Open-loop Control Research Articles

Towards a modelling, optimization and predictive control framework for smart irrigation

Smart irrigation scheduling is a promising approach for improving the efficiency and sustainability of agricultural water use, especially in arid and semi-arid lands. In this paper, we present a design and simulation of a model predictive controller for smart irrigation scheduling. The proposed controller is based on a mathematical model of the irrigation system, which is used to predict the future states of the system and determine the optimal irrigation schedule for a given crop and field. This study further evaluates the impact of the predictive control framework on crop yield, water use efficiency (WUE), and water savings in tomato production. Three control strategies, including manual control, open-loop control, and model predictive control (MPC), were compared using a Completely Randomized Design (CRD) methodology. Results indicate that MPC outperforms the other strategies, yielding the highest average crop yield of 20 t/ha and demonstrating superior WUE at 10.4 kg/m3. Additionally, MPC significantly reduces water consumption, achieving a 29 % and 8 % savings compared to manual and open-loop control, respectively. These findings underscore the efficacy of MPC in optimizing crop yield, conserving water resources, and promoting sustainable agriculture practices. The proposed controller has the potential to address the global water scarcity challenge and contribute to the sustainability of agriculture in arid and semi-arid lands.

A novel linear piezoelectric inchworm actuator based on a ratchet mechanism

Abstract A new working principle of inchworm actuator, which converts vibrations of a single piezo actuator into unidirectional step movement of a mover via a ratchet mechanism, was proposed. The proposed working principle has the following priorities: (1) it requires only one piezoelectric actuator which greatly simplifies its driving signals and driving circuits; (2) it can achieve a large driving speed with little compromise of a large force output while maintaining a high positioning precision of piezoelectric actuator and a theoretically unlimited motion range, although it can only achieve unidirectional movement with unidirectional self-locking capability; (3) it could be open-loop controlled with no accumulated step errors. The proposed actuator was designed with compliant mechanism and an analytical model of the design was developed, validated by finite element simulations carried out in Commercial Software ANSYS and used to guide the selection of design parameters. A prototype was fabricated and tested. Experiments show that the proposed actuator achieved a speed larger than 12 mm s−1, a driving load larger than 60 N in the moving direction, a reliable open-loop controllability with no step accumulated errors even under driving load variations of 60 N, and a working range larger than 1 mm with a high positioning precision around 320 nm under closed-loop control, which validated the superiorities of the proposed actuator.

Modifications to ArduSub That Improve BlueROV SITL Accuracy and Design of Hybrid Autopilot

Improvements to ArduSub for the BlueROV2 (BROV2) Heavy, necessary for accurate simulation and autonomous controller design, were implemented and validated in this work. The simulation model was made more accurate with new data obtained from real-world testing and values from the literature. The manual control algorithm in the BROV2 firmware was replaced with one compatible with automatic control. In a Robot Operating System (ROS), a proportional–derivative (PD) controller to assist augmented reality (AR) pilots in controlling angular degrees of freedom (DOF) of the vehicle was implemented. Open-loop testing determined the yaw hydrodynamic model of the vehicle. A general mathematical method to determine PD gains as a function of the desired closed-loop performance was outlined. Testing was carried out in the updated simulation environment. Step response testing found that a modified derivative gain was necessary. Comparable real-world results were obtained using settings determined in the simulation environment. Frequency response testing of the modified yaw control law discovered that the bandwidth of the nonlinear system had a one-to-one correspondence with the desired closed-loop natural frequency of a simplified linear approximation. The control law was generalized for angular DOF and linear DOF were operated with open-loop control. A full six-DOF simulated dive demonstrated excellent tracking.

A Data-Driven Comprehensive Evaluation Method for Electromagnetic Suspension Maglev Control System

As new advanced vehicles, the safety and stability of electromagnetic suspension maglev trains have always been a subject of concern. This study introduces the improved R index and τ-distance index into the performance evaluation of the suspension control system, respectively assessing the stability of the suspension gap and the smoothness of train operation, combining them with grey relational analysis to achieve data-driven comprehensive evaluation. Furthermore, feasibility tests on the Fenghuang Maglev Express validate the effectiveness and superiority of the comprehensive evaluation method based on measured data. Experimental results demonstrate that the data-driven comprehensive evaluation method, through designing specialized evaluation metrics and increasing assessment dimensions, effectively evaluates the performance of the suspension system control loop. Compared to a traditional error integral comprehensive performance index, it offers greater comprehensiveness and accuracy, along with real-time state-monitoring capabilities.

Modeling and Control of a Road Wheel Actuation Module in Steer-by-Wire System

Since the steer-by-wire system removes the mechanical connection and uses electrical signals to drive the system, it has the disadvantage of being less stable in the failure of parts or systems. Therefore, in this paper, we present a methodology for developing a digital model of the road wheel actuator of the steer-by-wire system. First, the detailed dynamics of the road wheel actuator are analyzed and simplified, and the friction model is estimated and compensated to obtain the equilibrium inertia and damping coefficient of the motor and the road wheel actuator. And to verify the accuracy of the digital model developed based on these parameters, the outputs are compared by giving the same inputs under open-loop control. Furthermore, to solve the problem caused by nonlinear disturbance and model uncertainty, a disturbance observer-based position controller is proposed. The validity of the proposed controller and the validity of the digital model development methodology are confirmed by the results of the position control experiment.

Closed-loop designed open-loop control of quantum systems: An error analysis

Quantum Lyapunov control, an important class of quantum control methods, aims at generating converging dynamics guided by Lyapunov-based theoretical tools. However, unlike the case of classical systems, disturbance caused by quantum measurement hinders direct and exact realization of the theoretical feedback dynamics designed with Lyapunov theory. Regarding this issue, the idea of closed-loop designed open-loop control has been mentioned in literature, which means to design the closed-loop dynamics theoretically, simulate the closed-loop system, generate control pulses based on simulation and apply them to the real plant in an open-loop fashion. Based on bilinear quantum control model, we analyze in this article the error, i.e., difference between the theoretical and real systems’ time-evolved states, incurred by the procedures of closed-loop designed open-loop control. It is proved that the error at an arbitrary time converges to a unitary transformation of initialization error as the number of simulation grids between 0 and that time tends to infinity. Moreover, it is found that once the simulation accuracy reaches a certain level, adopting more accurate (thus possibly more expensive) numerical simulation methods does not efficiently improve convergence. We also present an upper bound on the error norm and an example to illustrate our results.

Smith GA-PID feedback control of hot strip final rolling temperature

Abstract At present, the hot strip finishing temperature control system generally adopts a computer model or mechanism model to predict the strip’s final rolling temperature, which is open-loop control. To realize the precise control of the final rolling temperature of high-performance steel, this paper introduces the PID feedback control strategy at the end of the finishing zone, to realize the high-precision temperature control of the strip steel. The Smith compensator is used to predict the compensation for the large hysteresis problem of the final rolling temperature, and various intelligent algorithms are used to optimize the three parameters of the PID controller, eliminate the nonlinear problem, and obtain better dynamic performance. After the experiment, it is concluded that the Smith GA-PID optimization control strategy system has a small overshoot, the peak time and regulation time are the shortest, and the dynamic response is the fastest. Compared with the traditional PID control method, the overshoot is reduced by 97.6%, and the required regulation time is reduced by 7.85 s.

Identifying optimal location for control of thermoacoustic instability through statistical analysis of saddle point trajectories.

We propose a framework of Lagrangian Coherent Structures (LCSs) to enable passive open-loop control of tonal sound generated during thermoacoustic instability. Experiments were performed in a laboratory-scale bluff-body stabilized turbulent combustor in the state of thermoacoustic instability. We use dynamic mode decomposition on the flow-field to identify dynamical regions where the acoustic frequency is dominant. We find that the separating shear layer from the backward-facing step of the combustor envelops a cylindrical vortex in the outer recirculation zone, which eventually impinges on the top wall of the combustor during thermoacoustic instability. We track the saddle points in this shear layer emerging from the backward-facing step over several acoustic cycles. A passive control strategy is then developed by injecting a steady stream of secondary air targeting the identified optimal location where the saddle points spend a majority of their time in a statistical sense. After implementing the control action, the resultant flow-field is also analyzed using LCS to understand the key differences in flow dynamics. We find that the shear layer emerging from the dump plane is deflected in a direction almost parallel to the axis of the combustor after the control action. This deflection, in turn, prevents the shear layer from enveloping the vortex and impinging on the combustor walls, resulting in a drastic reduction in the amplitude of the sound produced.

Primary Frequency Control with an Air Handling Unit

The green energy transition threatens stability of the power grid due to associated reduction in grid synchronous inertia. Primary frequency control (PFC) can compensate for the challenge; however, sufficient procurement of primary frequency capacity could depend on more extensive employment of demand-side loads for PFC. Ventilation fans in particular present a promising class of such loads because of ubiquity of variable-frequency drives and relatively slow thermal and ventilation dynamics of indoor spaces. This research proposes a novel method for PFC by an air handling unit: the open loop control is shown to have favorable dynamic characteristics, and its impact on indoor climate is shown to be tolerable. This study suggests that the largely unused primary frequency capacity of ventilation fans could be exploited to provide primary frequency response for low inertia power grids.

Apply frequency response function schemes for damage detection in composite nanoscale-pipes under transient conditions

Knowledge of Nanoscale-structures sustainability is strongly associated with the reliability and the performances of Nano-electro-mechanical systems such as Nanoactuators and Nano-sensors that are widely used in bio-applications. Therefore, the damages in these types of infinitesimal systems must be quickly and accurately detected, located, and repaired. In this study, a novel damage detection framework in Carbon Fiber Reinforced Polymer (CFRP) composite Nanoscale-Pipes (CNP) subjected to the fatigue effect of water internal pressure and thermal effect via Nanoscale-electrical capacitance sensors (NECS). First, a finite element model (FEM) of CNP damage is established, then the electrical potential difference (EPD) between the Nanoscale-electrodes (NEs) pairs before and after damage is measured. The distributed NECS measuring nano-electrodes are installed around the CNP and load the transient external excitations between each other. It is proposed and applied the transfer function (TF) of the CNP as an "open loop control" system to reflect the evolution of damage. The accuracy and reliability of the suggested technology are verified through the experimental results available in the literature. The results show that when the damage under NEs pairs has begun to grow, the signal amplitude value will change suddenly and sensitively, which demonstrates the effectiveness of the proposed method and the good potential for engineering applications.

Revisiting kinetic Monte Carlo algorithms for time-dependent processes: From open-loop control to feedback control.

Simulating stochastic systems with feedback control is challenging due to the complex interplay between the system's dynamics and the feedback-dependent control protocols. We present a single-step-trajectory probability analysis to time-dependent stochastic systems. Based on this analysis, we revisit several time-dependent kinetic Monte Carlo (KMC) algorithms designed for systems under open-loop-control protocols. Our analysis provides a unified alternative proof to these algorithms, summarized into a pedagogical tutorial. Moreover, with the trajectory probability analysis, we present a novel feedback-controlled KMC algorithm that accurately captures the dynamics systems controlled by an external signal based on the measurements of the system's state. Our method correctly captures the system dynamics and avoids the artificial Zeno effect that arises from incorrectly applying the direct Gillespie algorithm to feedback-controlled systems. This work provides a unified perspective on existing open-loop-control KMC algorithms and also offers a powerful and accurate tool for simulating stochastic systems with feedback control.

Open-loop quantum control of small-size networks for high-order cumulants and cross-correlations sensing

Quantum control techniques are one of the most efficient tools for attaining high-fidelity quantum operations and a convenient approach for quantum sensing and quantum noise spectroscopy. In this work, we investigate dynamical decoupling while processing an entangling two-qubit gate based on an Ising-xx interaction, each qubit affected by pure dephasing classical correlated 1/f-noises. To evaluate the gate error, we used the Magnus expansion introducing generalized filter functions that describe decoupling while processing and allow us to derive an approximate analytic expression as a hierarchy of nested integrals of noise cumulants. The error is separated in contributions of Gaussian and non-Gaussian noise, with the corresponding generalized filter functions calculated up to the fourth order. By exploiting the properties of selected pulse sequences, we show that it is possible to extract the second-order statistics (spectrum and cross-spectrum) and to highlight non-Gaussian features contained in the fourth-order cumulant. We discuss the applicability of these results to state-of-the-art small networks based on solid-state platforms.

A multi-chamber soft robot for transesophageal echocardiography: continuous kinematic matching control of soft medical robots.

This research investigates designing a continuum soft robot and proposing a kinematic matching control to enable the robot to perform a specified medical task, which in this paper is the transesophageal echocardiography (TEE). A multi-chamber soft robot was designed and fabricated based on the molding of separate layers. The method of transformation matrices was used to develop the kinematic models, and a control method using Jacobian matrices was proposed to manipulate the robot. A prototype was made based on a multi-chamber multi-layer design. The system contains three segments that can be actuated independently to mimic the active bending part of the respective probe. Kinematic models were developed. Negative pressure (vacuum) was used as actuation input. An open-loop controller inspired by a redundancy resolution technique was proposed to make the soft robot tip follow the desired path, i.e.the path of the rigid ultrasound probe. It is concluded that the soft solution can perform the required task as the reachable points of the TEEtip cover the proposed robot workspace and the proposed control can be used for maneuvering in arbitrary trajectories.

Intentional binding for noninvasive BCI control.

Noninvasive brain-computer interfaces (BCIs) allow to interact with the external environment by naturally bypassing the musculoskeletal system.
Making BCIs efficient and accurate is paramount to improve the reliability of real-life and clinical applications, from open-loop device control to closed-loop neurorehabilitation. By promoting sense of agency and embodiment, realistic setups including multimodal channels of communication, such as eye-gaze, and robotic prostheses aim to improve BCI performance. However, how the mental imagery command should be integrated in those hybrid systems so as to ensure the best interaction is still poorly understood.
To address this question, we performed a hybrid EEG-based BCI training involving healthy volunteers enrolled in a reach-and-grasp action operated by a robotic arm. Main results showed that the hand grasping motor imagery timing significantly affects the BCI accuracy evolution as well as the spatiotemporal brain dynamics. Larger accuracy improvement was obtained when motor imagery is performed just after the robot reaching, as compared to before or during the movement. The proximity with the subsequent robot grasping favored intentional binding, led to stronger motor-related brain activity, and primed the ability of sensorimotor areas to integrate information from regions implicated in higher-order cognitive functions.
Taken together, these findings provided fresh evidence about the effects of intentional binding on human behavior and cortical network dynamics that can be exploited to design a new generation of efficient brain-machine interfaces.

Coupled hysteresis and resonance control of three-degree-of-freedom tip-tilt-piston piezoelectric stage

In order to solve the coupled hysteresis and resonance problems in three-degree-of-freedom tip-tilt-piston piezoelectric stage, a coupled hysteresis model and a coupled integral resonance compensation model are designed. The inverse coupled hysteresis feedforward control combined multi-axis coupled resonance control and high-gain integral tracking controller are used to improve the tracking speed and positioning accuracy of the stage. Firstly, a kinematic model of the stage is built to translate the three-degree-of-freedom motion of the end-effector into the outputs of three piezoelectric actuators. Then, a coupled hysteresis model based on the rate-dependent Prandtl–Ishlinskii model is established and parameterized to simultaneously compensate for the hysteresis of a single piezoelectric actuator and the coupled hysteresis between multiple piezoelectric actuators. The effectiveness of the model is verified by open-loop and closed-loop inverse model feedforward control experiments. Afterwards, a coupled integral resonance compensation model is also established and parameterized to suppresses the resonance and expand the system bandwidth. Finally, the proposed controller is used to conduct tracking experiments on a composite signal, and the results show that the accuracy and speed of trajectory tracking are further improved, and the mechanical coupling between axes are significantly reduced.

Asymptotic method for solving the problem of transition process optimization in a three-tempo singularly perturbed system

The problem of constructing a transition process with minimal energy costs for a linear singularly perturbed system containing three groups of variables with significantly different rates of change is considered. Asymptotic approximations to solving this problem are constructed in the form of an open-loop and feedback controls. The main advantage of the proposed computational procedures is that the original problem is split into three unperturbed optimal control problems of lower dimension.

Genetically-Based Active Flow Control And PIV Measurements Of A Circular Cylinder Wake

In the field of flow manipulation, considerable attention is devoted to controlling the wake of bluff bodies, which are characterized by the phenomenon of vortex shedding. Specifically, the control of the wake behind a single circular cylinder has garnered substantial research interest due to its engineering applications, such as reducing aerodynamic drag in the automotive and aerospace industries. Therefore, in the current work, through the application of the planar PIV technique, the fluid dynamic behavior of a circular cylinder wake controlled via synthetic jets (SJ) and Machine Learning (ML) methods is analyzed and discussed. ML algorithms are used to determine the optimal voltage signal for the loudspeaker, which effectively minimizes the aerodynamic drag of the cylinder. This approach overcomes the limitations associated with the a priori assumption of a parametric waveshape. In particular, the gradient-enriched machine learning control (gMLC) algorithm is chosen as optimization tool to search the best open-loop control policy for aerodynamic drag reduction. The cost function for this problem has been defined considering both the drag reduction contribution and the electrical power spent for the actuation. Initial optimization tests are performed by minimizing a cost function considering only the reduction in aerodynamic drag. Subsequently, a term related to the electrical power consumption for actuation is incorporated into the cost function. Once obtained the optimal control policies for these cases (hereinafter renamed CC1 and CC2, respectively), the physical mechanism responsible for reducing the aerodynamic drag is investigated more closely by analyzing the effect of the control laws on the turbulent flow statistics. Figure 1 reports a comparative assessment of the CC1 and CC2 with the baseline configuration and the configuration controlled by a sinusoidal law (CCS). The baseline configuration is characterized by an extended region of localized turbulent fluctuations in the far wake, where the time-averaged signature of von Kármán vortices is found. In contrast, all the controlled configurations show a global reduction in wake turbulence, with a peak of turbulent energy at the rear stagnation point due to the momentum injection from the SJ actuator. Notably, the CCS and CC2 configurations share similar flow field morphologies, while the CC1 configuration shows a greater reduction in turbulent fluctuations compared to the other controlled cases. This control law also achieves the largest drag reduction among all configurations studied reporting a 9.7% of drag reduction, compared to CCS and CC2, which exhibit similar reductions of 8.4% and 8.5%, respectively. Therefore, the optimization procedure has proven the capability of finding control policies able to outperform the traditional sinusoidal control widely studied in literature.

New Opportunities in Real-Time Diagnostics of Induction Machines

This manuscript addresses the critical challenges in achieving high-accuracy remote control of electromechanical systems, given their inherent nonlinearities and dynamic complexities. Traditional diagnostics often suffer from data inaccuracies and limitations in analytical techniques. The focus is on enhancing the dynamic model accuracy for remote induction motor control in both closed- and open-loop speed control systems, which is essential for real-time process monitoring. The proposed solution includes real-time measurements of input and output physical quantities to mitigate inaccuracies in traditional diagnostic methods. The manuscript discusses theoretical aspects of nonlinear torque formation in induction drives and introduces a dynamic model employing vector control and speed control schemes alongside standard frequency control methods. These approaches optimize frequency converter settings to enhance system performance under varying nonlinear conditions. Additionally, the manuscript explores methods to analyze dynamic, systematic errors arising from frequency converter inertial properties, thereby improving electromechanical equipment condition diagnostics. By addressing these challenges, the manuscript significantly advances the field, offering a promising future with enhanced dynamic model accuracy, real-time monitoring techniques, and advanced control methods to optimize system reliability and performance.

Stiction parameter identification for pneumatic valves with a simultaneous approach

In industrial processes, stiction in control valves is a common cause of degradation in control loop performance. It is important to identify and establish reliable stiction models to enhance control loop performance. This study focuses on constructing a precise model of the pneumatic control valve using the LuGre friction model. To improve the model, a smooth function based on probability density is introduced to alleviate the LuGre friction model. The maximum error resulting from this smoothing process is also analyzed. To estimate the valve stiction parameters, the direct transcription method is utilized to convert the problem from systems of ordinary differential equations into a nonlinear programming problem. The interior point method is then used to solve this problem. Furthermore, the estimability of the parameters is analyzed based on the reduced Hessian matrix before estimation. Numerical results demonstrate that the proposed approach in this study effectively estimates the stiction parameters of pneumatic control valves.

Robustness of dynamic quantum control: Differential sensitivity bounds

Dynamic control via optimized, piecewise-constant pulses is a common paradigm for open-loop control to implement quantum gates. While numerous methods exist for the synthesis of such controls, there are many open questions regarding the robustness of the resulting control schemes in the presence of model uncertainty; unlike in classical control, there are generally no analytical guarantees on the control performance with respect to inexact modeling of the system. In this paper, a new robustness measure based on the differential sensitivity of the gate fidelity error to parametric (structured) uncertainties is introduced, and bounds on the differential sensitivity to parametric uncertainties are used to establish performance guarantees for optimal controllers for a variety of quantum gate types, system sizes, and control implementations. Specifically, it is shown how a maximum allowable perturbation over a set of Hamiltonian uncertainties that guarantees a given fidelity error can be reliably computed. This measure of robustness is inversely proportional to the upper bound on the differential sensitivity of the fidelity error evaluated under nominal operating conditions. Finally, the results show that the nominal fidelity error and differential sensitivity upper bound are positively correlated across a wide range of problems and control implementations, suggesting that in the high-fidelity control regime, rather than there being a trade-off between fidelity and robustness, higher nominal gate fidelities are positively correlated with increased robustness of the controls in the presence of parametric uncertainties.