Open-loop Control Research Articles

Examining the physical and psychological effects of combining multimodal feedback with continuous control in prosthetic hands

Myoelectric prosthetic hands are typically controlled to move between discrete positions and do not provide sensory feedback to the user. In this work, we present and evaluate a closed-loop, continuous myoelectric prosthetic hand controller, that can continuously control the position of multiple degrees of freedom of a prosthesis while rendering proprioceptive feedback to the user via a haptic feedback armband. Twenty-eight participants without and ten participants with upper limb difference (ULD) were recruited to holistically evaluate the physical and psychological effects of the controller via isolated control and sensory tasks, dexterity assessments, embodiment and task load questionnaires, and post-study interviews. The combination of proprioceptive feedback and continuous control enabled more accurate position and force modulation than without proprioceptive feedback, and restored blindfolded object identification ability to open-loop discrete controller levels. Dexterity assessment and embodiment questionnaire results revealed no significant physical performance or psychological embodiment differences between control types, with the exception of perceived sensation questions, which were significantly higher (p < 0.001) for closed-loop controllers. Key differences between participants with and without ULD were identified, including increasingly lower perceived body completeness and heterogeneity in frustration in participants with ULD, which can inform future development and rehabilitation.

Control Mechanisms of Sensorimotor System on Manipulation of Proprioceptive Inputs During Balance Maintenance

Proprioceptive inputs have crucial roles in control of the posture. The aim of the present study was to assess the effect of interfering with these signals on postural stability by ice-induced anaesthesia and local calf muscle fatigue. Seventeen healthy young individuals participated in this study to stand quietly and on an unstable platform under normal, anaesthesia, and fatigue conditions. A force platform calculated excursions of centre of pressure. Stabilogram-diffusion analysis was utilised to evaluate how body controls the posture with and without proprioceptive inputs. Time intervals of using the sensory feedback is significantly increased by anaesthesia in quiet standing (430 ms, p = 0.034) to note more delayed use of sensory information in a closed-loop. Additionally, fatigue significantly increased the time intervals of using sensory feedback during standing on the unstable platform (290 ms, p = 0.016). Interestingly, sensory interventions had no effect on the stability of the open-loop control of posture (short-term control), but they significantly influenced the closed-loop control (long-term control) (p < 0.004). Specifically, fatigue led to increased instabilities when the body used sensory inputs during both quiet standing (p = 0.021) and standing on the unstable platform (p = 0.041). These findings highlight the importance of proprioception in balance control for healthy individuals. Interfering with proprioceptive inputs, either through anaesthesia or fatigue, resulted in instabilities during balance maintenance. Our study provides new insights into the mechanisms underlying postural control, emphasising the significance of proprioceptive inputs. Understanding how proprioception affects balance maintenance may have implications for rehabilitation strategies, injury prevention, and the development of interventions to improve postural stability.

Uncertainty Optimization of Industrial Production Operations Considering the Stochastic Performance of Control Loops

Process optimization is a highly successful method for achieving optimal efficiency in industrial production. The conventional optimization approach presupposes that the operational parameters should align with the optimization settings. However, it fails to consider that, influenced by the stochastic performance of the control loops, the operating parameters may deviate from the optimal operating settings. Consequently, this results in the violation of constraints in the optimization results and affects production safety. Therefore, this paper proposes an uncertainty optimization method that considers the stochastic performance of control loops to accurately determine the optimal operational performance that can be practically achieved in industrial production. Firstly, a multi-optimization variational mode decomposition strategy is developed to precisely extract the smooth random and trend terms of the control loop output data. Secondly, the random grouping smooths out the random terms and accurately characterizes the uncertainty associated with these terms. Subsequently, a moment uncertainty set with mild mean-zero net condition is then defined to construct an improved distribution robust optimization model considering the stochastic performance of control loops. Finally, the validation of the proposed optimization method in the actual hydrocracking process shows that the optimization error of the proposed method is reduced by more than 10%, and the constraint violation rate is reduced by 14%, which fully proves the effectiveness and applicability of the method.

Input-Output Cross Autocorrelation Diagram (IO-CAD) for control loop performance assessment in offshore oil production

Severe slugging is a prevalent issue in offshore oil wells that significantly hampers oil production. While active pressure control has proven effective in mitigating this problem, determining optimal setpoints remains a manual and labor-intensive procedure. This study introduces the Input-Output Cross Autocorrelation Diagram (IO-CAD), which examines both input and output autocorrelations to provide a more comprehensive assessment of control loops compared to previous methods that only use output autocorrelation data. Four indicators based on IO-CAD were developed and tested in two case studies involving offshore oil production. They were compared to an oscillation detection method published in the literature, as oscillations in the control loop may indicate the slugging flow. Early detection of slugging patterns is crucial in offshore oil production to prevent severe slugging and stabilize control loops. The results demonstrated that the IO-CAD indicators are robust against setpoint changes, disturbances, and noise in the control loop performance assessment, while the oscillation detection method from the literature is sensitive to measurement and process noise, as well as control loop oscillations.

Smooth braking control of excavator hydraulic load based on command reshaping.

Excavators, a type of human-operated construction machinery, suffer from poor hydraulicload braking stability, which seriously affects operator comfort. To address this challenge, this study investigates load braking laws through model analysis and designs an open-loop control algorithm called command reshaping, which can prolong the small-opening time of the main valve by segmentally adjusting the joystick command during load braking and then actively adjusting the key parameters reflecting the system's kinetic-potential energy state, thereby suppressing braking oscillations. The experimental results based on a 1.5-ton excavator indicated that the proposed algorithm can significantly enhance braking stability with a small stop-position deviation, the maximum amplitude of the working pressure of the boom cylinder was reduced by at least 40 %, and the maximum amplitude of the swing motor speed was reduced by at least 54 %. Furthermore, the algorithm is easily implemented, cost-free, and suitable for the performance tuning of excavators.

Linear controller for wheel loader trajectory tracking

The paper proposes a trajectory-tracking controller for an autonomous small-sized skidsteer wheel loader. The control system consists of two parts: feedforward control and feedback control. The open loop control uses the wheel loader desired velocities and accelerations. The feedback controller is a state-space feedback controller based on a linearized kinematic model.

Camera-based control system of a planar cable-driven parallel robot intended for functional rehabilitation

Abstract This paper investigates a closed-loop visual servo control scheme for controlling the position of a fully constrained cable-driven parallel robot (CDPR) designed for functional rehabilitation tasks. The control system incorporates real-time position correction using an Intel RealSense camera. Our CDPR features four cables exiting from pulleys, driven by AC servomotors, to move the moving platform (MP). The focus of this work is the development of a control scheme for a closed-loop visual servoing system utilizing depth/RGB images. The developed algorithm uses this data to determine the actual Cartesian position of the MP, which is then compared to the desired position to calculate the required Cartesian displacement. This displacement is fed into the inverse kinematic model to generate the servomotor commands. Three types of trajectories (circular, square, and triangular) are used to test the controller’s compliance with its position. Compared to the open-loop control of the robot, the new control system increases positional accuracy and effectively handles cable behavior, various perturbations, and modeling errors. The obtained results showed significant improvements in control performance, notably reduced root mean square error and maximal error in terms of position.

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.

Determining Key Factors for the Open-Loop Control of Molecular Fragmentation Using Shaped Strong Fields.

Pulse shaping has long been employed for tailoring femtosecond laser pulses to study and control the fragmentation of polyatomic molecules. In many cases, a physical explanation connecting the properties of the field to the observed control is difficult to ascertain. We utilized 80 bit binary spectral phase functions to parametrize and map the search space, gaining insight into which pulse parameters most impact the ion yield and fragmentation pattern for the relatively large triethylamine [N(C2H5)3] molecule. Pulse structures used to control the m/z 86 branching ratio beyond a simple intensity dependence are identified and compared to pump-probe results. All of these findings are explained in terms of control via a dissociative Rydberg state in the neutral molecule. This methodology may be used to discover new control mechanisms and shed light onto which pulse parameters most influence the interaction between strong field lasers and matter.

Assessment and deployment of a LSTM-based virtual sensor in an industrial process control loop

AbstractMeasurement of certain variables within the industrial sector remains a challenge due to the prohibitive costs of sensors, the intricate installation processes, or the continuous nature of production demands. Moreover, if a backup sensor is required in case the main sensor fails, the installation and maintenance difficulties are further increased. A possibility to address this issue is the indirect estimation of the desired variable by leveraging other correlated measures within the operational process. Data-driven techniques are well-suited for this aim, given their capacity to model potentially complex industrial processes. This paper proposes the implementation of a virtual flow sensor for its integration in the control loop of an industrial process. More specifically, four different data-driven methods have been tested to obtain the virtual sensor: multiple linear regression (MLR), multilayer perceptron (MLP), long-short term memory (LSTM) and deep long-short term memory (DeepLSTM). MAE, RMSE and $$R^2$$ R 2 have been chosen as evaluation metrics for model selection and testing. Furthermore, the robustness of the virtual flow sensor is not only evaluated under ideal operating conditions, but it is also tested under adverse conditions with various noise levels added to the measured signals. Additionally, the performance of the flow control loop using the real and virtual sensors is also evaluated in both ideal and adverse conditions. IAE, ITAE, and IAVU indices are used to assess the control performance. The results prove the robustness of the LSTM-based virtual flow sensor and the effectiveness of the control loop using it, avoiding the modification of the controller and interrupting the process when the real flow sensor fails.

Model-Free Closed-Loop Control of Flow Past a Bluff Body: Methods, Applications, and Emerging Trends

Flow past one or multiple bluff bodies is almost ubiquitous in nature and industrial applications, and its rich underlying physics has made it one of the most typical problems in fluid mechanics and related disciplines. The search for ways to control such problems has attracted extensive attention from both the scientific and engineering fields, as this could potentially bring about benefits such as reduced drag, mitigated noise, suppressed vibration, and enhanced heat transfer. Flow control can be generally categorized into passive and active approaches, depending on whether there is an external energy input to the flow system. Active control is further divided into open-loop approaches and closed-loop approaches, depending on whether the controller depends on feedback signals extracted from the flow system. Unlike in many other applications of passive flow control and open-loop active flow control, theoretically advantageous closed-loop controls are quite rare in this area, due to the complicated features of flow systems. In this article, we review the recent progress in and future perspectives of flow past a single or multiple bluff bodies using model-free closed-loop control so as to outline the state-of-the-art research, determine the physical rationale, and point to some future research directions in this field.

Development of an observer of rotor angular velocity and resistance moment on the shaft of an adjustable permanent magnet synchronous motor powered through long cable

Relevance. Currently, when using submersible electric motors in centrifugal electric pump installations in cyclic operation mode, there is a reduction in the inter-repair period of submersible equipment, which is associated with a reduction in oil pumping periods to several minutes. As a result, there is a multiple increase in starting currents and torque, which leads to an increase in mechanical loads on the pump shaft and appearance of resonance phenomena during the electric motor acceleration, reducing the reliability of a seal protection. To solve these problems, it is necessary to synthesize closed-loop vector control systems with current and electromagnetic torque control in transient responses. Open-loop scalar control systems for electric drives of oil well production are used in operation, which is due to the complexity of obtaining feedback signals on the angular velocity of the rotor and the moment of resistance on the shaft by means of submersible telemetry. This problem determines the relevance and necessity of developing observers for rotor speed and load torque estimation, taking into account the features of the technological process of installing centrifugal electric pumps, in particular, the presence of a long cable for powering a submersible electric motor based on a synchronous machine with permanent magnets. Aim. To develop a full-order observer rotor speed and load torque for the dynamic system "long supply cable – synchronous motor with permanent magnets". Methods. System analysis and identification of dynamic systems, synthesis of Luenberger observers, mathematical modeling of dynamic systems, electric drives and electrical machines. Result and conclusions. The authors have proposed the mathematical model of a rotor speed and load torque full-order observer for the dynamic system "long supply cable – synchronous motor with permanent magnets". The performance of the observer was investigated under varying load on the shaft, mismatch of non-zero initial conditions, deviation of the parameters of the equivalent circuit of the observer and the object in the range from –20 to +20% of the nominal values.

Reflex regulation of model-based biomimetic control for a tendon-driven prosthetic hand

Remarkable compliance of human hands due to muscular biomechanics and neural reflexes, attributes lacking in conventional prosthetic hands. Our prior study replicated neuromuscular reflex in prosthetic control using neuromorphic modeling. Nonetheless, the specific impact of the spinal reflex feedback in biomimetic control on amputees’ manipulation capabilities remains unclear. This study aims to investigate the plausible role of reflex regulation in biomimetic control for tendon-driven prosthetic hands. Eleven subjects with forearm amputations participated in force control tasks and functional tasks. The force control tasks were performed using either the prosthetic forefinger or the entire hand. The functional tasks of varying difficulties included the standard rigid object test (SROT), refined rigid object test (RROT), and fragile object test (FOT). Additionally, the stiffness properties of biomimetic control were tested prior to the human-in-loop evaluations. Closed-loop control (CLC) with integrated proprioceptive feedback and open-loop control (OLC) were employed for assessments. Results showed that the reflex regulation contributed significantly to the increase by 20.4% in success rate, 17.9% in effective throughput, and 65.7% decrease in break rate during in the pressing force control task. CLC consistently outperformed OLC across all indices. Indeed, the gripping force control task revealed comparable trends as well. In functional tasks, the reflex regulation yielded 10.5%, 17.9%, and 146.4% improvements in performance for SROT, RROT and FOT, respectively. The findings highlight the essential role of reflex regulation in biomimetic prosthesis control, providing evidence for enhancing amputees’ fine manipulation abilities by replicating the human sensorimotor functions.

Co-contraction embodies uncertainty: An optimal feedforward strategy for robust motor control.

Despite our environment often being uncertain, we generally manage to generate stable motor behaviors. While reactive control plays a major role in this achievement, proactive control is critical to cope with the substantial noise and delays that affect neuromusculoskeletal systems. In particular, muscle co-contraction is exploited to robustify feedforward motor commands against internal sensorimotor noise as was revealed by stochastic optimal open-loop control modeling. Here, we extend this framework to neuromusculoskeletal systems subjected to random disturbances originating from the environment. The analytical derivation and numerical simulations predict a characteristic relationship between the degree of uncertainty in the task at hand and the optimal level of anticipatory co-contraction. This prediction is confirmed through a single-joint pointing task experiment where an external torque is applied to the wrist near the end of the reaching movement with varying probabilities across blocks of trials. We conclude that uncertainty calls for impedance control via proactive muscle co-contraction to stabilize behaviors when reactive control is insufficient for task success.

Pontryagin Neural Networks for the Class of Optimal Control Problems With Integral Quadratic Cost

This work introduces Pontryagin Neural Networks (PoNNs), a specialised subset of Physics-Informed Neural Networks (PINNs) that aim to learn optimal control actions for optimal control problems (OCPs) characterised by integral quadratic cost functions. PoNNs employ the Pontryagin Minimum Principle (PMP) to establish necessary conditions for optimality, resulting in a two-point boundary value problem (TPBVP) that involves both state and costate variables within a system of ordinary differential equations (ODEs). By modelling the unknown solutions of the TPBVP with neural networks, PoNNs effectively learn the optimal control strategies. We also derive upper bounds on the generalisation error of PoNNs in solving these OCPs, taking into account the selection and quantity of training points along with the training error. To validate our theoretical analysis, we perform numerical experiments on benchmark linear and nonlinear OCPs. The results indicate that PoNNs can successfully learn open-loop control actions for the considered class of OCPs, outperforming the commercial software GPOPS-II in terms of both accuracy and computational efficiency. The reduced computational time suggests that PoNNs hold promise for real-time applications.

Directional control law functional analysis and design for civil aircraft simulator

Abstract Functional analyses of directional flight control law for a typical civil aircraft are conducted, including directional maneuver, rudder limiter, coordinated turn, Yaw Damper, Thrust Asymmetrical Compensation, Gust Alleviation, manual rudder trim, etc. Through inputs of doublet, step input, and sweeping frequency, open loop control characteristics are analyzed based on a linearized state-space matrix. Simulink block architecture of a closed-loop directional control law is constructed, aiming to fulfill flight control functions, including pedal-to-rudder control, Yaw Damper, coordinated turn, and gain-tuned rudder limiter. Simulations for the previously designed closed-loop directional control law are performed to verify the characteristics of directional handling qualities in the aspect of the Yaw Damper, directional static and dynamic stability, maneuverability, and sweeping frequency.

Inverse feedforward control of piezoelectric actuators using optimized composite neural network-based Hammerstein model

This paper utilizes the optimized composite neural network (OCNN)-Hammerstein model to directly identify the dynamic inverse hysteresis effect, employing it as a feedforward controller to compensate for the rate-dependent and amplitude-dependent dynamic hysteresis of the piezoelectric actuator. The OCNN-Hammerstein model consists of an optimized composite neural network and an auto-regressive exogenous model in series. The OCNN comprises convolutional neural network layer and radial basis function neural network layer, with its hyperparameters optimized using a modified grey wolf optimizer algorithm. Compared to the existing dynamic Prandtl-Ishlinskii-Hammerstein and dynamic Bouc-Wen-Hammerstein models in the literature, the feedforward controller based on the proposed model in this paper demonstrates superior performance, with an RSME below 0.45 μm and a 74.5% reduction in relative hysteresis at the condition of 300 Hz and maximum amplitude. The feedforward controller exhibits universality across all amplitude ranges and within the 50–300 Hz frequency range, while also providing predictive compensation for dynamic hysteresis at 300–400 Hz. The feedforward controller based on the OCNN-Hammerstein model in this paper provides a crucial methodology for open-loop control of piezoelectric actuators.

A comparative analysis of intelligent energy modelling approaches for a grid-tied PVT system application

Renewable energy resources are a long-term answer to the current chronic energy dilemma. Solar photovoltaic (PV) systems are the most often used form of a practical solution for power supply and energy management in buildings. A PV-thermal (PVT) system is essential to lowering the demand for grid energy by capturing electrical and thermal energy from sunlight, optimising energy usage and encouraging sustainable energy practices. The most significant difficulty faced by developing countries, such as South Africa, is a lack of power supply to meet demand while limiting grid overload. An alternative source of energy is a critical component. This research compares two intelligent energy modelling methodologies: optimal control scheme-based open loop and model predictive control (MPC) in a PVT application. The primary goal of the modelling is to limit the electricity required from the grid while stressing the system’s energy efficiency and cost-effectiveness. The open loop approach uses mathematical optimisation techniques to establish the best control strategy for the PVT system. The ideal set-points for various system parameters are found by presenting the problem as an optimisation task to reduce grid power usage. The optimal control technique delivers a global optimisation solution based on the system dynamics and constraints. The MPC technique employs a predictive control technique of the PVT system to create optimal control actions in a receding horizon manner. The MPC algorithm anticipates future system behaviour and generates control actions that minimise grid power drawn by iteratively solving an optimisation problem at each time step. This robust online control scheme enables real-time modifications and responses to system disruptions that effectively and dynamically coordinate system behaviour.

Research on parameter optimization method of thrust vector/aerodynamic rudder composite control law based on singular value method

Advanced aircraft with thrust vectoring/aerodynamic rudder layout have significant control coupling problems. The traditional single-loop control parameter design method cannot take into account the performance of multiple control loops in this scenario. Therefore, a control law parameter optimization method based on the singular value of the hysteresis matrix is proposed. First, the control parameter optimization interval is determined using the time domain control performance index. Then, the singular value method is used to measure the stability margin of the multiple-input multiple-output (MIMO) system on this basis, and the corresponding optimal objective function is established to optimize the controller parameters. The feasibility of this method is verified by numerical simulation. The results show that the designed control parameter optimization algorithm has better time domain control performance and larger system stability margin than the traditional single-loop control parameter design method.

A Method for Evaluating Demand Response Potential of Industrial Loads Based on Fuzzy Control

Demand response (DR) can ensure electricity supply security by shifting or shedding loads, which plays an important role in a power system with a high proportion of renewable energy sources. Industrial loads are vital participants in DR, but it is difficult to assess DR potential because of many complex factors. In this paper, a new method based on fuzzy control is given to assess the DR potential of industrial loads. A complete assessment framework including four steps is presented. Firstly, the industrial load data are preprocessed to mitigate the influence of noisy and transmission losses, and then the K-means algorithm considering the optimal cluster number is used to calculate baseline load of industrial load. Subsequently, an open-loop fuzzy controller is designed to predict the response factor of different industrial loads. Three strongly correlated indicators, namely peak load rate, electricity intensity, and load flexibility, are selected as the input of fuzzy control, which represents response willingness. Finally, the baseline load of diverse clustering scenarios and the response factor are used to calculate the DR potential of different industrial loads. The proposed method takes into account both economic and technical factors comprehensively, and thus, the results better represent the available DR potential in real-world situations. To demonstrate the effectiveness of the proposed method, the case of a medium-sized city in China is studied. The simulation focuses on the top eight industrial types, and the results show they can contribute about 189 MW available DR potential.