Switching Control Research Articles

AI‐enabled bumpless transfer control strategy for legged robot with hybrid energy storage system

AbstractDesigning Hybrid energy storage system (HESS) for a legged robot is significant to improve the motion performance and energy efficiency of the robot. However, switching between the driving mode and regenerative braking mode in the HESS may generate a torque bump, which has brought significant challenges to the stability of the robot locomotion. To address this issue, an AI‐enabled control strategy for bumpless transfer switching is proposed, which is composed of a Proximal Policy Optimisation (PPO)‐based non‐linear active disturbance rejection controller and Deep neural network (DNN)‐based bumpless transfer strategy. We indicate that the proposed intelligent strategy solves bumpless transfer by reducing the torque bump during the system switching process. Meanwhile, the authors analyse the energy driving subsystem and the energy regenerating subsystem based on operational modes and the energy flow. Via the parameters tuning criterion, the authors adopt a PPO algorithm to adaptively tune the parameters of the non‐linear active disturbance rejection controller, which can improve the performance of the primary torque control. A DNN‐based intelligent bumpless transfer strategy is proposed to set the initial value of two switching controllers at a switching moment. Simulations and experimental results validate the effectiveness of the proposed control strategy.

High‐level decision‐making for autonomous overtaking: An MPC‐based switching control approach

AbstractThe key motivation of this paper lies in the development of a high‐level decision‐making framework for autonomous overtaking maneuvers on two‐lane country roads with dynamic oncoming traffic. To generate an optimal and safe decision sequence for such scenario, an innovative high‐level decision‐making framework that combines model predictive control (MPC) and switching control methodologies is introduced. Specifically, the autonomous vehicle is abstracted and modelled as a switched system. This abstraction allows vehicle to operate in different modes corresponding to different high‐level decisions. It establishes a crucial connection between high‐level decision‐making and low‐level behaviour of the autonomous vehicle. Furthermore, barrier functions and predictive models that account for the relationship between the autonomous vehicle and oncoming traffic are incorporated. This technique enables us to guarantee the satisfaction of constraints, while also assessing performance within a prediction horizon. By repeatedly solving the online constrained optimization problems, we not only generate an optimal decision sequence for overtaking safely and efficiently but also enhance the adaptability and robustness. This adaptability allows the system to respond effectively to potential changes and unexpected events. Finally, the performance of the proposed MPC framework is demonstrated via simulations of four driving scenarios, which shows that it can handle multiple behaviours.

Dual sliding mode control of linear maglev synchronous motor based on novel extended state observer

In order to improve the convergence speed and disturbance immunity of the linear maglev synchronous motor (LMLSM) maglev system, this paper proposes a dual sliding mode control strategy based on a novel expanded state observer (NESO). Firstly, a dual terminal sliding mode (DTSM) surface is designed, which exhibits the desired dynamic and steady state performance. The problem of the TSM singularity is solved by the dual sliding mode technique, which turns the control signal into a continuous switching control through the filtering function and effectively reduces the jitter problem. Since the maglev system is susceptible to uncertainties such as nonlinearity, strong coupling and external perturbations, which affect its tracking accuracy, a continuous smooth ESO is designed to estimate and compensate for these uncertainty perturbations, thus solving the problem that the traditional ESO is continuous but not derivable at the switching point. Then, it is proved that the DTSM control strategy based on NESO converges to the equilibrium point in finite time by Lyapunov function. Finally, the effectiveness and superiority of the proposed strategy are verified on the LMLSM maglev system platform.

Spatial Writing of Ultrafast All-Optical Switching.

Writing spatial information on ultrafast all-optical switching is essential for constructing ultrafast processing units in photonic applications, such as optical communication and computing networks. However, most methods ignore the fabrication and imaging of controllable switching area, limiting its spatial information and the further design in ultrafast devices. Here, we propose a method to spatially write in ultrafast all-optical switching based on MAPbI3 perovskite with nanocone structure and visualize the switching effect in arbitrary designed area. Due to the light confinement effect of nanocone fabrication using a fs laser, the light is strongly absorbed by perovskite and reach saturable absorption. It leads to ultrafast broadband transmittance change with 25 fs switching time and 10% modulation depth in nanocone perovskite area. Our preparation method offers high efficiency, performance, and flexibility for the spatial writing of ultrafast all-optical switching, which is promising for developing ultrafast all-optical networks and the next generation of communication technology.

Adaptive optimal control of switched linear systems with input constraints

In this paper, an adaptive optimal control scheme is proposed for a class of switched linear systems with input constraints and unknown dynamics. Firstly, in the case that the system dynamics is known, the optimal controller and optimal switching condition are determined by introducing a non-quadratic cost function and employing the embedding transformation technique. After that, an adaptive algorithm is established for estimating the unknown dynamics of the switched linear systems. Then, when the system dynamics is unknown, the adaptive optimal controller and optimal switching condition are deduced by resorting to the certainty equivalent principle. A common Lyapunov function is constructed, by using which, it is proved that the closed-loop system is globally uniformly asymptotically stable. Simulations are finally conducted to further illustrate the effectiveness of the proposed method.

Sequential time scaling transformation technique for time-delay optimal control problem

The time-scaling transformation technique used within the computational framework of control parameterization serves as an effective method for the optimization of control switching time as well as the control value for time-delay optimal control problems. However, the conventional time-scaling transformation stipulates that the switching times for all control components must be identical, which may not be feasible in real-world scenarios. To address this limitation, we introduce the sequential time-scaling transformation technique for solving time-delay optimal control problems. By sequentially applying control parameterization and time-scaling transformation to each of the control variable, the new technique enables the configuration of a varying number of segments for the control, and the switching time for each control component can be adaptively selected. Numerical results demonstrate that our approach not only achieves superior cost performance but also offers enhanced flexibility in control strategy.

Self-Stabilizing Control Strategy of Stabilized Platform for Rotary Steerable Drilling System Based on Adaptive Backstepping Control

To enhance the robustness of the toolface angle control in a fully rotary steerable drilling tool, a backstepping control law and a drilling fluid flow adaptive law are devised based on the dynamic model of the stable platform. The Lyapunov function is constructed, and it is proven that the adaptive backstepping control system of the stabilized platform is stable. Furthermore, in order to address problems such as the friction dead zone and excessive rotational kinetic energy in the stabilized platform system, which could cause toolface angle oscillations and the rapid rotation of the stabilized platform, we additionally propose an online estimation method for the balancing torque and velocity-angle control switching strategy. By combining the backstepping control law, drilling fluid flow adaptive law, and velocity-angle control switching strategy, a self-stabilizing control strategy for the stabilized platform is established. In comparison with the PID control method, the simulation results show the superiority of the proposed scheme under complex disturbances from a downhole environment. And the drilling simulation experiment results indicate that the proposed control method has a good anti-interference capability under various conditions, including drilling fluid flow rate, inclination angle, and drill collar rotational speed. Therefore, the proposed control method can improve the robustness of the stabilized platform control system.

Event-triggered-based encoding–decoding consensus control of continuous-time multi-agent systems under DoS attacks

The security of multi-agent systems (MASs) is important particularly when considering the potential threat posed by malicious attackers. Specifically, network attacks and information theft caused by attackers have garnered significant attention due to their potential to destroy the consensus of the system. Therefore, this paper presents a novel approach to achieve leader-following consensus control of continuous-time MASs under DoS attacks, leveraging event-triggered encoding and decoding techniques. When the DoS attacks occur, the transmitted information between agents becomes unavailable. To address this challenge, a buffer is introduced to store the latest transmission data and then the stored data can be used when the DoS attacks exist. We also propose an event-triggered encoding–decoding strategy that can be employed for privacy protection in linear continuous-time MASs. The transmitted data is encoded as a codeword before transmitting, and the encoder updates it when event-triggered conditions are met. The receiving end decodes the data upon receipt. Furthermore, a distributed switching controller is designed using the encoded and stored state values to achieve leader-following consensus. The control gain is calculated to obtain sufficient conditions for DoS attacks through the linear matrix inequalities (LMI) and Lyapunov functions. The boundedness of transmitted data and the Zeno phenomenon are also analyzed. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed control algorithm.

Novel high-safety aeroengine performance predictive control method based on adaptive tracking weight

Increasing attention has been attracted to the dynamic performance and safety of advanced performance predictive control systems of the next-generation aeroengine. The latest research demonstrates that Subspace-based Improved Model Predictive Control (SIMPC) can overcome the difficulty in solving the predictive model in MPC/NMPC applications. However, applying constant design parameters cannot maintain consistent control effects in all states. Meanwhile, the designed system relies too much on sensor-measured data, and thus it is difficult to thoroughly validate the safety of the system because of its high complexity. This means that any potential hardware/software faults will endanger the engine. Therefore, this paper first presents a novel nonlinear mapping relationship to adaptively tune the tracking weight online with the change of Power Lever Angle (PLA) and real-time relative tracking error. Thus, without introducing additional design parameters, an Adaptive Tracking Weight-based SIMPC (ATW-SIMPC) controller is designed to improve the control performance in all operating states effectively. Then, a Primary/Backup Hybrid Control (PBHC) strategy with the ATW-SIMPC controller as the primary system and the traditional speed (Nf) controller as the backup system is proposed to ensure safety. The designed affiliated switching controller and the real-time monitor therein can be used to realize reasonable and smooth switching between primary/backup systems, so as to avoid bump transition. The PBHC system switches to the Nf controller when the ATW-SIMPC controller is wrong because of potential hardware/software faults; otherwise, the ATW-SIMPC controller keeps acting on the engine. The main results prove that the ATW-SIMPC controller with the optimal nonlinear mapping relationship, compared with the existing SIMPC controller, uplifts the dynamic control performance by 32% and reduces overshoots to an allowable limit, resulting in a better control effect in full state. The comparison results consistently indicate that the PBHC can guarantee engine safety in occurrence of hardware/software faults, such as sensor/onboard adaptive model faults. The approach proposed is applicable to the design of a model-based engine intelligent control system.

Adaptive supervisory control for automated manufacturing systems using borrowed-buffer slots

Robust deadlock supervisory control techniques for automated manufacturing systems under resource failures that need additional central buffers may lead to supererogatory cost in control implementation. To mitigate this issue, this paper reports a Petri net-based low-cost adaptive supervisory control policy that does not require extra buffers. If an unreliable resource fails, three classes of buffer spaces can be temporarily used to store parts that require the failed resource in their impending processing routes. The proposed adaptive supervisory control comprises of control places and switch controllers. If an unreliable resource fails, the switch controllers are activated to move the part types that require the failed resource in their subsequent processing stages into borrowed buffer spaces. After the failed resource is recovered, the system returns to its normal operating mode. The part types in the borrowed buffer spaces will be returned to their last processing stages before the occurrence of the failure and continued along their processing route. We propose criteria for selecting buffer space borrowers and buffer space lenders. Furthermore, redundant control places are removed by a new technique proposed in the paper in order to reduce the complexity of the supervisory control structure. We demonstrate the proposed method using examples.

A Compact Mode Controller (Switch) Using an Inverse-Designed 2-Mode Y-Junction

A Compact Mode Controller (Switch) Using an Inverse-Designed 2-Mode Y-Junction

Learning Fuel-Optimal Trajectories for Space Applications via Pontryagin Neural Networks

This paper demonstrates the utilization of Pontryagin Neural Networks (PoNNs) to acquire control strategies for achieving fuel-optimal trajectories. PoNNs, a subtype of Physics-Informed Neural Networks (PINNs), are tailored for solving optimal control problems through indirect methods. Specifically, PoNNs learn to solve the Two-Point Boundary Value Problem derived from the application of the Pontryagin Minimum Principle to the problem’s Hamiltonian. Within PoNNs, the Extreme Theory of Functional Connections (X-TFC) is leveraged to approximate states and costates using constrained expressions (CEs). These CEs comprise a free function, modeled by a shallow neural network trained via Extreme Learning Machine, and a functional component that consistently satisfies boundary conditions analytically. Addressing discontinuous control, a smoothing technique is employed, substituting the sign function with a hyperbolic tangent function and implementing a continuation procedure on the smoothing parameter. The proposed methodology is applied to scenarios involving fuel-optimal Earth−Mars interplanetary transfers and Mars landing trajectories. Remarkably, PoNNs exhibit convergence to solutions even with randomly initialized parameters, determining the number and timing of control switches without prior information. Additionally, an analytical approximation of the solution allows for optimal control computation at unencountered points during training. Comparative analysis reveals the efficacy of the proposed approach, which rivals state-of-the-art methods such as the shooting technique and the adaptive Gaussian quadrature collocation method.

An Algorithm for a Space-Vector Pulse Width Modulation with a Hybrid Switching Sequence for a Three-Level Neutral Point Clamped Voltage Source Inverter

This paper proposes a new algorithm for a space-vector pulse width modulation (SVPWM) with a hybrid switching sequence (SS) designed to control a three-level (3L) neutral point clamped (NPC) voltage source inverter (VSI). Based on the advantages of five-stage and seven-stage SS, we designed a hybrid SS to improve four key parameters in the system: inverter output current quality, neutral point (NP) and common-mode (CM) voltage levels, and switching losses of power switches. The proposed algorithm flexibly regulates meeting the four criteria depending on the system operating conditions by changing the regulation coefficient. The paper obtained an approximate dependence to determine the optimal regulation coefficient for any values of the inverter modulation coefficient when operating jointly with an active-inductive load. The effectiveness of the proposed algorithm is confirmed by computer simulation in the MatLab+Simulink environment, as well as the results of experimental studies. The paper presents the experimental dependences of static state spaces, including the distortion coefficient of higher current harmonics at the inverter output, the maximum error of the NP voltage, the number of switching pairs of power switches, and the pulse duty factor of CM voltage, depending on the inverter modulation coefficient and the regulation coefficient of the hybrid SS. The results showed that in the algorithm for a SVPWM with a hybrid SS, the number of switchings of power switches is reduced by an average of 13.5 % while maintaining the NP voltage balance and the quality of the curve at the inverter output at an acceptable level, close to a seven-stage SS (the deviations do not exceed 0.5 % and 0.2 %, respectively). The average CM voltage coefficient decreased by no more than 4.5 %. The indicators improve the energy saving, the weight-size parameters, and the operational reliability of the 3L NPC VSI. The application area of the algorithm is much wider and includes the control of power switches of active front ends, grid converters for electricity storage systems, active power filters, and power conditioners. DOI: https://doi.org/10.52783/pst.237

Controllable single-photon transport mediated by a time-modulated Jaynes–Cummings model

Controllable single-photon scattering in a one-dimensional waveguide coupled to a Jaynes–Cummings structure containing a time-modulated two-level atom interacting with a single-mode cavity is investigated. The photon transmission and reflection amplitudes are calculated by using an effective Floquet Hamiltonian in real space. The results show that the coupling between the atom and the cavity mode can dynamically be tuned via periodically modulating the atomic transition frequency. As a consequence, the scattering behaviors of the waveguide photons can be actively manipulated, and a controllable single-photon switch with high on-off ratio could be realized. More interestingly, the switch works well within a wide frequency region, i.e., the transmission of both resonant and off-resonant waveguide photons can be effectively switched on or off with appropriate system parameters. Furthermore, the proposed dynamically tunable switching scheme is robust against atomic dissipation associated with the help of atom-cavity coupling mismatch. Such single-photon device can be used as an elementary unit for various quantum information processing.

Deep Deterministic Policy Gradient (DDPG) Agent-Based Sliding Mode Control for Quadrotor Attitudes

A novel reinforcement deep learning deterministic policy gradient agent-based sliding mode control (DDPG-SMC) approach is proposed to suppress the chattering phenomenon in attitude control for quadrotors, in the presence of external disturbances. First, the attitude dynamics model of the quadrotor under study is derived, and the attitude control problem is described using formulas. Second, a sliding mode controller, including its sliding mode surface and reaching law, is chosen for the nonlinear dynamic system. The stability of the designed SMC system is validated through the Lyapunov stability theorem. Third, a reinforcement learning (RL) agent based on deep deterministic policy gradient (DDPG) is trained to adaptively adjust the switching control gain. During the training process, the input signals for the agent are the actual and desired attitude angles, while the output action is the time-varying control gain. Finally, the trained agent mentioned above is utilized in the SMC as a parameter regulator to facilitate the adaptive adjustment of the switching control gain associated with the reaching law. The simulation results validate the robustness and effectiveness of the proposed DDPG-SMC method.

Effective “off-on” switch for fertility control in female zebrafish

The implementation of a controllable sterility strategy is crucial for the commercialization of precise trait improvements in farmed fish using genome editing and sustainable development of fisheries. Our previous research has demonstrated that females deficient in pituitary gonadotropin luteinizing hormone β-subunit (lhβ) or gonadal steroidogenesis gene steroidogenic acute regulatory protein (star) exhibit sterility due to impaired oocyte maturation and ovulation. Nevertheless, the effective restoration of fertility in lhβ- or star-deficient females remains unsolved. This study has discovered that the administration of exogenous 17α,20β-dihydroxy-4-pregnen-3-one (DHP) at 100 and 300 μg/L for 6 h (from 02:00 to 08:00 a.m.) effectively restores the fertility of lhβ- or star-deficient females. Fertilized eggs from these mutant females can be raised without noticeable developmental defects for up to 3 weeks post-fertilization (wpf) compared to the wild-type (WT) control zebrafish. The increased expression levels of adamts9 and adam8b in lhβ- or star-deficient zebrafish females treated with DHP demonstrate a positive correlation with oocyte maturation and ovulation restoration. In contrast, exogenous DHP administration did not rescue the sterility phenotype observed in progesterone receptor (pgr)-deficient females. Building on our recent success in generating an all-female carp population through cytochrome P450, family 17, subfamily A, polypeptide 1 (cyp17a1)-depletion, our research presents a promising and effective strategy for an “off-on” switch for managing fertility in genome-edited cyprinids. The strategy would offer practical guidance and theoretical justification for developing “controllable fertility” in all-female fish, which would support the sustainable development of fisheries by promoting the use of novel biotechnologies in aquaculture in an eco-friendly manner.

Controllable switching of the magnonic excitation based on the magnetostrictive effect

The magnetostrictive effect in a yttrium iron garnet sphere induces a coherent interaction between magnetization and elastic strain. The dispersive-type coupling between the ferromagnetic magnon mode and the phonon mode is treated analytically, and the features of the magnonic excitation are discussed. We show that the resonant magnonic excitation of a signal driving field can be well controlled by another strong field via the interference of the excitation pathways, which results in convenient magnonic control and enables a magnonic switch with excellent functionality. The parameter optimization of the system has been performed to expand the operating bandwidth, and the influence of thermal noises to the magnonic switch has been discussed. Our analysis may provide a viable tool for controlling the magnonic excitation in magnetic materials and find applications in designing magnon-based devices.

New Speed Control Scheme for a Three-Phase Induction Machines - (3~IM) Based on Sine Duty Cycle Modulation (SDCM)

This paper proposes a new control scheme for three-phase induction machines based on the sinusoidal duty cycle modulation control strategy combined with V/f control. This technique allows efficient open-loop control of inverter switches. The proposed diagram shows the main characteristics:1 0.1   ,0 50 ,Hzf 21.44 ,base KHzf  2 ,12ref V Vu  . In addition, it guarantees optimum operation of the inverter with the torque and speed parameters of the three-phase induction machine complying with current requirements:10rC Nmmax 6,5A/50Hz/ THD=1,2%

An intelligent DNA nanodevice for precision thrombolysis.

Thrombosis is a leading global cause of death, in part due to the low efficacy of thrombolytic therapy. Here, we describe a method for precise delivery and accurate dosing of tissue plasminogen activator (tPA) using an intelligent DNA nanodevice. We use DNA origami to integrate DNA nanosheets with predesigned tPA binding sites and thrombin-responsive DNA fasteners. The fastener is an interlocking DNA triplex structure that acts as a thrombin recognizer, threshold controller and opening switch. When loaded with tPA and intravenously administrated in vivo, these DNA nanodevices rapidly target the site of thrombosis, track the circulating microemboli and expose the active tPA only when the concentration of thrombin exceeds a threshold. We demonstrate their improved therapeutic efficacy in ischaemic stroke and pulmonary embolism models, supporting the potential of these nanodevices to provide accurate tPA dosing for the treatment of different thromboses.

Hybrid Renewable Production Scheduling for a PV–Wind-EV-Battery Architecture Using Sequential Quadratic Programming and Long Short-Term Memory–K-Nearest Neighbors Learning for Smart Buildings

Electricity demand in residential areas is generally met by the local low-voltage grid or, alternatively, the national grid, which produces electricity using thermal power stations based on conventional sources. These generators are holding back the revolution and the transition to a green planet, being unable to cope with climatic constraints. In the residential context, to ensure a smooth transition to an ecological green city, the idea of using alternative sources will offer the solution. These alternatives must be renewable and naturally available on the planet. This requires a generation that is very responsive to the constraints of the 21st century. However, these sources are intermittent and require a hybrid solution known as Hybrid Renewable Energy Systems (HRESs). To this end, we have designed a hybrid system based on PV-, wind-turbine- and grid-supported battery storage and an electric vehicle connected to a residential building. We proposed an energy management system based on nonlinear programming. This optimization was solved using sequential quadrature programming. The data were then processed using a long short-term memory (LSTM) model to predict, with the contribution and cooperation of each source, how to meet the energy needs of each home. The prediction was ensured with an accuracy of around 95%. These prediction results have been injected into K-nearest neighbors (KNN), random forest (RF) and gradient boost (GRU) repressors to predict the storage collaboration rates handled by the local battery and the electric vehicle. Results have shown an R2_score of 0.6953, 0.8381, and 0.739, respectively. This combination permitted an efficient prediction of the potential consumption from the grid with a value of an R²-score of around 0.9834 using LSTM. This methodology is effective in allowing us to know in advance the amount of energy of each source, storage, and excess grid injection and to propose the switching control of the hybrid architecture.