear feedback of its angular velocity. We prove that the control objective is achieved if the sinusoidal gain is greater than a negative constant and the derivative gain is positive. We establish crucial relationships between the relative stability of the Acrobot under the SD controller and its physical parameters, presenting analytically all optimal control gains. These gains minimize the real parts of the dominant poles of the linearized model of the resulting closed-loop system around the downward equilibrium point. We demonstrate that the resulting dominant closed-loop poles can be double complex conjugate poles, or a quadruple real pole, or a triple real pole, depending on the Acrobot's physical parameters. Simulation studies indicate that the proposed SD controller outperforms the derivative (D) controller in rapidly stabilizing the Acrobot at the downward equilibrium point.

Nonlinear control with friction compensation to swing-up a Furuta pendulum.

Different works in literature have reported that nonlinear controllers based on the energy approach are not effective to completely swing-up an inverted pendulum subjected to friction. Most studies trying to solve this issue consider static friction models in the design of controllers. This consideration is mainly because the stability proof of the system with dynamic friction in closed-loop is difficult. Hence, this paper presents a nonlinear controller with friction compensation to swing-up a Furuta pendulum with dynamic friction. With this aim, we consider that only the active joint of the system is subjected to friction, which is represented via a dynamic model, namely, the Dahl model. We first present Furuta Pendulum dynamic model with dynamic friction. Then, by slightly modifying an energy-based controller that has been previously reported in literature and by including friction compensation, we propose a nonlinear controller that allows to swing-up completely a Furuta pendulum subjected to friction. The unmeasurable friction state is estimated through a nonlinear observer and a stability analysis of the closed-loop system is accomplished with the direct Lyapunov method. Finally, successful experimental results are presented for a Furuta pendulum prototype built by authors. This shows the effectiveness of the proposed controller in achieving a complete swing-up of the Furuta pendulum, in a time feasible for experimental implementation, and ensuring closed-loop stability.