Open-loop Control Research Articles

A brain machine interface framework for exploring proactive control of smart environments

Brain machine interfaces (BMIs) can substantially improve the quality of life of elderly or disabled people. However, performing complex action sequences with a BMI system is onerous because it requires issuing commands sequentially. Fundamentally different from this, we have designed a BMI system that reads out mental planning activity and issues commands in a proactive manner. To demonstrate this, we recorded brain activity from freely-moving monkeys performing an instructed task and decoded it with an energy-efficient, small and mobile field-programmable gate array hardware decoder triggering real-time action execution on smart devices. Core of this is an adaptive decoding algorithm that can compensate for the day-by-day neuronal signal fluctuations with minimal re-calibration effort. We show that open-loop planning-ahead control is possible using signals from primary and pre-motor areas leading to significant time-gain in the execution of action sequences. This novel approach provides, thus, a stepping stone towards improved and more humane control of different smart environments with mobile brain machine interfaces.

An Improved Voltage Control Method for EAST Fast Control Power Supply

The Experimental Advanced Superconducting Tokamak (EAST) fast control power supply (FCPS) is an important device for controlling the vertical displacement of plasma during the nuclear fusion power generation process, adopting a multiple H-bridge invertor branch parallel operation structure to output total current. At the beginning of each shot of plasma discharge, FCPS works in open-loop voltage control mode (VCM) or closed-loop current control mode (CCM) determined by the plasma control system to output current for exciting the load coil, to achieve plasma vertical displacement control. VCM has the characteristics of fast dynamic response speed but poor consistency of branch current and insufficient branch current control accuracy and stability because of open-loop control. CCM has the characteristics of high branch current control accuracy but poor dynamic response and robustness because of control delay and control parameters determined based on engineering experience. To achieve fast and robust control, an improved voltage control method (IVCM) is proposed by combining the advantages of VCM and CCM. In the beginning of establishing the output current, FCPS operates in VCM, and rapid establishment of the output current is ensured. After the output current rapidly increases to the critical value, closed-loop current control is added to VCM to ensure the accuracy of output current control. In closed-loop current control, linear super-twisting sliding mode control is designed to achieve fast and robust control, ensuring good consistency and fast dynamic response performance of each branch current. Simulations and experiments verify that the designed IVCM has better compatibility characteristics in output current stability, control accuracy, and consistency of each branch current compared to VCM.

Reliable optimal controls for SEIR models in epidemiology

We present and compare two different optimal control approaches applied to SEIR models in epidemiology, which allow us to obtain some policies for controlling the spread of an epidemic. The first approach uses Dynamic Programming to characterise the value function of the problem as the solution of a partial differential equation, the Hamilton–Jacobi–Bellman equation, and derive the optimal policy in feedback form. The second is based on Pontryagin’s maximum principle and directly gives open-loop controls, via the solution of an optimality system of ordinary differential equations. This method, however, may not converge to the optimal solution. We propose a combination of the two methods in order to obtain high-quality and reliable solutions. Several simulations are presented and discussed, also checking first and second order necessary optimality conditions for the corresponding numerical solutions.

Six Steps Commutation Torque and Dynamic Characteristics of Spherical Brushless Direct Current Motor

This paper presents developing a new three-degrees of freedom (DOF) electromagnetic actuator referred to here as a spherical brushless direct current (SBLDC) motor. The SBLDC motor consists of permanent magnets (PMs) in a rotor and three layers of electromagnets (EMs) in a stator. The EMs in the middle layer of the stator generate a spinning torque, and the other two layers, placed symmetrically with respect to the middle, produce a tilting torque. Winding layouts of the EMs in each layer are analytically studied for decoupling between spinning and tilting torque. Furthermore, a six steps commutation is proposed to independently control the tilting torque as well as the spinning torque. The commutation torque model is validated by the moment method based distributed multipoles (MMDMP) according to various tilting angles and directions. Then, the commutation torque model is further utilized to develop open-loop control and analyze stability with respect to equilibria. Finally, the performance of the SBLDC motor and control system is verified experimentally using commercial brushless direct current motor (BLDC) drivers. The results show the substantial potential of the SBLDC motor in automation and mobility applications requiring orientation control with high-speed spinning.

Research on N-type silicon crystal processing by liquid-immersion wire electrical discharge machining

Passivation at the input end of N-type silicon crystals leads to a decrease in the efficiency of wire electrical discharge machining (WEDM). In response to this problem, this paper proposes a method for the liquid-immersion WEDM of N-type silicon crystals. An equivalent circuit model for the WEDM of N-type silicon crystals was established, the volt-ampere characteristics of N-type silicon crystals immersed in different liquids at the incoming end were studied, and an experimental platform for the liquid-immersion WEDM of N-type silicon crystals was established. The experimental results demonstrate that immersion in deionized water can effectively suppress the passivation phenomenon at the input end of N-type silicon crystals, thus improving the chip removal conditions between electrodes. Compared to open-loop control machining, the machining efficiency was found to increase by 42.7 % and the surface roughness was decreased by 32.8 %. After continuous machining for 1 h, the current in the machining circuit did not significantly decrease, and the machining efficiency remained unchanged.

A dual grid-based deep reinforcement learning and computational fluid dynamics method for flow control and its application to nucleate pool boiling

This paper presents a proposition of a dual grid (DG)-based coupled deep reinforcement learning (DRL) and computational fluid dynamics (CFD) method for active flow control. The DG-DRL-CFD method uses a dual-resolution grid, with a coarser grid for the training phase and a finer grid for the testing phase of the DRL-CFD method. Further, after a validation study for our DRL-CFD-based parallelized in-house solvers, this paper presents a performance study for a periodic suction/ejection-based drag reduction for a cylinder in a channel-confined flow. Using an immersed boundary method for CFD on a Cartesian grid, this novel DG-DRL-CFD method is shown to result in almost same accuracy (within 1%) in a substantially reduced computational time as compared to the traditional DRL-CFD method (on the finer uniform grid). Finally, using a sharp interface level set method for an axisymmetric two-phase flow, this paper presents an application of the DR-DRL-CFD method for an oscillating base plate-based enhancement of heat transfer during nucleate pool boiling. As compared to our recent CFD study-based parametric study for the nucleate pool boiling problem, the present DG-DRL-CFD method leads to a profile and frequency of plate-oscillation that results in a larger value of average Nusselt number Nuavg for the plate. The coupled DRL-CFD method leads to plate oscillation profile giving higher enhancement in Nuavg, compared to an open-loop control strategy that involves a parametric sweep involving multiple simulations.

Experimental investigation of closed-loop active control to modulate coherent structures by mu-level method*

Abstract The experimental research on zero-net-mass-flux jet closed-loop active control was conducted in the wind tunnel.The mu-level method successfully detected burst events of the coherent structures. The streamwise velocity signals in the turbulent boundary layer were measured by HWA. The drag reduction rate of 16.7% is obtained comparable to that of the open-loop control and saves 75% of the input energy at the ASYN 100V/160 Hz control case, which reflects the advantages of the closed-loop control.The experimental findings indicate that the intensity increases in the near-wall region. The perturbation of the PZT vibrators on skewness factor is concentrated in the region y + < 60 . The generation of high-speed fluids is depressed and the downward effect of high-speed fluids weakens. The alteration of energy distribution and the discernible impact of modulation between structures of varying scales is observed. The correlation coefficient exhibits a strong positive correlation, which indicates that the large-scale structures produce modulation effect on small-scale ones. The occurrence of burst events is effectively suppressed.The disturbance has the characteristics of stable periodicity, positive and negative symmetry, low intermittency and high pulsation strength.The conditional phase waveform shows the fluctuation amplitude increases, indicating amplitude modulation effects on coherent structures.

An open-loop control algorithm for improved tracking in a heliostat

The growing energy demand and its relation to climate change have driven the search for sustainable alternatives, such as concentrated solar energy. In this context, heliostats play a crucial role by reflecting and concentrating solar light onto a receiver. However, traditional control approaches based on geographical data have limitations. This study introduces an autonomous control system for heliostats that eliminates the need for preloaded geographical data. The approach is based on communication between the heliostat and the solar tracker, with two configuration modes: map calibration and automatic. Centralized and autonomous heliostats are distinguished, with the latter being the focus of the study. Autonomous heliostats have their own control system and can make decisions regarding positioning and safety. The methodology involves a mathematical algorithm that calculates the optimal rotation and tilt of the heliostat to redirect light toward a target. Simulation and physical prototype testing validate a remarkable consistency between simulated and experimental data. A key result is the surprising similarity of 97.9% between the obtained data, validating the algorithm's effectiveness. This study provides a robust approach for designing autonomous heliostat control systems, integrating simulation and experimentation. These results support the algorithm's precision and ability to direct solar radiation effectively. Expanding towards autonomous control and complete heliostat system evaluation facilitates the path toward more efficient and sustainable concentrated solar energy.

Experimentally realized physical-model-based frugal wave control in metasurface-programmable complex media

Metasurface-programmable radio environments are considered a key ingredient of next-generation wireless networks. Yet, identifying a metasurface configuration that yields a desired wireless functionality in an unknown complex environment was so far only achieved with closed-loop iterative feedback schemes. Here, we introduce open-loop wave control in metasurface-programmable complex media by estimating the parameters of a compact physics-based forward model. Our experiments demonstrate orders-of-magnitude advantages over deep-learning-based digital-twin benchmarks in terms of accuracy, compactness and required calibration examples. Strikingly, our parameter estimation also works without phase information and without providing measurements for all considered scattering coefficients. These unique generalization capabilities of our pure-physics model unlock unforeseen and previously inaccessible frugal wave control protocols that significantly alleviate the measurement complexity. For instance, we achieve coherent wave control (focusing or perfect absorption) and phase-shift-keying backscatter communications in metasurface-programmable complex media with intensity-only measurements. Our approach is also directly relevant to dynamic metasurface antennas, microwave-based signal processors and emerging in situ reconfigurable nanophotonic, optical and room-acoustical systems.

Field oriented control driver development based on BTS7960 for physiotherapy robot implementation

In conjunction with sustainable development goal 3 (SDG 3), it is important to develop a national electrical component for physiotherapy robot development. This study aimed to develop an open-loop field-oriented control (FOC) driver utilizing BTS7960. The driver utilized three BTS7960s that produce sinewave with variable angular frequency (ω). The research then compared the open-loop FOC driver with electronics speed controller (ESC) performance to drive a brushless DC (BLDC) motor with an initial rotation per minute (RPM) of 400, 500, and 600. The main observation was RPM reduction when the BLDC motor was subjected to loads of 20, 35, 50, 65, and 80 gr. The result showed that the open-loop FOC driver performed better, especially on an 80 gr load. For an initial RPM of 600, the RPM reduced to 100 when controlled with an open-loop FOC driver, but lesser when controlled using ESC. The open-loop FOC driver produces higher torque on the BLDC motor so it could rotate with less reduction compared to ESC, which is evident. The open-loop FOC driver can be easily developed using BTS7960 with a settling time of 4 seconds. However future studies should still consider close-loop FOC drivers to achieve higher torque performance and faster transient response for physiotherapy robot applications.

A Untethered Magnetic Micro-Robot for Local Therapeutic Delivery for Neurosurgical Oncology

INTRODUCTION: Microrobots designed for biomedical applications have garnered significant research interest, offering advantages in drug delivery, microsurgery, and biopsies. Magnetic fields enabled by electromagnetic coils permit untethered nonlinear movement of these tiny robots within the brain on the surface, the cerebrospinal fluid, and parenchyma. Microdrillers (either individually or in swarms) are a promising tool for microsurgery, capable of drilling through viscoelastic media (brain tissue). However, challenges remain in scaling down the robot size and exploring diverse applications. METHODS: Parallelizable (batch) microfabrication involved 3D printing via two-photon lithography. The pH-sensitive hydrogel was synthesized through UV-initiated radical polymerization mixed with a tracer (fluorescein sodium salt). Imaging via photon-counting CT was performed for microbot tracking in preclinical models including hydrogel phantoms, large animal cadaveric fresh brain tissue, and human cadaveric tissue. RESULTS: Microbot speeds in various tissues were characterized with speeds up to 800 um/sec over magnetic field amplitudes of 5 to 55 mT while performing complex nonlinear trajectories via open-loop surgeon control via surgeon. Targeted hydrogel delivery of fluorescein label/nanoparticle drug over a pH range of 6.2 (tumor tissue) and 7.8 (normal tissue) over a time interval of 50 s is demonstrated as proof of concept with a rate of 2.4 times faster in acidic conditions. Lastly, we show state-of-the-art micromillmetric resolution for real time imaging via photon-counting spectral CT for implementation of closed loop control. CONCLUSIONS: A novel minimally invasive microrobotic platform technology capable of targeted local drug delivery in brain parenchyma is demonstrated.

Automatic Re-tuning of Poor-Performing PI-based Control Systems

The goal of the work is to create a new method for automatic re-tuning of PI controllers. It is achieved by solving the following problems. The first problem is to develop a method to identify a FOPDT estimation model by analyzing the dynamics of a control loop with a PI controller. The second problem is to demonstrate that the use of the estimated model allows to obtain better processes in control systems with PI controllers by comparing the resulting FOPDT model with a number of reduced models. The third problem is to develop a five-stage algorithm for automatic re-tuning of PI controllers. The fourth problem is to verify the software implementation of the developed algorithm. The most significant result was that the basis for estimating the FOPDT model in a loop with a Ziegler-Nichols tuned PI controller was the estimation the ratio of the delay to the time constant of the FOPDT model based on an assessment of the process overshoot. When the real control plant dynamics did not match with the dynamics of the FOPDT model this way was found effective for finding the optimal tuning of the PI controller. The significance of the results obtained was a structural robustness in case of estimated FOPDT model usage. The effectiveness of the procedure has been demonstrated on both linear and nonlinear models. The developed procedure is proposed to be used to automate the procedure of re-tuning SISO control systems when the control loop performance degradation has been detected.

An adaptive fluid-solid coupling time step control algorithm for unsteady conjugate heat transfer

Unsteady simulation with conjugate heat transfer has to tackle the stiffness issue that originates from the wide disparate time scales between fluid and solid heat transfer. In the loosely coupled framework, challenges remain in determining the optimal coupling time step between the fluid and solid solvers, in terms of efficiency and accuracy. This study formulates a correlation for the coupling error, together with the Proportional-Integral-Derivative (PID) control method, to propose an adaptive fluid-solid coupling time step algorithm, which dynamically adjusts the coupling time step such that the computational efficiency can be improved without sacrificing accuracy. The proposed algorithm has been tested in a 1D conjugate heat transfer application with typical operating conditions of a real engine, and the performance of different controllers (I, PI, PID) has been analyzed and compared. Results show that with the closed-loop control, all controllers can ensure the error control below the user specified tolerance at the cost of reducing efficiency. Meanwhile, with the open-loop control which is of more interest to practical applications, controllers suffer from instability issues when a conventional algorithm based on integration error is employed. The proposed coupling-error-based algorithm successfully tackles the instability issues. Compared with the baseline algorithm of the constant coupling time step, the proposed algorithm can reduce approximately 40%–90% computational cost for the 1D cases considered.

Counteracting Bleeding in Centrifugal Partition Chromatography: Redosing of the Stationary Phase

Centrifugal Partition Chromatography is a type of Liquid-Liquid Chromatography that offers several advantages compared to Liquid Chromatography. Two immiscible liquids are utilized, and one phase has to be immobilized to implement chromatographic separation. This is performed with the help of centrifugal force. As this immobilization is not ideal, the stationary phase continuously leaks out of the apparatus (so-called bleeding). We measured the stationary phase’s loss precisely and implemented a controller to compensate for it during operation. This innovative mode of operation is called redosing of the stationary phase and prolongs the experimental runtime significantly. In a first step, we implemented an open-loop controller, which was capable of counteracting bleeding but could not dial to a given setpoint precisely. Therefore, a closed-loop controller with a moving frame shifting factor was programmed. This controller reached and maintained setpoints with high accuracy. The last experimental step was to check for boundaries of this new degree of freedom. In addition, we highlighted the accompanying hydrodynamics during redosing with the help of Computational Fluid Dynamics. We were able to show the influence of different volumes of the redosed stationary phase on the flow regime.

Offline supervised learning v.s. online direct policy optimization: A comparative study and a unified training paradigm for neural network-based optimal feedback control

This work is concerned with solving neural network-based feedback controllers efficiently for optimal control problems. We first conduct a comparative study of two prevalent approaches: offline supervised learning and online direct policy optimization. Albeit the training part of the supervised learning approach is relatively easy, the success of the method heavily depends on the optimal control dataset generated by open-loop optimal control solvers. In contrast, direct policy optimization turns the optimal control problem into an optimization problem directly without any requirement of pre-computing, but the dynamics-related objective can be hard to optimize when the problem is complicated. Our results underscore the superiority of offline supervised learning in terms of both optimality and training time. To overcome the main challenges, dataset and optimization, in the two approaches respectively, we complement them and propose the Pre-train and Fine-tune strategy as a unified training paradigm for optimal feedback control, which further improves the performance and robustness significantly. Our code is accessible at https://github.com/yzhao98/DeepOptimalControl.

Detection of poor controller tuning with Gramian Angular Field (GAF) and StackAutoencoder (SAE)

Efficient control loop performance is pivotal in process industries to ensure optimal production, maintain product quality, and adhere to regulatory standards. Poorly tuned controllers can disrupt these objectives, necessitating accurate detection methods. This paper introduces a novel approach for detecting poor controller tuning through advanced techniques: the Gramian Angular Field (GAF) and Stack Auto-Encoder (SAE). Unlike manual methods, this automated system promptly identifies poorly tuned controllers, offering real-time monitoring and timely alerts to operators. The proposed methodology is substantiated through two case studies: the ISDB dataset and the pulp and paper dataset. The outcomes illustrate that the proposed approach correctly determines the appropriate outcome for the majority of the analyzed control loops across diverse industries.

Power control in LTE based on heuristic game theory for interference management

In the conventional LTE homogeneous network, sufficient transmit power of user equipment (UE) is determined by open-loop power control (OL-PC) and closed-loop power control (CL-PC) schemes. However, in a Het-Net environment, setting the UE’s transmit power requires delicate responsiveness to handle the severe and complicated uplink interference. In this paper, an interference-aware uplink power control mechanism based on Heuristic game theory approach is proposed for devices coexisting in a heterogeneous wireless network. Various wireless constraints like channel response, path loss, fading, shadowing, interference and metrics like SNR, SINR, throughput and bit rates are considered. Uplink power is controlled by suitably selecting the penalization factor (β) based on a simple Heuristic game theory approach considering the possible wireless constraints of each user depending on its location in the cell under consideration. The algorithm is framed in such a way to reduce inter-cell interference, limit transmit power, enhance bit rates and throughput of users. A significant improvement of 5.2% in the user coverage/distribution is achieved as a result of interference management compared to conventional power control scheme and power control with convex pricing.

High authenticity steering feel control strategy for Steer-by-Wire system based on terminal sliding mode rack force observer

This article proposes a high authenticity steering feel control strategy for Steer-by-Wire (SBW) system based on terminal sliding mode rack force observer, in response to the issue of how to design the steering feel of the SBW system to reflect road conditions. Firstly, a terminal sliding mode rack force observer is designed to achieve real-time and accurate estimation of steering system rack force. Then, based on the vehicle motion state, design the feedback characteristic curve of the rack force and the compensation torque of the handwheel to obtain the expected steering feel, and use the automatic disturbance rejection controller (ADRC) method to control the voltage of the handwheel motor. In simulation testing, a SBW open loop control system model is established to verify the performance of the designed rack force observer. The objective evaluation indicators are used to verify that the designed feedback method can intuitively adjust and meet the driver's steering feel needs, and the impact of various compensation torques on the evaluation indicators is analyzed. Finally, the results of actual vehicle experiments indicate that the designed method can provide suitable steering feel, verifying the feasibility of the design technology.

Design of 2DOF control system fused with artificial intelligence for power enhancement and mitigation of degradation in fuel cell systems

The commercialization of Fuel Cell Systems (FCSs) faces a key obstacle in the form of limited cell lifespan and resultant cell degradation. This paper introduces a new approach, leveraging an artificial intelligence-based 2DOF control system to regulate the Oxygen Excess Ratio (OER) with two main goals: mitigating cell degradation due to oxygen starvation and optimizing net power output. The proposed control system incorporates a data-driven feedforward controller in conjunction with a feedback controller, facilitating the tracking of desired OER values generated by a data-driven reference generator. We develop fuzzy models and neural networks as the reference generator and feedforward controller to capture the complex FCS behavior by processing the stack current w/wo the temperature of FCS (i.e. Single input or Double input models). By exploring the effects of the structural settings of the models, this study provides a comprehensive understanding of their impact on the representation performance of the FCS characteristics. Although the fitting performances of all models are quite satisfactory, their actual performance gain is evaluated on a realistic FCS model at various operation points. The findings and comparative analysis emphasize the efficacy of incorporating stack temperature in fuzzy-model-based 2DOF control systems, showcasing their potential to maximize net power output through enhanced OER control loop performance while also extending the lifespan of FCSs, as confirmed by results from a developed degradation model.

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%