Open-loop Control Approach Research Articles

Biomimetic Closed-Loop Control of a Novel Soft Gastric Simulator Toward Emulating Antral Contraction Waves.

Soft gastric simulators are in vitro biomimetic modules that can reproduce the antral contraction waves (ACWs). Along with providing information concerning stomach contents, stomach simulators enable experts to evaluate the digestion process of foods and drugs. Traditionally, open-loop control approaches were implemented on stomach simulators to produce ACWs. Constructing a closed-loop control system is essential to improve the simulator's ability to imitate ACWs in additional scenarios and avoid constant tuning. Closed-loop control can enhance stomach simulators in accuracy, responding to various food and drug contents, timing, and unknown disturbances. In this article, a new generation of anatomically realistic soft pneumatic gastric simulators is designed and fabricated. The presented simulator represents the antrum, the lower portion of the stomach where ACWs occur. It is equipped with a real-time feedback system to implement diverse closed-loop controllers on demand. All the details of the physical design, fabrication, and assembly process are discussed. Also, the measures taken for the mechatronics design and sensory system are highlighted in this article. Through several implementation algorithms and techniques, three closed-loop controllers, including model-based and model-free schemes are designed and successfully applied on the presented simulator to imitate ACWs. All the experimental outcomes are carefully analyzed and compared against the biological counterparts. It is demonstrated that the presented simulator can serve as a reliable tool and method to scrutinize digestion and promote novel technologies around the human stomach and the digestion process. This research methodology can also be utilized to develop other biomimetic and bioinspired applications.

Implementation of unidirectional control mechanism for DC-DC converters

In the ongoing global transition from non-renewable energy sources to renewables such as solar, wind, and tidal power, solar energy plays a predominant role. Take, for instance, the widespread use of solar-powered e-rickshaws. These vehicles require a consistent and lowharmonics DC supply to charge their batteries. However, during overcast days, the solar panels on the e-rickshaws may struggle to provide the necessary power for battery charging, leading to potential interruptions. One effective solution to address this challenge involves implementing a DC-DC converter to maintain a stable voltage supply to the load. Three types of DC converters have been designed and their input-output voltage relations are calculated and compared. All three converters are simulated using MATLAB with an open-loop control approach.

Design of two-dimensional piezoelectric laser scanner system for precision laser beam steering.

In this work, a large aperture two-dimensional (2D) piezoelectric laser scanner system with an onboard sensor to detect the scanner's deflection angle has been designed and prototyped. The proposed 2D laser scanner is driven using three amplified piezoelectric actuators assembled in an equilateral triangle configuration to provide structural stability and compactness. To overcome the nonlinearity that derives from the hysteresis effect of the piezoelectric actuators, the tip-tilt principle and the control schemes of the 2D scanner are analyzed and compared. Thanks to the combination of onboard sensing of the deflection angle of the scanner with the closed-loop control capability, this 2D scanner's features include a large aperture and high accuracy. The experimental results demonstrate that closed-loop control achieves higher control accuracy than the open-loop control approach, leading to a reduction in the relative error from 2%-4% to ∼0.5%, while the deflection angle tracking accuracy lies approximately within the 40 μrad range.

Artificial neural networks for adaptive control of profiled haemodialysis in patients with renal insufficiency

Background and objectiveCurrently, haemodialysis treatment is performed using an open-loop control approach, with initial settings of parameters such as ultrafiltration rate and dialyser composition being adapted to the current haemodynamic condition of each patient, although unexpected events may require additional adjustments to be made.Therefore, an artificial neural network-based approach has been presented to automatically control the ultrafiltration rate according to the specific patient conditions during the haemodialysis session, in order to regulate body weight loss, and the elimination of electrolytes and uremic toxins. MethodsThis modelling task is performed using a mathematical model of fluid and solute exchange based on first principles, which is used to simulate the process of a haemodialysis session in a specific patient under SIMULINK in order to define the underlying dynamic equations. Alongside this, MATLAB neural network tools are used to adjust the settings of the automatic controller for different body weight loss regulation profiles and variable dialysate sodium conditions during haemodialysis treatment. ResultsComputer simulation results show the adequate performance of the body weight loss neuroadaptive control system when submitted to different haemodialysis patterns, uremic toxins and sodium elimination evolution under changing dialysate sodium conditions. ConclusionsThe proposed approach proves to be a valuable tool as a test bench for the assessment of alternate haemodialysis profiles aimed to improve the treatment of patients by preventing dialysis-induced haemodynamic complications. The adaptive nature of the model-based control approach here presented makes it possible to personalize the haemodialysis treatment to the specific needs of each patient.

Control of Fractional Diffusion Problems via Dynamic Programming Equations

We explore the approximation of feedback control of integro-differential equations containing a fractional Laplacian term. To obtain feedback control for the state variable of this nonlocal equation, we use the Hamilton–Jacobi–Bellman equation. It is well known that this approach suffers from the curse of dimensionality, and to mitigate this problem we couple semi-Lagrangian schemes for the discretization of the dynamic programming principle with the use of Shepard approximation. This coupling enables approximation of high-dimensional problems. Numerical convergence toward the solution of the continuous problem is provided together with linear and nonlinear examples. The robustness of the method with respect to disturbances of the system is illustrated by comparisons with an open-loop control approach.

Experiment and Validation of Open-Loop Control on the Simulation of a Cable-Driven Planar Robot for Drawing and Writing Tasks

In this paper, we introduce a groundbreaking cable-driven parallel robot meticulously engineered for precision tasks such as drawing and 2D printing, For example, drawing on boards, fabric, or two-dimensional prints for decorations, and so forth. Delving into the specifics of its design, we emphasize key features that optimize its performance and accuracy in executing these creative tasks. Furthermore, we outline a comprehensive set of validation experiments meticulously designed to showcase the robot's capabilities. These experiments rigorously test and validate the efficacy of our chosen open-loop control approach, providing in-depth insights into the remarkable results achieved during this validation process. The obtained results not only underline the potential of this innovative robot but also offer valuable insights for advancing similar applications in the future. This unique robot is built upon an inherently unstable base, a distinctive departure from conventional robotic systems. At the core of its operation, four motors are strategically installed at the ends of the unstable base, each responsible for winding the cable around a pulley to ultimately manipulate the end effector. The work presented in this paper is organized into three main sections. Firstly, the authors provide a comprehensive definition of the cable-driven parallel robot and its intended applications. Secondly, the paper delves into the development of the geometric and kinematic model of the robot, offering insights into the underlying principles governing its motion. Finally, the third section details the simulation of the proposed robot, conducted using Solidworks and LabView software, offering a practical demonstration of the robot's capabilities and performance characteristics. This work represents a significant contribution to the field of cable-driven robotics, with potential applications in diverse domains, from artistic endeavors to precise manufacturing processes.

A fuzzy logic-based intelligent decision support system for the selection of an appropriate input-shaping technique for controlling flexible link systems

ABSTRACT Most robotic systems consist of heavy machine elements that help reduce the vibrations generated by motion and that support good positional accuracy. Large-sized actuators are needed for such systems. Energy consumption significantly increases in oversized robotic systems. The proposed solution to which is to design robotic systems in lightweight and flexible forms. This makes possible smaller overall structure and actuator sizes. However, such systems suffer from residual vibrations. One well-known control solution to residual vibration problem could be input shaping techniques. In this study, a new Fuzzy Logic-based intelligent input shaping selection technique has been proposed. With this proposed approach, it is possible to set optimum settling time, positioning accuracy of robotic systems, and minimum residual vibrations by implementing the crucial open-loop control approach. Comprehensive experience is needed to overcome design issues on command shaping and system modeling. Based on these design criteria constraints and extensive knowledge requirements, the recommended solution is a Fuzzy Logic-based intelligent input shaping technique for accurate modeling and parameter estimation of the systems. The proposed approach is a Fuzzy Logic-based intelligent input shaper selection and input shaper setting determination algorithm. The applicability of the proposed method was verified on the Quanser Flexible Link experimental setup.

Two-degree-of-freedom flow-induced vibration suppression of a circular cylinder via externally forced rotational oscillations: comparison of active open-loop and closed-loop control systems

In the current work, both active open-loop and closed-loop control approaches are considered to reduce two-degree-of-freedom vortex-induced vibration (VIV) of a circular-section cylinder, by means of externally imposed rotational oscillations. The open-loop control method utilizes pre-specified rotary oscillations, while the active closed-loop controller computes the proper angular velocity depending on the feedback signal of lift coefficient. The interactions between fluid and structure are realized by utilizing of the moving mesh methodology by means of user-defined function, which incorporated into the central code of computational fluid dynamic solver. According to the numerous set of numerical simulations, three optimum open-loop control systems for appropriate suppression of cylinder vibration are selected. In particular, it is observed that the VIV suppression capability of the closed-loop control strategy in comparison with optimum open-loop systems is more desirable despite the lower level of consuming control effort. Moreover, it is detected the excellent performance of selected closed-loop controller with constant proportional control gain in the entire range of reduced velocities, while the optimum open-loop systems are executable mainly near their corresponding reduced velocities. In particular, the selected closed-loop VIV control system (Kp = 10) is found to suppress the maximum transverse response amplitudes by 99.6%, 99.5%, and 99.3% for reduced velocities Vr = 5, 6, and 7, respectively, while the associated in-line displacement amplitudes decrease by 99.2%, 98.3%, and 96.2%. Also, the closed-loop controller does not require to find the “lock-on” frequency to execute appropriate de-synchronization type action and can effectively shift the C(2S)-mode vortices to the typical von Karman 2S-mode vortex shedding.

Investigations on Bending Characteristics of Soft Mesh Structure using Shape Memory Alloy Spring Towards Bio-Inspired Robotic Applications

A biomimetic soft worm robot has been developed using soft mesh structured polyethylene terephthalate thermoplastic driven by shape memory alloy springs. The bending behaviour of the robot for achieving steering in peristaltic and bending for two anchor locomotion has been investigated in detail. A flex sensor is attached with the soft worm to measure the bending/steering angle. An open-loop control approach is adopted for the study of soft worm’s bending actions. In peristaltic crawling, a bending angle of 57°, 48° and 48° was achieved in upward, left and right directions, respectively. The movement of the soft worm robot has been probed towards imitating the bending action of two anchor crawling locomotion with kapton polyamide legs. In two anchor crawling, a bending angle of 60° is achieved in downward direction.

State-based open-loop control of plant growth by means of water stress training

In this paper a first evidence is provided that the targeted control of adaptive plant behavior for irrigation purposes is possible. The objective is aligning plant growth to water availability (and not vice versa) and utilizing training mechanisms to affect the relation between water use and plant growth. The approach is based on the experimental specification of two water deficit-related behavioral patterns: memory of stress and point of no return (damage). Mild stress duration has to be shorter than 2.7 days to avoid irreversible growth rate reduction (maximum stress duration time). Water stress information is stored (memorized) by the plant for three days at most (maximum water stress memory time). Therefore, adequate stress stimuli have to be repeated within this period to maintain training effect. Exceeding maximum memory time without stimulus results in a drop of water-based growth performance (growth [cm]water [g]) back to the level of untrained plants. In control experiments two different plant growth performance ranges were identified: ‘Hydrological time’ performance range without activated memory, and ‘usage-bound’ performance range in memorized states. ‘Usage-bound’ growth performance range shows 47 % higher water-based growth performance than ‘hydrological time’-based. An open-loop control approach is developed to control growth and water consumption using the intended alternation between the two performance ranges. The plant behavior due to water stress is modelled as a state machine (method of a conditioned automaton (control engineering)) representing directly the control algorithm. Based on the statistical validation results it can be concluded that training plants with intended stress sequences allows the control of plant growth and water use.

Stochastic analysis of genetic feedback controllers to reprogram a pluripotency gene regulatory network.

Cellular reprogramming is traditionally accomplished through an open loop (OL) control approach, wherein key transcription factors (TFs) are injected in cells to steer the state of the pluripotency (PL) gene regulatory network (GRN), as encoded by TFs concentrations, to the pluripotent state. Due to the OL nature of this approach, the concentration of TFs cannot be accurately controlled. Recently, a closed loop (CL) feedback control strategy was proposed to overcome this problem with promising theoretical results. However, previous analyses of the controller were based on deterministic models. It is well known that cellular systems are characterized by substantial stochasticity, especially when molecules are in low copy number as it is the case in reprogramming problems wherein the gene copy number is usually one or two. Hence, in this paper, we analyze the Chemical Master Equation (CME) for the reaction model of the PL GRN with and without the feedback controller. We computationally and analytically investigate the performance of the controller in biologically relevant parameter regimes where stochastic effects dictate system dynamics. Our results indicate that the feedback control approach still ensures reprogramming even when both the PL GRN and the controller are stochastic.

From Physics to Bioengineering: Microbial Cultivation Process Design and Feeding Rate Control Based on Relative Entropy Using Nuisance Time.

For historic reasons, industrial knowledge of reproducibility and restrictions imposed by regulations, open-loop feeding control approaches dominate in industrial fed-batch cultivation processes. In this study, a generic gray box biomass modeling procedure uses relative entropy as a key to approach the posterior similarly to how prior distribution approaches the posterior distribution by the multivariate path of Lagrange multipliers, for which a description of a nuisance time is introduced. The ultimate purpose of this study was to develop a numerical semi-global convex optimization procedure that is dedicated to the calculation of feeding rate time profiles during the fed-batch cultivation processes. The proposed numerical semi-global convex optimization of relative entropy is neither restricted to the gray box model nor to the bioengineering application. From the bioengineering application perspective, the proposed bioprocess design technique has benefits for both the regular feed-forward control and the advanced adaptive control systems, in which the model for biomass growth prediction is compulsory. After identification of the gray box model parameters, the options and alternatives in controllable industrial biotechnological processes are described. The main aim of this work is to achieve high reproducibility, controllability, and desired process performance. Glucose concentration measurements, which were used for the development of the model, become unnecessary for the development of the desired microbial cultivation process.

Receding horizon control for oil reservoir waterflooding process

ABSTRACTWaterflooding is a recovery technique where water is pumped into an oil reservoir for increase in production. Changing reservoir states will require different injection and production settings for optimal operation which can be formulated as a dynamic optimization problem. This could be solved through optimal control techniques which traditionally can only provide an open-loop solution. However, this solution is sensitive to uncertainties which is inevitable to reservoirs. Direct feedback control has been proposed recently for optimal waterflooding operations with the aim to counteract the effects of reservoir uncertainties. In this work, a feedback approach based on the principle of receding horizon control (RHC) was developed for waterflooding process optimization. Application of RHC strategy to counteract the effect of uncertainties has yielded gains that vary from 0.14% to 19.22% over the traditional open-loop approach. The gain increases with introduction of more uncertainties into the configuration. The losses incurred as a result of the effect of feedback is in the range of 0.25%–15.21% in comparison to 0.39%–31.51% for the case of traditional open-loop control approach.

Accuracy Quantification and Improvement of Serial Micropositioning Robots for In-Plane Motions

High positioning accuracy with micropositioning robots (MPRs) is required to successfully perform many complex tasks, such as microassembly, manipulation, and characterization of biological tissues and minimally invasive inspection and surgery. Despite the widespread use of high-resolution micro- and nanopositioning robots, there is very little knowledge about the real positioning accuracy that can be obtained and what the main influential factors are. Indeed, very few notable methods are available to measure multi-degree-of-freedom motions with adapted range, resolution, and dynamic capabilities. The main objective of this paper is to quantify the positioning accuracy of serial MPRs and to identify the main influential factors (a typical $XY$ $\Theta$ serial robot is chosen as a case study). To reach this goal, a measuring system that combines vision and pseudoperiodic patterns with an extremely large range-to-resolution ratio is introduced as a new way to quantify the positioning accuracy of MPRs for in-plane motions. Then, an open-loop control approach based on MPR calibration is chosen for several reasons: the use of different models to identify influential factors, the quantification of the positioning accuracy, and the necessity of the method when sensor integration is too complex. Experiments using five different calibration models were conducted to classify factors influencing the positioning accuracy of MPRs. The results show that positioning accuracy can be improved by more than 35 times from 96 $\mu \text{m}$ with no imperfection compensation to 2.5 $\mu \text{m}$ by compensating for geometric, position-dependent, and angle-dependent errors through the MPR calibration approach.

Control of fast crane operation

Abstract This research provided a simple vibration control method for fast crane operation using an open-loop control approach. With the equations of double pendulum motion, we used two related acceleration and deceleration to propose a three-stage method consisting of (1) piecewise acceleration, (2) constant speed, and (3) piecewise deceleration. Our method is to reduce the sway angle and to reach high speed operation. The proposed method maintains a small sway angle in fast speed while the other open-loop methods do not. Our method is studied both numerically and experimentally. We found out that in numerical simulations the operational time was approximately proportional to the square root of the operation distance; a fourfold increase in distance only required a 200% increase in operational time. The method is stable with respect to the 10% variations of system parameters. In comparison to a common method in 100 m operation distance, the operation time is 25% faster when the maximum sway angle is the same, and the sway angle is 44% smaller when the operation time is the same. We conducted a KUKA Robot KR 16 experiment to validate the proposed method. The KUKA experiments suggest a four times smaller vibration magnitude in the proposed method. Moreover, the numerical simulations of the proposed method compare favorably with the KUKA experiments with 95% confidence interval. With the scaled parameters, the KUKA experiment corresponds to 30 s operation time and 180 m operation distance in the real crane situation. The proposed method is safe and fast, and without the addition of any sensors the operator was able to control the sway of the crane cable.

Optimal Controller Design of Legless Piezo Capsubot Movement

This paper presents an optimal control method based on control effort minimization for a legless piezo capsubot. The capsubot is an underactuated nonlinear dynamics system which is driven by an internal impact force and friction. Here, the motion mechanism of the capsubot is divided into two stages. In the rst stage, the aim is to design an optimal controller minimizing energy consumption. In the second stage, optimization is not an objective and instead a four-step strategy for inner mass of the capsubot is proposed. Then, based on the proposed motion strategy, a trajectory prole is given. Using this trajectory prole, the capsubot moves in the desired direction. To evaluate the performance of the proposed control scheme, a comparative study has been performed by means of simulation. Simulation results show that the proposed approach is promising as compared to the Open-Loop Control (OLC) approach and Close-Loop control (CLC) approach which are widely used in the literature for control of capsubots.

Adaptive Prefilter Design for Control of Quasistatic Microscanners

This paper describes an open loop control approach with analog impedance feedback damping for quasistatic MEMS microscanners based on electrostatic comb transducers. The control task is determined by high dynamic input trajectory following in the presence of an extremely lightly damped mechanical mass-spring system with nonlinear electromechanical characteristics. The approach presented in this paper makes use of a model-based design approach resulting in a nonlinear adaptive prefilter for optimally preshaping of customized trajectory inputs. The open loop control is augmented by an easy to implement resistive analog impedance feedback scheme that introduces electromechanical damping and increases robustness against physical parameter uncertainties of the MEMS microscanner assembly. The paper outlines the underlying MEMS microscanner technology, it describes the mathematical model based on ANSYS computation, and it discusses in detail the proposed control concepts. Simulations and experimental results prove the applicability of the control approach.

Mathematical modeling and trajectory planning of mobile manipulators with flexible links and joints

This paper is concerned with mathematical modeling and optimal motion designing of flexible mobile manipulators. The system is composed of a multiple flexible links and flexible revolute joints manipulator mounted on a mobile platform. First, analyzing on kinematics and dynamics of the model is carried out then; open-loop optimal control approach is presented for optimal motion designing of the system. The problem is known to be complex since combined motion of the base and manipulator, non-holonomic constraint of the base and highly non-linear and complicated dynamic equations as a result of the flexible nature of both links and joints are taken into account. In the proposed method, the generalized coordinates and additional kinematic constraints are selected in such a way that the base motion coordination along the predefined path is guaranteed while the optimal motion trajectory of the end-effector is generated. This method by using Pontryagin’s minimum principle and deriving the optimality conditions converts the optimal control problem into a two point boundary value problem. A comparative assessment of the dynamic model is validated through computer simulations, and then additional simulations are done for trajectory planning of a two-link flexible mobile manipulator to demonstrate effectiveness and capability of the proposed approach.

Path Planning of Mobile Elastic Robotic Arms by Indirect Approach of Optimal Control

Finding optimal trajectory is critical in several applications of robot manipulators. This paper is applied the open-loop optimal control approach for generating the optimal trajectory of the flexible mobile manipulators in point-to-point motion. This method is based on the Pontryagin's minimum principle that by providing a two-point boundary value problem is solved the problem. This problem is known to be complex in particular when combined motion of the base and manipulator, nonholonomic constraint of the base and highly non-linear and complicated dynamic equations as a result of flexible nature of links are taken into account. The study emphasizes on modeling of the complete optimal control problem by remaining all nonlinear state and costate variables as well as control constraints. In this method, designer can compromise between different objectives by considering the proper penalty matrices and it yields to choose the proper trajectory among the various paths. The effectiveness and capability of the proposed approach are demonstrated through simulation studies. Finally, to verify the proposed method, the simulation results obtained from the model are compared with the results of those available in the literature.

Robust Trajectory Planning and Robust NMPC of Processes with Model Uncertainties

This paper presents an approach for robust open-loop and robust closed-loop control of biological production processes described by models which comprise uncertain initial conditions and parameter uncertainties. The application of these robust techniques dramatically decreases the variability of the realized process trajectory and hence, the fluctuation of the product amount. The algorithm utilizes a second order approximation to calculate the first two statistical moments of the system's output as a function of the stochastic system's state and uncertain model parameters. Extensive simulation studies of both open- and closed-loop controlled fermentations based on a simple unstructured production model as well as on a structured compartment model consisting of 7 biotic states are shown.