Error Control Research Articles

Nonlinear control designs and their application to cancer differentiation therapy

We designed three new controllers: a sigmoid-based controller, a polynomial dynamic inversion-based controller, and a proportional–integral–derivative (PID) impulsive controller for cancer differentiation therapy. We compared these three controllers to existing control strategies to show the improvement in performance and compare their robustness. The sigmoid-based controller adds a sigmoid term associated with the error of the controlled state and a selected observed state. The sigmoid term is multiplied by a control gain, thereby decreasing the control effort for state transition. The polynomial dynamic inversion-based controller adds a cubic error term in the error dynamic aiming to achieve a shorter convergence time to the desired value of the controlled state. The PID impulsive controller considers the accumulated controlled state error and the rate of change of the controlled state error, thereby forcing the controlled state to converge to the desired value and alleviating the damping effect in the steady state. For the considered cancer network, the 3 new cancer control strategies exhibit superior and robust performance. The PID impulsive controller has a significant improvement in robustness compared to the impulsive controller and has greater potential for cancer differentiation therapy.

Insight into Functional Boiti–Leon–Mana–Pempinelli Equation and Error Control: Approximate Similarity Solutions

The Boiti–Leon–Mana–Pempinelli Equation (BLMPE) is an essential mathematical model describing wave propagation in incompressible fluid dynamics. In the present manuscript, a novel generalization of the BLMPE is introduced, called herein the functional BLMPE (F-BLMPE), which involves different functions, including exponential, logarithmic and monomaniacal functions. In these cases, the F-BLMPE reduces to an explicit form in the dependent variable. In addition to this, it is worth deriving approximate similarity solutions of the F-BLMPE with constant coefficients using the extended unified method (EUM). In this method, nonlinear partial differential equation (NLPDE) solutions are expressed in polynomial and rational forms through an auxiliary function (AF) with adequate auxiliary equations. Exact solutions are estimated using formal solutions substituted into the NLPDEs, and the coefficients of the AF of all powers are set equal to zero. This approach is valid when the NLPDE is integrable. However, this technique is not valid for non-integrable equations, and only approximate solutions can be found. The maximum error can be controlled by an adequate choice of the parameters in the residue terms (RTs). Multiple similarity solutions are derived, and the ME is depicted in various examples within this work. The results found here confirm that the EUM is an efficient method for solving NLPDEs of the F-BLMPE type.

A novel constant pressure control method for nucleation boiling of CO2/lubricant in molecular dynamics simulation

Conventional molecular dynamics simulations of nucleation boiling typically employ the fixed volume method, resulting in a non-constant pressure during the boiling process. In this paper, we propose an additional pressure control method, placing a cover plate at the top of the liquid phase to achieve liquid phase pressure control during boiling. The novel method yields a pressure control error within ±5 %. CO2 and CO2/PEC4 lubricating oil mixture are used to investigate the differences in heat transfer characteristics between non-constant and constant pressure nucleation boiling. Furthermore, the effect of PEC4 lubricating oil on CO2 nucleation boiling under a fixed pressure condition is explored. The results show that both the bubble nucleation and growth time of CO2 and the CO2/PEC4 mixture are prolonged under constant pressure. This causes a compression in CO2 molecular spacing, thereby reducing CO2 diffusion and mass transfer capabilities. Heat transfer from the solid to the liquid, and within the liquid itself, is diminished, while the distribution of PEC4 molecules within the liquid phase becomes more even. The migration rate of PEC4 molecules in the liquid phase slows down. Under the constant pressure model, PEC4 increases the mass transfer resistance of CO2. As the lubricant mass fraction rises, The nucleation and growth rate of CO2 bubbles decline. In comparison to CO2, PEC4 molecules exhibit a stronger interaction energy with the Cu wall surface. During boiling, some PEC4 molecules accumulate on the Cu surface, forming an oil film that impedes the direct contact between CO2 molecules and the Cu wall.

Three-Dimensional Path-Following Control Method for Flying–Walking Power Line Inspection Robot Based on Improved Line of Sight

The Flying–Walking Power Line Inspection Robot (FPLIR) faces challenges in maintaining stability and reliability when operating in harsh transmission line environments with complex conditions. The robot often switches modes frequently to land accurately on the line, resulting in increasing following errors and premature or delayed switching caused by reference path switching. To address these issues, a path-following control method based on improved line of sight (LOS) is proposed. The method features an adaptive acceptance circle strategy that adjusts the radius of the acceptance circle of the path point based on the angle of the path segment and the flight speed at the time of switching, improving path-following accuracy during reference trajectory switching. Also, an adaptive heading control with vertical distance feedback is designed to prioritize different path-following methods in real time based on variations in vertical distance, achieving rapid convergence along the following path. The state feedback following control law, based on the improved LOS, achieves the stable following of the reference path, which was validated by simulations. The simulation results show that the improved LOS reduces the convergence time by 0.8 s under controllable error conditions for path angles of θ ∈ (0, π⁄2). For path angles of θ ∈ (π⁄2, π), the following error is reduced by 0.3 m, and the convergence time is reduced by 0.4 s. These results validate the feasibility and effectiveness of the proposed method. This method demonstrates advantages over the traditional LOS in terms of following accuracy and convergence speed, providing theoretical references for future 3D path following for path-following robots and aerial vehicles.

Controllable Errors in the Management of Infectious Keratitis.

Controllable Errors in the Management of Infectious Keratitis.

Angle-only Autonomous Terminal Guidance and Navigation Algorithm for Asteroid Defense based on Meta-reinforcement Learning

This paper presented a robust angle-only guidance and navigation algorithm for asteroid defense missions based on meta-reinforcement learning. A recurrent neural network, trained via proximal policy optimization, is used to map the line-of-sight angles captured in real-time by the onboard camera to the optimal thrust. The neural network effectively replaces the roles of the navigation and guidance system while simultaneously removing the dependence on dynamic and observation models. The guidance and navigation model is tested on numerical simulations of a simulated mission directed to asteroid Bennu. The objective is to enable the spacecraft to hit the asteroid precisely, despite the presence of scattered initial conditions, uncertain model parameters, thruster control error, and attitude control and measurement error.

A priori and a posteriori error analysis for semilinear problems in liquid crystals

In this paper, we develop a unified framework for the a priori and a posteriori error control of different lowest-order finite element methods for approximating the regular solutions of systems of partial differential equations under a set of hypotheses. The systems involve cubic nonlinearities in lower order terms, non-homogeneous Dirichlet boundary conditions, and the results are established under minimal regularity assumptions on the exact solution. The key contributions include (i) results for existence and local uniqueness of the discrete solutions using Newton–Kantorovich theorem, (ii) a priori error estimates in the energy norm, and (iii) a posteriori error estimates that steer the adaptive refinement process. The results are applied to conforming, Nitsche, discontinuous Galerkin, and weakly over penalized symmetric interior penalty schemes for variational models of ferronematics and nematic liquid crystals. The theoretical estimates are corroborated by substantive numerical results.

Run Length Limited Error Control Codes Derived from Reed Solomon Codes

Reed Solomon codes were standardized for numerous wireless communication systems. Most practical Reed Solomon codes belong to non-binary linear block codes defined over finite fields with characteristic two. Each linear block code contains one codeword composed of all zeros. The concatenation of this and also other codewords can lead to long, theoretically even infinite, runs of equal symbols. Such long runs do not support synchronization in wireless receivers and therefore are unwanted. In this paper it is shown that extended and some appropriately shortened Reed Solomon codes constructed over finite fields with characteristic two can be transformed into Run Length Limited Reed Solomon codes. The presented method, if applicable, allows for doing it without inserting additional redundancy. Another advantage is that after the transformation, if some round conditions are fulfilled, the decoding does not have to be rebuilt.

Robust Design of a Fractional-Delay Repetitive Controller for Uncertain Feedback Control Systems and Its Validation on a Magnetic Bearing Test Rig

This article presents a robust design of a fractional-delay repetitive controller in the frequency domain. The control goal is to accurately suppress the control error caused by a disturbance whose period may be a non-integer multiple of the sampling period. Coefficients of the repetitive controller finite impulse response (FIR) filters are calculated by solving a convex optimization problem that includes constraints limiting both the sensitivity function and the control sensitivity function. The proposed new design algorithm is verified by experiments on a test rig to efficiently damp shaft vibrations of an active magnetic bearing (AMB).

Data-Driven Load Frequency Control Based on Multi-Agent Reinforcement Learning With Attention Mechanism

With the massive penetration of renewable energy, traditional reinforcement learning algorithms suffer from slow convergence and area control error (ACE) in interconnected power systems. This paper proposes data-driven load frequency control (LFC) based on multi-agent reinforcement learning with attention mechanism in interconnected power systems. It can be divided into two phases; in the centralized training, the agents are trained by an experience replay mechanism; in the decentralized execution, the trained agent automatically regulates the generation power to control the load frequency by real-time access to the grid data in the area. The agent can selectively focus on specific information in the environment by introducing a criticism network with an attention mechanism. The attention mechanism can reduce the training time for reinforcement learning while improving control performance under disturbance. A novel reward function based on a cooperation mechanism is used to score the performance of agent, which can guide the reinforcement learning algorithm to reduce the ACE of each area simultaneously. The proposed method is validated by the IEEE three-area interconnected power system, and it is concluded that the method can reduce the ACE caused by load and renewable power disturbances, and greatly reduce the training time of the algorithm.

A hierarchical optimization strategy in the intelligent ecological control of the greenhouse downy mildew

In a low-cost greenhouse, where there are few actuators to regulate the climate, increasing yield and preventing crop diseases are contradictory processes because pathogens and hosts stay in a similar ecological niche. Under given weather conditions, the environmental range for crop growth and disease control must be carefully balanced. The aim of this paper is to develop an accurate optimal control algorithm for disease ecological control and explore its contribution in the application of intelligent downy mildew prevention in a low-cost solar greenhouse. The proposed hierarchical optimization control strategy is compared with four common management methods in order to evaluate its impact on crop growth and disease prevention. The results show that the hierarchical optimization control strategy reaches the best balance between disease prevention, accumulated temperature promotion, and low temperature stress risk reduction without using an additional heating system. Moreover, an event-based PID controller is applied to the system considering the energy conservation aspect, which decreases the vents movements areas by 16%, while the control error is only increased by 1.9%. On the whole, this paper provides a solution for low-cost greenhouses about the joint use of greenhouse modelling, disease early warning and environment control, looking for promoting the green production in greenhouse environments by considerably reducing the disease occurrence and use of pesticides.

Improving the efficiency of diagnosing errors in computer devices for processing economic data functioning in the class of residuals

This paper proposes an improved method for diagnosing errors of computer devices, which process economic data in computer systems, by increasing efficiency. The object of research is the processes of control and diagnosis of data errors, in the class of residuals (CR). The improved method of diagnosing economic data represented in the non-positional system of calculation in CR is based on the application of orthogonal bases of partial sets of bases. The use of these bases makes it possible to organize the process of parallel processing of the projections of the output number of the non-positional code structure. This makes it possible to increase the efficiency of data diagnostics by n times, depending on the length of the bit grid of computer systems. For a single-byte computer system, the efficiency of data diagnosis increases by 1.2 times compared to the existing method based on the zeroing principle. At the same time, the effectiveness of operational control and diagnostics systems for data processing in CR with the growth of bit grids has been proven. Based on the results, it is shown that, unlike the correction codes used in the positional counting system, the arithmetic codes in CR have additional correction capabilities. An example of a specific implementation of the process of applying operational control and diagnosing errors in the processing of economic data presented in CR is given. The improved diagnostic method will make it possible (in comparison with existing diagnostic methods) to reduce the time, which increases the efficiency of the diagnostic procedure of data operating in CR. The results give grounds for asserting that, based on the improved method and the developed algorithm for the implementation of data diagnostics, it is possible to synthesize a device for reliable and operational control and diagnostics of economic data operating in CR

Influence of the commutator properties of Hamiltonians on the robustness of quantum circuits

We have proved new estimates for the coherent control errors of quantum circuits used in quantum computing. These estimates essentially take into account the commutator properties of the Hamiltonians and are based on the formulas of the commutator calculus.

Features of the pre-analytical stage in quantitative determination of TREC/KREC in peripheral blood

The use of dried blood spots (DBS) obtained from the heels of infants has many advantages over the collection of whole blood samples. DNA extracted from DBS can be used to detect genetic diseases by PCR, which has contributed to the development of population-based newborn screening worldwide. Since January 2023, the list of identified diseases includes a group of primary immunodeficiencies (PIDs), associated with the absence or decrease in the levels of T and/or B lymphocytes, determined as part of screening by the levels of TREC and KREC molecules in peripheral blood, respectively. Quantitative analysis requires special attention to biological material. The aim is to evaluate the impact of the preanalytical step on the quantitative analysis of TREC/KREC levels in peripheral blood.The material included 5219 DBS obtained from infants on days 3-4 of life, as well as DBS prepared from the whole blood of 100 apparently healthy individuals aged 18 to 29 years. A comparative analysis of the TREC/KREC molecules number in correctly and incorrectly collected DBS from newborns and adults, as well as depending on the volume of applied blood, was carried out by RT-PCR using test systems to assess the levels of TREC and KREC in peripheral blood. DBS quality was assessed visually.In the first months of the project, a significant number of incorrectly taken samples were identified – over a third of all DNA maps received for each corresponding month. As a result of additional training of medical staff, the amount of incorrectly collected material decreased to a level not exceeding 1% of all monthly samples collected. When using DNA extracted from DBS with application errors, the majority of samples (64% for newborns, 78% for adults) failed to obtain a result. In the remaining cases, the results obtained were significantly lower than the normal levels of TREC/KREC determined in the same samples with correct DBS collection (all p < 0.0001, 95% CI). The volume of blood used when correctly applied to Guthrie cards did not affect the results obtained, TREC and KREC levels were comparable; when comparing the medians calculated for each group of samples, no significant differences were identified (p > 0.05).When quantitatively analyzing TREC/KREC levels in peripheral blood, correctly taken material is fundamental importance to obtain reliable indicators, primarily to exclude false-positive results. To minimize errors in the preanalytical stage, additional training of medical personnel is necessary to control and/or correct errors.

Multimedia intelligent 3D images for automatic detection of sports injuries

Abstract This paper uses the types and causes of sports injuries as the entry point to fuse 2D dynamic MRI with a 3D static motion for image alignment in multimedia 3D image plane technology. Using a weight-sharing network and convolution operation, sports injury features are extracted and fused, and a fusion detection framework for sports injury image features is created. Data analysis was conducted using an example to verify the detection framework’s effectiveness. The results show that the peak signal-to-noise ratio of acquiring athletes’ sports injury region imaging by the algorithm in this paper is 43 dB, and the average detection time is 5.91 s. The error control for sports injury detection was reduced from 0.102 to 0.011 after 600 iterations of the algorithm in this paper.

Frequency regulation in interconnected power system with DFIG participation under ABT-based various operating strategies

The implementation of various operating strategies based on the Availability Based Tariff (ABT) in a two-area interconnected power system with thermal and DFIGs in each area for frequency regulation is investigated in this paper. A suitable Generation Control Error (GCE) signal is needed to regulate frequency and tie-line power flow in an ABT-based operating strategy. This signal combines marginal generation cost and frequency-dependent unscheduled generation cost. A GCE signal generated in succession by three distinct operating strategies controls all thermal generators. To determine the best ABT-based operating strategy for the system under consideration, a simulation study is performed to compare and analyse the corresponding system responses. The operating strategy that utilises GCE, which is the difference between unscheduled interchange charges and marginal cost from their reference values, performed better than the other frequency regulation operating strategies. Furthermore, the simulation study compares traditional and identified ABT-based better control strategies.

Time evolution for the Pauli–Fierz operator (Markov approximation and Rabi cycle)

This article is concerned with a system of particles interacting with the quantized electromagnetic field (photons) in the non relativistic Quantum Electrodynamics (QED) framework and governed by the Pauli–Fierz Hamiltonian. We are interested not only in deriving approximations of several quantities when the coupling constant is small but also in obtaining different controls of the error terms. First, we investigate the time dynamics approximation in two situations, the Markovian (Theorem 1.4 completed by Theorem 1.14) and non Markovian (Theorem 1.6) cases. These two contexts differ in particular regarding the approximation leading terms, the error control and the initial states. Second, we examine two applications. The first application is the study of marginal transition probabilities related to those analyzed by Bethe and Salpeter in (Bethe and Salpeter, 1957), such as proving the exponential decay in the Markovian case assuming the Fermi Golden Rule (FGR) hypothesis (Theorem 1.15 or Theorem 1.13) and obtaining a FGR type approximation in the non Markovian case (Theorem 1.5). The second application, in the non Markovian case, includes the derivation of Rabi cycles from QED (Theorem 1.7). All the results are established under the following assumptions at some steps of the proofs: an ultraviolet and an infrared regularization are imposed, the quadratic terms of the Pauli–Fierz Hamiltonian are dropped, and the dipole approximation is assumed but only to obtain optimal error controls.

Control Strategies for Piston Trajectory in Ionic Compressors for Hydrogen Storage

The ionic compressor is a new and prospective technology applied for hydrogen storage which adopts a hydraulic system in which the hydraulic drive unit is a solid piston in the compression cavity. Controlling the trajectory of the solid piston is critical for achieving the designed thermodynamic process of compression. However, a strategy for controlling the position of a piston in an ionic compressor has not been reported in the open literature. In this paper, three valve-controlled methodologies are proposed for the effective control of a piston’s trajectory in an ionic compressor. A transient numerical model of the entire compression system was built using AMESim 2021 software. The performances of the proposed control methods were simulated and compared. The results show that the maximum isothermal efficiency, 50.28%, was obtained in the system using Position-P control, for which the highest hydrogen discharge mass for a single compression cycle of 1.14 g, a relatively low specific energy consumption of 2395.17 J/g, and a relatively small velocity control error of 0.32 m/s were observed. Although the lowest specific energy consumption was found in the case of the Dual-PS control method, the smallest mass product was also found for this case. Therefore, the Position-S control strategy was identified as the optimal method for a hydraulically driven ionic liquid compressor system.

Trimmed helicoids: an architectured soft structure yielding soft robots with high precision, large workspace, and compliant interactions

The development and use of architectured structures is changing the means by which we design and fabricate soft robots. These materials utilize their topology and geometry to control physical and mechanical structural properties. We propose an architectured structure based on trimmed helicoids that allows for independent regulation of the bending and axial stiffness which facilitates tuneability of the resulting soft robot properties. Leveraging FEA and computational analysis we select a geometry that provides an optimal trade-off between controllability, sensitivity to errors in control, and compliance. By combining these modular trimmed helicoid structures in conjunction with control methods, we demonstrate a meter-scale soft manipulator that shows control precision, large workspace, and compliant interactions with the environment. These properties enable the robot to perform complex tasks that leverage robot-human and robot-environment interactions such as human feeding and collaborative object manipulation.

Two‐order cooperative optimization of swarm control based on reinforcement learning

AbstractThis paper presents a study of the cooperative optimal swarm control problem for two‐order multi‐agent systems with partially unknown nonlinear functions. Unlike traditional approaches that consider a single error, this paper proposes to use multi‐order errors in the performance index function to achieve optimal control performance. Additionally, different proportional coefficients are assigned to illustrate the varying influences of each sequence error, and a two‐order cooperative (TOC)performance index function is designed. To address the influence of unknown nonlinear functions, a swarm control system based on sliding mode control with an actor‐critic network is constructed, which increases the applicability of the proposed method to a variety of dynamic models. Furthermore, to alleviate the computational pressure caused by the multi‐order errors in the TOC performance index function, a new reinforcement learning (RL)‐based sliding mode swarm controller is designed. The stability of the proposed controller is demonstrated using the Lyapunov function. Finally, the control model and control rate are applied to a quadrotor unmanned aerial vehicle system, and simulation results demonstrate that the multi‐agent systems can effectively achieve swarm control.Impact Statement: This paper proposes a reinforcement learning‐based sliding mode control strategy for the cooperative optimal swarm control problem, where the nonlinear functions of two‐order multi‐agent systems are only partially known. In addition, we also propose a cooperative performance index function, which takes into account multi‐order errors for optimizing the performance. This contribution is significant for research in sliding mode control strategies and error co‐optimization.