Accurate Control Research Articles

Tailoring Solution Accuracy for Fast Whole-Body Model Predictive Control of Legged Robots

Tailoring Solution Accuracy for Fast Whole-Body Model Predictive Control of Legged Robots

Research on deformation prediction of CFETR multi-purpose overloaded robot based on real-time structural simulator

The CFETR Multi-Purpose Overload Robot (CMOR) is a key subsystem of the China Fusion Engineering Test Reactor (CFETR), which can perform maintenance tasks of the internal components. However, the large slenderness ratio of the structure results in low control accuracy of the CMOR end-effector. This paper proposes a CMOR deformation prediction method based on the real-time structural simulator. The overall deformation model of CMOR is analyzed based on the single joint deformation mechanism. Based on the principle of layered control, the control framework of the CMOR structural simulator is constructed, and the deformation data of CMOR in different positions are calculated based on the finite element method. A hybrid neural network containing a multilayer perceptron, transformer, and attention mechanism is designed to train the CMOR deformation prediction model. The training results show that the deformation prediction model converges quickly and fits the deformation of the CMOR structure well with small prediction errors. Finally, the real-time structure simulator is developed based on the deformation prediction model, and the deformation of CMOR is reconstructed with the update frequency of 2 Hz and the absolute error at any point within ±10 mm, which verifies the correctness of the CMOR structural deformation prediction method.

Image motion compensation control method for the dynamic scan and stare imaging system

The main function of the dynamic scan and stare imaging system is to quickly and continuously search a large area and perform high-resolution imaging. To eliminate image motion during the scanning process, this paper proposes an image motion compensation control method with dual-channel control. First, an improved model-assisted active disturbance rejection control is proposed, in which an auxiliary model is integrated into the algorithm to improve the control accuracy and response speed of the rotation rate of the platform. Second, a fast steering mirror (FSM) is introduced into the control system to compensate for the scanning speed and multiple disturbances. By adopting the amplitude frequency characteristics of the tracking differentiator, a parallel tracking differentiator filter is designed to suppress the interference of gyroscope noise on the FSM. When the system is disturbed by multiple frequency disturbances, the residual error of the image motion compensation is less than the spatial angular resolution of one pixel through the dual-channel stable control of the platform and the FSM. In the scan imaging experiment results, the average value of the grayscale variance function for the compensated images is close to 90% that of the static reference images.

Non-singular fast terminal sliding mode trajectory tracking control of cantilever piezo-electric stack actuator based on asymmetric hysteresis compensation

Abstract Piezoelectric actuators are widely employed in micro-precision applications due to their fast response and high resolution. This paper investigates the trajectory tracking control of a cantilever piezoelectric stack actuator (CPSA) under external perturbations and hysteresis. A control scheme is proposed that effectively compensates for hysteresis by employing an asymmetric Bouc-Wen (ABW) model, integrated with a non-singular fast terminal sliding mode control (NFTSMC) featuring a variable convergence law. This methodology guarantees finite-time convergence and robustness, thereby enhancing the overall control performance of the CPSA. Experimental results reveal that the proposed control algorithm significantly improves control accuracy and speed, achieving stable closed-loop system performance and maintaining bounded closed-loop signals within a finite time frame. The effectiveness and superiority of the NFTSMC method are validated through comprehensive experimental studies.

Utilizing a Transparent Model of a Semi-Direct Acting Water Solenoid Valve to Visualize Diaphragm Displacement and Apply Resulting Data for CFD Analysis

This article introduces a comprehensive methodology that combines physical prototyping and computational modeling to analyze the hydrodynamics and design of a semi-direct acting solenoid valve for water applications. A transparent, injection-molded valve model was used to experimentally measure diaphragm displacement, which exhibited linear behavior at flow rates up to 10.1 L/min. Beyond this threshold, the diaphragm reached maximum displacement, constraining flow control accuracy. These experimental results informed the creation of a computational domain for detailed CFD analysis, demonstrating strong validation against experimental pressure drop data. The CFD simulations identified critical inefficiencies, such as uneven pressure distribution on the diaphragm due to inlet flow, flow imbalances, and vortex formation within the chamber and outlet channel. These issues were traced to specific design limitations. To address these design flaws, this study suggests optimizing the inlet geometry, implementing a symmetric chamber design, and modifying the outlet channel with smoother transitions to enhance flow control and improve operational efficiency.

Tracking control strategy of tendon driven robotic arm under adaptive neural network

IntroductionWith the rapid optimization and evolution of various neural networks, the control problem of robotic arms in the area of automation control has gradually received more attention.MethodsTo improve the control performance of robotic arms under complex dynamic models, this study proposes an adaptive affective radial basis function network control strategy. Firstly, the kinematic and dynamic mathematical models of the tendon driven robotic arm are constructed. Then, by integrating the affective computing model and the radial basis function network, an adaptive affective radial basis function network control algorithm is constructed.Results and DiscussionThe research results indicate that the designed algorithm significantly outperforms the other two compared algorithms in terms of control accuracy and stability. In benchmark performance testing, the designed algorithm has a error accuracy of up to 0.97 and a steady state of up to 0.95. In the simulation results, the maximum torque change of the designed algorithm is only 3.8 Nm, which is much lower than other algorithms. In addition, the control error fluctuation range of this algorithm is between −0.001 and 0.001, almost close to zero error. This study provides a new optimization strategy for precise control of tendon driven robotic arms, and also opens up new avenues for the application of artificial intelligence technology in complex nonlinear system control.

Three-Dimensional Active Magnetic Levitation Actuating and Control System for Curved Pipes

A three-dimensional active maglev (magnetic levitation) actuating system based on force imbalance is proposed. By combining the principle of force imbalance control with the control algorithm, the stable levitation and controllable levitating motion of the magnetic ball can be realized. The four electromagnetic actuating structures are used to stabilize the force of the controlled object, and the dual-hall sensor group and hardware differential method are used to improve control stability and accuracy. By combining the fine adjustment of the active maglev actuating system with the coarse adjustment of the mechanical arm, the three-dimensional levitation motion of the magnetic ball in curved pipes is realized. Experimental results show that the proposed control algorithm solves problems such as the increase of deviation between the controlled object and the steady-state operating point and the rapid deterioration of tracking performance in the model-based control method. In the vertical direction, the overshoot is within 0.418%, regardless of axis motion or non-axis motion. In the horizontal direction, the offset limits left and right of the axis are 4.590 mm and 3.536 mm, respectively. The fluctuation of vertical and horizontal motion is within the allowable range of ±0.2 mm. This can be applied to the non-destructive quality detection of the inner walls and the internal dredging of long and thin pipes in examinations and industrial fields.

The pressure control of independent load port electro-hydraulic load simulation system using fixed time integral sliding mode active disturbance rejection

To address the issues of motion disturbance and low loading accuracy in electro-hydraulic load simulation systems, and to effectively mitigate the impact of uncertainty on system performance, this paper proposes a novel independent load port electro-hydraulic load simulation system. A nonlinear variable structure state equation is derived to describe the pressure working states of the left and right chambers of the servo valve and hydraulic cylinder under loading conditions. In light of the presence of substantial motion disturbances, an integrated sliding mode automatic disturbance rejection composite controller is proposed with a focus on fixed-time convergence. To mitigate system chattering and enhance system loading performance and disturbance immunity, a fixed-time integral sliding mode controller was developed within the framework of active disturbance rejection control. Stability analysis was also carried out. Simulation research demonstrates that the pressure control accuracy of the composite controller is 99.88%, 99.86%, and 99.83% respectively when the system step command is given with disturbance frequencies of 1 Hz, 3 Hz, and 5 Hz. Similarly, under the same disturbance frequencies, the pressure control accuracy is 99.88%, 99.86%, and 99.84% respectively when the system sine command is given. The simulation results demonstrate that the independent load port electro-hydraulic load simulation system exhibits a notable energy-saving effect. Specifically, energy savings of around 45% and 48% were achieved compared to the original system. The research and exploration conducted have laid a strong groundwork for future in-depth studies on the independent load port electro-hydraulic load simulation system.

Convergence rate sliding mode control of permanent magnet synchronous motor based on expanded sliding mode observer

The aim is to address the limitations of conventional sliding mode control for PMSM, namely the problems of sliding mode oscillation, load disturbance, and poor control accuracy. An improved sliding mode control strategy for PMSM is proposed based on the novel convergence rate and extended state observer. Firstly, an integral sliding mode velocity loop controller is designed based on the novel convergence rate to solve the sliding mode vibration problem. The stability of the proposed control strategy is derived by the Lyapunov criterion. Secondly, an extended state observer capable of estimating the disturbance torque is designed for the purpose of disturbance compensation. The disturbance torque estimate is then used as a feed-forward signal for the sliding mode velocity loop controller. A fast second-order sliding mode controller is used for the current loop to avoid the potential introduction of noise and disturbances. The simulation results show that compared to alternative control strategies, the proposed control strategy enables the PMSM to respond effectively to start-up and load disturbances, significantly reduces the sliding mode jitter, and improves both the torque estimation accuracy and the system control accuracy.

Pressure Control of Multi-Mode Variable Structure Electro-Hydraulic Load Simulation System.

During the loading process, significant external position disturbances occur in the electro-hydraulic load simulation system. To address these position disturbances and effectively mitigate the impact of uncertainty on system performance, this paper first treats model parameter uncertainty and external disturbances as lumped disturbances. The various states of the servo valve and the pressures within the hydraulic cylinder chambers are then examined. Building on this foundation, the paper proposes a nonlinear multi-mode variable structure independent load port electro-hydraulic load simulation system that is tailored for specific loading conditions. Secondly, in light of the significant motion disturbances present, this paper proposes an integral sliding mode active disturbance rejection composite control strategy that is based on fixed-time convergence. Based on the structure of the active disturbance rejection control framework, the fixed-time integral sliding mode and active disturbance rejection control algorithms are integrated. An extended state observer is designed to accurately estimate the lumped disturbance, effectively compensating for it to achieve precise loading of the independent load port electro-hydraulic load simulation system. The stability of the designed controller is also demonstrated. The results of the simulation research indicate that when the command input is a step signal, the pressure control accuracy under the composite control strategy is 99.94%, 99.86%, and 99.76% for disturbance frequencies of 1 Hz, 3 Hz, and 5 Hz, respectively. Conversely, when the command input is a sinusoidal signal, the pressure control accuracy remains high, measuring 99.94%, 99.8%, and 99.6% under the same disturbance frequencies. Furthermore, the simulation demonstrates that the influence of sensor random noise on the system remains within acceptable limits, highlighting the effective filtering capabilities of the extended state observer. This research establishes a solid foundation for the collaborative control of load ports and the engineering application of the independent load port electro-hydraulic load simulation system.

Adaptive Fast Smooth Second-Order Sliding Mode Fault-Tolerant Control for Hypersonic Vehicles

In response to control issues in hypersonic vehicles under external disturbances, model uncertainties, and actuator failures, this paper proposes an adaptive fast smooth second-order sliding mode fault-tolerant control scheme. First, a system separation approach is adopted, dividing the hypersonic vehicle model into fast and slow loops for independent design. This ensures that the airflow angle tracking error and sliding mode variables converge to the vicinity of the origin within a finite time. A fixed-time disturbance observer is then designed to estimate and compensate for the effects of model uncertainties, external disturbances, and actuator failures. The controller parameters are dynamically adjusted through an adaptive term to enhance robustness. Furthermore, first-order differentiation is used to estimate differential terms, effectively avoiding the issue of complexity explosion. Finally, the convergence of the controller within a finite time is rigorously proven using the Lyapunov method, and the perturbation of aerodynamic parameters is tested using the Monte Carlo method. Simulation results under various scenarios show that compared with the terminal sliding mode method, the proposed method outperforms control accuracy and convergence speed. The root mean square errors for the angle of attack, sideslip angle, and roll angle are reduced by 65.11%, 86.71%, and 45.51%, respectively, while the standard deviation is reduced by 81.78%, 86.80%, and 45.51%, demonstrating that the proposed controller has faster convergence, higher control accuracy, and smoother output than the terminal sliding mode controller.

Integration of Sense and Control for Uncertain Systems Based on Delayed Feedback Active Inference.

Asa result of the time lag in transmission, the data obtained by the sensor is delayed and does not reflect the state at the current moment. The effects of input delay are often overlooked in active inference (AIF), which may lead to significant deviations in state estimation and increased prediction errors, particularly when the system is subjected to a sudden external stimulus. In this paper, a theoretical framework of delayed feedback active inference (DAIF) is proposed to enhance the applicability of AIF to real systems. The probability model of DAIF is defined by incorporating a control distribution into that of AIF. The free energy of DAIF is defined as the sum of the quadratic state, sense, and control prediction error. A predicted state derived from previous states is defined and introduced as the expectation of the prior distribution of the real-time state. A proportional-integral (PI)-like control based on the predicted state is taken to be the expectation of DAIF preference control, whose gain coefficient is inversely proportional to the measurement accuracy variance. To adaptively compensate for external disturbances, a second-order inverse variance accuracy replaces the fixed sensory accuracy of preference control. The simulation results of the trajectory tracking control of a quadrotor unmanned aerial vehicle (UAV) show that DAIF performs better than AIF in state estimation and disturbance resistance.

Prediction and Elimination of Physiological Tremor During Control of Teleoperated Robot Based on Deep Learning.

Currently, teleoperated robots, with the operator's input, can fully perceive unknown factors in a complex environment and have strong environmental interaction and perception abilities. However, physiological tremors in the human hand can seriously affect the accuracy of processes that require high-precision control. Therefore, this paper proposes an EEMD-IWOA-LSTM model, which can decompose the physiological tremor of the hand into several intrinsic modal components (IMF) by using the EEMD decomposition strategy and convert the complex nonlinear and non-stationary physiological tremor curve of the human hand into multiple simple sequences. An LSTM neural network is used to build a prediction model for each (IMF) component, and an IWOA is proposed to optimize the model, thereby improving the prediction accuracy of the physiological tremor and eliminating it. At the same time, the prediction results of this model are compared with those of different models, and the results of EEMD-IWOA-LSTM presented in this study show obvious superior performance. In the two examples, the MSE of the prediction model proposed are 0.1148 and 0.00623, respectively. The defibrillation model proposed in this study can effectively eliminate the physiological tremor of the human hand during teleoperation and improve the control accuracy of the robot during teleoperation.

Neural network–based transfer learning to improve stiffness modeling of industrial robots with small experimental data sets

AbstractStiffness modeling is an essential subject for the composition of robot control. Accurate stiffness modeling is helpful for improving the control accuracy of industrial robots, particularly under dynamic load circumstances. The classic virtual joint modeling (VJM) method is challenging in predicting the deformation of the end-effector throughout the full workspace due to the nonlinear deformation of the robot joint and its serial articulated structure. This paper proposes a full-space stiffness modeling method for robots based on the integration of a multi-layer perceptual (MLP) model and VJM. To provide enough training data for the MLP model, VJM is used to build a stiffness model with a small set of experimental data to generate 106,400 training data. A model-based transfer learning approach is proposed to improve the model’s accuracy and generalization regarding the difference between generated training data and actual experimental data. The VJM stiffness model is compared with the MLP stiffness model and the existing CNN-based transfer learning model based on the same experimental data. Considering the deformation prediction in the three directions in Cartesian space, the mean absolute error, standard deviation, and maximum error of the MLP model are decreased by at least 24.90%, 14.20%, and 8.50%, respectively, than the VJM. These prediction results demonstrate that the proposed modeling technique can significantly increase the accuracy of robot stiffness modeling, which is essential for position compensation in precise motion control of robots under dynamic load.

Gas–Water–Sand Inflow Patterns and Completion Optimization in Hydrate Wells with Different Sand Control Completions

Sand production poses a significant problem for marine natural gas hydrate efficient production. However, the bottom hole gas–water–sand inflow pattern remains unclear, hindering the design of standalone screen and gravel packing sand control completions. Therefore, gas–water–sand inflow patterns were studied in horizontal and vertical wells with the two completions. The experimental results showed that gas–water stratification occurred in horizontal and vertical standalone screen wells. The gas–water interface changed dynamically, leading to an uneven screen plugging, with severe plugging at the bottom and high permeability at the top. The high sand production rate and low well deviation angle exacerbated screen plugging, resulting in a faster rising rate of the gas–water interface. The screen plugging degree initially decreased and then increased as the gas–water ratio increased, resulting in the corresponding variation in the gas–water interface rising rate. Conversely, gas–water stratification did not occur in the gravel packing well because of the pore throat formed between the packing gravels. However, the impact of gas and water led to gravel rearrangement and the formation of erosion holes, causing sand control failure. A higher gas–water ratio and lower packing degree could result in more severe destabilization. Therefore, for the standalone screen completion, sand control accuracy should be designed at different levels according to the uneven plugging degree of the screen. For the gravel packing completion, increase the gravel density without destabilizing the hydrate reservoir, and use the coated gravel with a cementing effect to improve the gravel layer stability. In addition, the screen sand control accuracy inside the gravel packing layer should be designed according to the sand size to keep long-term stable hydrate production.

The sliding mode controller with composite reaching law for upper limb rehabilitation robot

Purpose Assisted training using upper limb rehabilitation robots is beneficial for flaccid paralysis patients in recovering their functional abilities. In the assisted training mode, the patient’s motor ability is limited by factors such as limb muscle tension, and it is prone for the rehabilitation robot to deviate from the prescribed training trajectory. A sliding mode control method based on a fixed time observer is proposed to address the problem of delayed trajectory tracking response of upper limb rehabilitation robots caused by external disturbances such as patient limbs. Design/methodology/approach First, aiming at the problem of estimating and compensating for external disturbances in the upper limb rehabilitation robot system, a fixed time observer was designed based on the robot’s dynamic model. Second, the composite sliding mode reaching law combining the smooth function and the power-exponential function is proposed to shorten the convergence time of system states in the startup phase, thereby reducing chattering in the control process and realizing the real-time tracking of the training trajectory by the control system. Findings The proposed method provides a solution for the trajectory tracking speed of upper limb rehabilitation robot controllers. In the circular trajectory tracking control, compared to the sliding-mode control method combined with the variable-exponential composite reaching law based on the fixed-time observer, the method in this paper reduces the time for the system state to reach the sliding surface by 0.89 s and improves the response speed by 0.66%. Originality/value The composite sliding mode approach law based on smooth function and power exponent function can reduce the time it takes for the system state to reach and remain on the sliding surface and improve the trajectory tracking speed of upper limb rehabilitation robots. This controller improves the accuracy of trajectory control and ensures the robustness of auxiliary rehabilitation training.

Advanced hybrid control for induction motor combining ANN-PID speed controller with neural predictive current regulator based on particle swarm optimization

This paper presents a hybrid control strategy for an induction motor (IM) organized into two nested loops. In the outer loop, an adaptive artificial neural network-based proportional-integral-derivative (ANN-PID) controller is used for speed control. The ANN-PID is divided into two stages: the first stage includes an adaptive ANN speed estimator, and the second stage includes an adaptation algorithm that fine-tunes both the weights and biases of the ANN estimator and the parameters of the PID controller. In the internal loop, a predictive current controller is used to control IM currents using neural networks. Specifically, the neural predictive controller (NPC) approaches the control problem by presenting it as an optimization problem using a neural predictor for IM currents. The considered objective function includes two elements: the current tracking error and the electromagnetic torque ripple. This function is computed over a given time horizon informed by predictions of IM currents. The particle swarm optimization (PSO) algorithm is applied to determine the optimal solution for this optimization task. The effectiveness of the hybrid control scheme is demonstrated by simulation results obtained using Matlab/Simulink, highlighting its superiority over conventional control methods. The proposed hybrid control is characterized by a reduction in torque ripple and overshoot, while also showing robustness to variations in load, rotor resistance, and rotor inductance. Comparisons with an ANN controller and a fuzzy PI controller further demonstrate the superior performance of the hybrid controller in handling nonlinear dynamics and maintaining control accuracy under various operating scenarios.

Predefined-Time Hybrid Tracking Control for Dynamic Positioning Vessels Based on Fully Actuated Approach

This study investigates the problem of tracking the trajectory of a dynamic positioning (DP) ship under sudden surges of elevated sea states. First, the tracking problem is reformulated as an error calibration problem through the introduction of fully actuated system (FAS) approaches, thereby simplifying controller design. Second, a predefined-time control term is designed to maintain the convergence time of the trajectory tracking error within a specified range; however, the upper bound of the perturbation must be estimated in advance. The high sea state during operation can result in an abrupt change in the upper bound of disturbance, thereby affecting the control accuracy and stability of the system. Therefore, a linear control matrix is developed to eliminate the system’s dependence on the estimation of the upper bound of disturbance following smooth switching, thereby achieving control decoupling and providing a conservative switching time. Additionally, a nonlinear reduced-order expansion observer (RESO) is constructed for feedforward compensation. The stability of the system is demonstrated using the Lyapunov function, indicating that the selection of appropriate poles can theoretically enhance the system’s convergence with greater control accuracy and robustness after switching. Finally, the effectiveness of the proposed method is validated through simulations and comparative experiments.

Application of the integrated well-surface facility production system for selecting the optimal operating mode of equipment

This work is dedicated to the analysis and development of an integrated production system that combines wells and surface facilities to select the optimal operating mode for equipment. The main focus is on studying data analysis methods, as well as collecting and processing information, which ensures high accuracy in process control and optimal use of technological capabilities. The integration of data from various levels of the production chain, from the well to the surface facilities, opens up new opportunities for optimizing equipment performance and improving resource management quality. Integration of wells and surface facilities – The effective integration of wells and surface facilities is crucial for optimizing production processes, minimizing operational costs, and reducing environmental impact. Integrated management systems allow the automation of many processes, providing continuous monitoring of operating parameters, automatic adjustment of settings, and supplying data for quick decision-making. This includes real-time data collection, analysis, and the use of the obtained information to select optimal operating modes, which helps improve both overall efficiency and safety in production processes. In the modern oil and gas industry, the efficient use of equipment at all stages of hydrocarbon production is of particular importance. Optimizing the operation of wells and surface facilities is a key aspect for increasing economic efficiency and reducing environmental impact. In this regard, the development and application of integrated systems that enable real-time control and adaptation of equipment operating modes has become a relevant and significant task.

Simple three-stage control scheme achieving interrupt-free integrated polarization stabilization

It is a long-outstanding challenge to achieve low-complexity interrupt-free integrated polarization stabilization of infinitely changing light. Integrated phase shifters have finite tuning range and experience abrupt switching or reset when reaching the limits of their tuning ranges during the polarization stabilization process. We propose a three-stage interrupt-free integrated polarization stabilization scheme with nested electronic-photonic loops (NEPL) and dimensionality reduction (DR) method. Based on the NEPL structure, only three Mach-Zehnder interferometer (MZI) stages are needed to achieve interrupt-free polarization stabilization scheme. The input state is first constrained into a predetermined range using a nested loops with the first MZI stage and then processed by regular two-stage scheme. Throughout the entire operation, all phase shifters are tuned continuously without any pause, abrupt switching or reset. Furthermore, a DR method is introduced to reduce the control complexity and enhance control speed and accuracy of the regular two-stage scheme. A detailed analysis of the proposed design is provided, and its effectiveness is verified using electronic-photonic co-simulation in the Cadence platform. This scheme consists of only phase shifters, couplers and photodiodes (PDs) with simple electronic controllers, and can be implemented in various material systems and process platforms. To the best of our knowledge, this is the first three-stage interrupt-free polarization stabilization scheme reported in the literature.