Error Disturbance Research Articles

Integration of Metrology in Grinding and Polishing Processes for Rotationally Symmetrical Aspherical Surfaces with Optimized Material Removal Functions

Aspherical surfaces, with their varying curvature, minimize aberrations and enhance clarity, making them essential in optics, aerospace, medical devices, and telecommunications. However, manufacturing these surfaces is challenging because of systematic errors in CNC equipment, tool wear, measurement inaccuracies, and environmental disturbances. These issues necessitate precise error compensation to achieve the desired surface shape. Traditional methods for spherical optics are inadequate for aspherical components, making accurate surface shape error detection and compensation crucial. This study integrates advanced metrology with optimized material removal functions in the grinding and polishing processes. By combining numerical control technology, computer technology, and data analysis, we developed CAM software (version 1) tailored for aspherical surfaces. This software uses a compensation correction algorithm to process error data and generate NC programs for machining. Our approach automates and digitizes the grinding and polishing process, improving efficiency and surface accuracy. This advancement enables high-precision mass production of rotationally symmetrical aspherical optical components, addressing existing manufacturing challenges and enhancing optical system performance.

Adaptive Nonsingular Fast Terminal Sliding Mode Control for Shape Memory Alloy Actuated System

Due to its high power-to-weight ratio, low weight, and silent operation, shape memory alloy (SMA) is widely used as a muscle-like soft actuator in intelligent bionic robot systems. However, hysteresis nonlinearity and multi-valued mapping behavior can severely impact trajectory tracking accuracy. This paper proposes an adaptive nonsingular fast terminal sliding mode control (ANFTSMC) scheme aimed at enhancing position tracking performance in SMA-actuated systems by addressing hysteresis nonlinearity, uncertain dynamics, and external disturbances. Firstly, a simplified third-order actuator model is developed and a variable gain extended state observer (VGESO) is employed to estimate unmodeled dynamics and external disturbances within finite time. Secondly, a novel nonsingular fast terminal sliding mode control (NFTSMC) law is designed to overcome singularity issues, reduce chattering, and guarantee finite-time convergence of the system states. Finally, the ANFTSMC scheme, integrating NFTSMC with VGESO, is proposed to achieve precise position tracking for the prosthetic hand. The convergence of the closed-loop control system is validated using Lyapunov’s stability theory. Experimental results demonstrate that the external pulse disturbance error of ANFTSMC is 8.19°, compared to 19.21° for the comparative method. Furthermore, the maximum absolute error for ANFTSMC is 0.63°, whereas the comparative method shows a maximum absolute error of 1.03°. These results underscore the superior performance of the proposed ANFTSMC algorithm.

Fuzzy PI vibration suppression control strategy for space double flexible telescopic manipulator with fractional disturbance observer

The space double flexible telescopic manipulator (SDFTM) with time-varying length, is called the space flexible manipulator and can carry out reciprocating linear telescopic motion along its axis. It possesses characteristics such as lightweight flexibility, small space occupation, large turning radius, and a wide working range. These features can assist astronauts in performing complex tasks in dangerous outer space environments and offer broad application prospects. However, due to the highly nonlinear and time-varying length characteristics of the space flexible manipulator, its dynamic modeling becomes more complex and challenging. Moreover, during the rotation of the space flexible manipulator, its inherent flexible behavior can trigger vibration phenomena, significantly impacting the execution accuracy of on-orbit tasks. Therefore, proposing control methods to address vibration issues is an effective approach. Thus, achieving stable and precise operation of the space flexible manipulator necessitates the precise establishment of its dynamic model and the development of high-precision control algorithms, which have become key research directions. Firstly, a time-varying dynamic modeling method of the space manipulator is proposed by combining the Lagrange principle and flexible beam vibration theory. Unlike the traditional method, which only considers the limit length of the space manipulator under static conditions, the dynamic process of the space manipulator presented during actual operation is fully considered, which greatly improves the modeling accuracy. Then, a time-varying model based on the fuzzy PI real-time control strategy (FD-FZPI) with fractional order disturbance observer (FO-DOB) is proposed. Based on the robust stability principle, a more accurate FO-DOB is proposed based on integer order DOB, coupled with a fuzzy PI controller to compensate for the external disturbance and modeling error of the space manipulator. Finally, numerical simulation analysis is carried out, and the results show that the average parameter difference between the time-varying dynamic model and the static dynamic model can reach about 5.2 %, which indicates the accuracy of the proposed dynamic modeling method. At the same time, the proposed FD-FZPI control strategy achieves about 14.6 % performance improvement over the traditional DOB-PI control method, indicating that the proposed control strategy can effectively suppress the flutter fluctuation of the space manipulator and achieve accurate and stable dynamic tracking.

Neuroadaptive high-order fully-actuated system approach for roll autopilot with unknown uncertainties

In this paper, a neuroadaptive high-order fully-actuated system approach control scheme incorporating the disturbance observer technique is proposed for the missile roll autopilot, subject to model uncertainties generated by the induced roll moment, along with actuator control efficiency deterioration and external disturbance. To address model uncertainties, the radial basis function neural network is implemented. The external disturbance and approximation error are treated as compound disturbances and estimated by a nonlinear disturbance. To avoid the “differential explosion” inherent in the backstepping technique, the high-order fully-actuated system approach is invoked to track the desired roll angle command. The semi-globally uniformly bounded of the closed-loop system is demonstrated via the Lyapunov method. Numerous simulations under various conditions have been conducted to verify the effectiveness of the proposed roll autopilot.

Less conservative robust reference governors and their applications

The applications of reference governors to systems with unmeasured set-bounded disturbances can lead to conservative solutions. This conservatism can be reduced by estimating the disturbance from output measurements and canceling it in the nominal control law. In this paper, a reference governor based on such an approach is considered and time-varying, disturbance and state estimation errors bounding sets are derived. Consequently, the traditional implementation of a reference governor, which exploits a constraint admissible positively-invariant set of constant commands and initial states, is replaced by one which utilizes a time-dependent sequence of similar sets (which are not necessary nested). Examples are reported which include two applications to longitudinal control of aircraft that illustrate handling of elevator uncertainty and wing icing.

Adaptive fixed-time fuzzy fault-tolerant control for robotic manipulator with unknown friction and composite actuator faults

This paper exploits the issue of fixed-time (FT) tracking control (TC) for uncertain robotic manipulator systems (RMS) with composite actuator faults (CAFs), unknown disturbances and joint friction. The fuzzy logic system (FLS) algorithm is introduced to fit the unknown uncertainties including unmodeled parameters, CAFs and joint friction of the RMS, after which a relevant updating algorithm is employed to generate the unidentified boundary of the plant disturbance and FLS approximation error. Then a novel adaptive fuzzy sliding mode (SM) fault-tolerant controller is synthesized, which not only guarantees the boundedness of estimation errors of the involved adaptive parameters, but also actualizes the reachability of the predevised sliding surface. Furthermore, the proof that the system trajectories can achieve the FT convergence to the expected position signals, is then provided on account of the FT stability theory. Ultimately, simulation experiments exhibit the rationality based upon the current control algorithm.

Disturbance rejection based consensus controller for swarm system

This article introduces a robust controller designed to facilitate consensus among leader-follower agents in a swarm system. The method employs algebraic graph theory to establish a consensus framework and incorporates a sliding surface-based uncertainty and disturbance estimator (SUDE) to mitigate the effects of nonlinearities, external disturbances, and parametric uncertainty inherent in swarm systems. The stability of the SUDE controller is examined using Lyapunov theory. Importantly, this approach operates without prior knowledge of disturbance bounds, relying instead on the dynamics of disturbance error. Simulation studies demonstrate the effectiveness of the proposed control methodology, showing that the SUDE controller expedites consensus achievement while ensuring smooth and non-chattering control behavior. The proposed SUDE controller demonstrates a notable improvement in achieving position and velocity consensus in both the X and Y directions, with a 75% and 66% faster convergence rate, respectively, when compared to the conventional sliding mode controller (SMC). Additionally, the performance of the proposed controller is assessed under various external disturbances and parametric uncertainty conditions. Comparative analyses using root mean square error (RMSE) and integral absolute time error (IATE), both based on consensus error, confirm the effectiveness of the proposed SUDE consensus controller.

Adaptive event-triggered tracking control for nonlinear systems with prescribed performance: A time-domain mapping approach

This paper proposed an event-triggered adaptive fuzzy prescribed time control strategy for a class of nonlinear systems with uncertain external disturbances and unknown virtual control coefficients under prescribed performance. A novel time-domain mapping technique is proposed to transform the prescribed time tracking control problem into an asymptotic convergence problem. An observer is designed to estimate for the external disturbance and fuzzy approximation error of fuzzy logic systems (FLSs). The problem of “explosion of complexity” is addressed by applying dynamic surface control (DSC) technique to backstepping. An adaptive fuzzy event-triggered strategy is designed using the Lyapunov stability theorem. This strategy ensures that, after time-domain mapping, the tracking error asymptotically converges to zero. Additionally, there is no Zeno behavior and all system signals are bounded. Finally, the effectiveness of the proposed scheme is verified through three simulation examples.

Tracking control of chained non‐holonomic systems with asymmetric function constraints and external disturbance

AbstractFor a class of chain non‐holonomic systems with external disturbance and function constraints, the tan‐type barrier Lyapunov function is used to solve the constraints of the system, and then the non‐linear disturbance observer is used to deal with the disturbance so that the disturbance error eventually converges exponentially. The control strategy designed by the backstepping method can effectively ensure that signals are bounded without violating the respective constraints. Through the simulation design of a three‐stage wheeled mobile robot, the effectiveness of the control scheme is verified again by the results.

Neural network‐based output‐feedback adaptive control for a class of uncertain strict feedback fractional‐order nonlinear systems subject to input saturation

SummaryThis paper presents neural networks (NNs) adaptive controller for an uncertain fractional‐order nonlinear system in strict‐feedback form, subject to input saturation, unavailable states for measurement, and external disturbances. The fractional‐order adaptive laws are derived based only on the output tracking error thanks to the implementation of the strictly positive real (SPR) property, differently from the existing results in the literature of fractional‐order strict‐feedback systems where all system states are used in the adaptive laws. The proposed design approach addresses the nonaffine nature of the control input due to saturation nonlinearity by using the mean value theorem and follows a nonrecursive design by using a state transformation where the advantage is twofold. First, it eliminates the explosion complexity found in back‐stepping control‐based approaches design, and second, it reduces the approximators units and parameters in controller implementation. Furthermore, an observer is introduced to estimate the unavailable newly defined states, and then an output adaptive feedback control design is ensured. An NN is used to approximate the unknown ideal control law, and an auxiliary control term is appended to deal with saturation effect, unknown disturbance, and approximation errors. The tracking error is proved to converge asymptotically to a bounded set using the Lyapunov theory. Simulation results on three examples show the effectiveness of the proposed approach.

High-temperature thermal disturbance elimination method based on multi-channel extraction and correction.

Digital image correlation (DIC) technology has been widely used in high-temperature measurement fields. However, due to the complexity of high-temperature environments, there are many interference factors that limit the development of high-temperature DIC technology, among which thermal disturbance is one of the most significant factors that severely affects the measurement accuracy of high-temperature DIC. In this paper, a multi-channel separation technique combined with a low-cost laser speckle device is proposed to eliminate thermal disturbance errors in high-temperature DIC measurements. First, a blue laser speckle generation system is independently designed to produce the most suitable speckle particles, and the best laser speckle is determined and projected onto the blue background white spot pattern. Then a green LED illuminates the sample to provide illumination for the sample's own grayscale characteristics. A color camera collects photos, and the obtained images are processed with channel separation to extract and calculate the displacement of different channels. Finally, the displacement fields of the green and blue channels are subtracted to separate the thermal disturbance error and correct the measurement values. In this paper, a laser speckle projection system is first assembled, followed by a comprehensive evaluation of the projected speckle and, finally, a DIC experimental system is constructed for verification experiments at both room temperature and high temperature, and the corrected values are compared with the true values. The results show that the corrected values are highly consistent with the true values, which verifies the reliability of the proposed method.

Stabilizing traffic flow by autonomous vehicles: Stability analysis and implementation considerations

The emergence of autonomous vehicles (AVs) is expected to revolutionize road transportation and it has been demonstrated that AVs can be employed as mobile actuators to effectively dissipate stop and go waves. This paper proposes a comprehensive theoretical framework including stability and implementation considerations for analyzing traffic platoon control via sparse AVs and discussing the number of HDVs that can be stabilized by one AV. Considering multiple disturbance sources, we first discuss the output error composition and analyze traffic dynamics in a platoon system. Then we discuss the Lyapunov asymptotic stability (LAS) of the system based on eigenvalue analysis and propose a novel ring string stability (RSS), which ensures the boundedness of disturbance error and guarantees that the effect of disturbance will not be amplified over a lap. The proposed RSS is flexible and applicable to mixed traffic, since the string instability for predecessor-follower pairs are allowed, provided they can be compensated by AVs. In addition, the practical constraints including driving safety and vehicle physical limitations are modeled. The worst-case evaluation of constraints is conducted to reproduce the critical scenarios in real life. Finally, we conduct numerical experiments and analyze the traffic platoon control from the aspects of both stability and practical implementations. New findings include: (1) The introduction of AVs is beneficial to dampen the waves and stabilize the HDV flow. (2) Considering the implementation issues, there is an upper bound of HDVs that can be stabilized by one single AV. (3) As the number and amplitudes of exogenous disturbances increase, it will be more challenging for sparse AVs to ensure stability and meanwhile satisfy the practical constraints.

SNAPIG: a model to study nutrient digestion and absorption kinetics in growing pigs based on diet and ingredient properties

Current feed formulation and evaluation practices rely on static values for the nutritional value of feed ingredients and assume additivity. Hereby, the complex interplay among nutrients in the diet and the highly dynamic digestive processes are ignored. Nutrient digestion kinetics and diet × animal interactions should be acknowledged to improve future predictions of the nutritional value of complex diets. Therefore, an in silico nutrient-based mechanistic digestion model for growing pigs was developed: “SNAPIG” (Simulating Nutrient digestion and Absorption kinetics in PIGs). Aiming to predict the rate and extent of nutrient absorption from diets varying in ingredient composition and physicochemical properties, the model represents digestion kinetics of ingested protein, starch, fat, and non-starch polysaccharides, through passage, hydrolysis, absorption, and endogenous secretions of nutrients along the stomach, proximal small intestine, distal small intestine, and caecum + colon. Input variables are nutrient intake and the physicochemical properties (i.e. solubility, and rate and extent of degradability). Data on the rate and extent of starch and protein hydrolysis of different ingredients per digestive segment were derived from in vitro assays. Passage of digesta from the stomach was modelled as a function of feed intake level, dietary nutrient solubility and diet viscosity. Model evaluation included testing against independent data from in vivo studies on nutrient appearance in (portal) blood of growing pigs. When simulating diets varying in physicochemical properties and nutrient source, SNAPIG can explain variation in glucose absorption kinetics (postprandial time of peak, TOP: 20–100 min observed vs 25–98 min predicted), and predict variation in the extent of ileal protein and fat digestion (root mean square prediction errors (RMSPE) = 12 and 16%, disturbance error = 12 and 86%, and concordance correlation coefficient = 0.34 and 0.27). For amino acid absorption, the observed variation in postprandial TOP (61 ± 11 min) was poorly predicted despite accurate mean predictions (58 ± 34 min). Recalibrating protein digestion and amino acid absorption kinetics require data on net-portal nutrient appearance, combined with observations on digestion kinetics, in pigs fed diets varying in ingredient composition. Currently, SNAPIG can be used to forecast the time and extent of nutrient digestion and absorption when simulating diets varying in ingredient and nutrient composition. It enhances our quantitative understanding of nutrient digestion kinetics and identifies knowledge gaps in this field of research. Already useful as research tool, SNAPIG can be coupled with a postabsorptive metabolism model to predict the effects of dietary and feeding-strategies on the pig’s growth response.

Bayesian Restricted Stein-rule Least Squares with non-spherical Disturbances

Bayesian restricted stein-rule least squares is a novelty estimator introduced to obtain parameters of the family of restricted least squares with intertwined heteroscedastic and autocorrelated disturbance errors. Errors in regression modelling set to measure the quality of the data and or model of which intertwining of most prominent errors disturbances play a significant role in determining the quality of the aforementioned. Efforts were geared towards the comparison of the performances and relative efficiency of the small sample property of Bayesian and classical family of restricted stein-rule least squares. The sample size was set at 25 to capture the intertwined disturbance errors, and the iteration of the Monte Carlo simulation was set at 10000 for both classical and Bayesian paradigms. and for both Autoregressive of order one (AR(1)) and Moving average of order one (MA(1)) processes respectively were set asymmetrically as -0.8,-0.5,-0.3, 0,0.3,0.5 and 0.8 while as a scale of heteroscedasticity was set at 0-homoscedasticity, -0.3-mild,-0.5-moderate and (0.7,0.9)-severe. The outcome of the study pointed out that Bayesian estimation (both posterior mean and Bayes estimates) for both restricted and restricted stein-rule estimators outperformed classical restricted and restricted stein-rule estimators in both performances and relative efficiency. It is therefore recommended to make use of the Bayesian framework when encountering similar disturbances in a small sample size.

Disturbance-observer-based discrete-time sliding mode predictive control for overhead cranes with matched and unmatched disturbances

In actual working environments, underactuated overhead cranes are susceptible to the adverse effects of the mechanical friction, uncertain parameters, and other disturbances. In particular, if the payload is disturbed (i.e. the unmatched disturbance), unpredictable swings would occur. Therefore, a discrete time sliding mode predictive control for overhead cranes based on a disturbance observer is proposed. Specifically, to eliminate uncertain disturbances, we propose a disturbance observer to estimate and compensate the disturbance to the input channel. Then, a new discrete sliding mode control law is obtained by adding the disturbance error estimator to the discrete sliding mode surface. In order to reduce the chattering inherent in the sliding mode, we integrate the discrete sliding mode law into the model predictive control, and use the rolling optimization and feedback correction of the predictive control, and finally output the optimal control law to act on the system. This control method can not only further improve the control accuracy, but also has sufficient robustness. Simulation and robustness experiments under various conditions verify the effectiveness of the proposed controller.

Data-driven predictive control for demand side management: Theoretical and experimental results

Demand side management is perceived as a tool to support a secure and reliable energy system operation amid growing integration of renewable energy resources. However, the lack of scalable modeling and control procedures hinders the practical implementation. To address this challenge, this paper proposes a novel signal matrix model predictive control algorithm. Compared to existing data-driven methods, this approach explicitly provides stochastic predictions considering both disturbance and measurement errors with few tuning parameters, ensuring reliability by high-probability constraint satisfaction. The performance is extensively compared with three state-of-the-art algorithms in a space heating case study using a high-fidelity simulator. The results are further validated with physical experiments using the same system that the simulator is based on. To assess transferability, the algorithm is further implemented on diverse controlled systems, including a domestic hot water heating system and a stationary electric battery. The simulation results show that, compared to existing data-driven methods, the proposed approach improves constraint satisfaction and energy savings by up to 90 % and 8 %, respectively. The experimental results further confirm that the algorithm is applicable to multiple tasks of demand side management, with reasonable control performance observed in all case studies.

Adaptive Saturated Path Following Control of Underactuated AUV With Unmodeled Dynamics and Unknown Actuator Hysteresis

This article addresses the three-dimensional (3-D) path-following problem of underactuated autonomous underwater vehicles (AUVs) in the presence of multiple uncertainties and nonlinearities, including unmodeled dynamics, actuator hysteresis, input saturation, and oceanic disturbance. First, the AUV kinetic model is reconstructed as a second-order nonlinear system by coupling the smooth saturation function and hysteresis dynamics of actuators. Second, the 3-D tracking error is derived in a simplified manner based on the relative motion theory. Then the approach angle-based symmetrical hyperbolic-tangent line-of-sight (SHLOS) guidance and kinematic control scheme are designed to ensure the global asymptotic stability of the kinematic system. Third, two sets of kinetic control schemes are designed without prior knowledge of AUV dynamics and disturbance, where the Nussbaum gain technique is applied to deal with the unknown hysteresis parameter. The application of the reconstructed kinetic model avoids the degraded performance caused by actuator nonlinearities. The first scheme is improved by adaptively estimating the bound of the generalized disturbance-like term which lumps the unmodeled dynamics, external disturbance, and saturation approximation error. The improved scheme guarantees the global stability of the system and transient tracking performance. Finally, comparative simulation studies under dynamic oceanic environments prove the effectiveness and robustness of the proposed control schemes.

Detection and minimizing the error caused by hand tremors using a leap motion sensor in operating a surgeon robot

The human hand has many limitations in microsurgery, but these limitations are minimized in the robot. Every vibration causes changes in the surgical process. By using different methods, the errors caused by the sensor and the surgeon's hand can be minimized in surgery. This paper aims to reduce the errors caused by the leap motion sensor, such as noise, disturbance, and surgeon's hand errors, such as hand tremors. First, hand tremors (physiological or due to fatigue) were detected using the leap motion sensor; Then, with different methods, including fast Fourier transform and its inverse, wavelet transform, and Empirical mode decomposition (EMD), the vibration was removed. According to the obtained results, the maximum calculated error beforehand tremor compensation is 0.19, 0.16, 0.07, 0.14, 0.2, respectively, for moving on the right line; Also, the maximum vibration compensation error for these people is equal to 0.15, 0.07, 0.06, 0.1, 0.16 respectively.

Adaptive Neural Network Global Fractional Order Fast Terminal Sliding Mode Model-Free Intelligent PID Control for Hypersonic Vehicle’s Ground Thermal Environment

In this paper, an adaptive neural network global fractional order fast terminal sliding mode model-free intelligent PID control strategy (termed as TDE-ANNGFOFTSMC-MFIPIDC) is proposed for the hypersonic vehicle ground thermal environment simulation test device (GTESTD). Firstly, the mathematical model of the GTESTD is transformed into an ultra-local model to ensure that the control strategy design process does not rely on the potentially inaccurate dynamic GTESTD model. Meanwhile, time delay estimation (TDE) is employed to estimate the unknown terms of the ultra-local model. Next, a global fractional-order fast terminal sliding mode surface (GFOFTSMS) is introduced to effectively reduce the estimation error generated by TDE. It also eliminates arrival time, accelerates the convergence speed of the sliding phase, guarantees finite time arrival, avoids the singularity phenomenon, and bolsters robustness. Then, as the upper bound of the disturbance error is unknown, an adaptive neural network (ANN) control is designed to approximate the upper bound of the estimation error closely and mitigate the chattering phenomenon. Furthermore, the stability of the control system and the convergence time are proven by the Lyapunov stability theorem and are calculated, respectively. Finally, simulation results are conducted to validate the efficacy of the proposed control strategy.

State and adaptive disturbance observer co‐design for incrementally quadratic nonlinear descriptor systems with nonlinear outputs

AbstractThe problem of designing state and adaptive disturbance observers for a class of nonlinear descriptor systems with the disturbance generated by an unknown exogenous system is addressed in this paper. Especially, the nonlinear terms satisfy incremental quadratic constraints parameterized by a bunch of multiplier matrices, which can represent various classes of nonlinearities. For three different cases in the output equation, the state and adaptive disturbance observers are proposed. Some sufficient conditions formulated in terms of linear matrix inequalities and algebraic constraints are derived to guarantee that state and disturbance error systems, simultaneously, are asymptotically stable with performance level. The availability of proposed observers is validated by two practical examples.