Error Control Research Articles

Finite-time event-triggered output-feedback adaptive decentralized echo-state network fault-tolerant control for interconnected pure-feedback nonlinear systems with input saturation and external disturbances: A fuzzy control-error approach

In this paper, we introduce a decentralized Event-Triggered fault-tolerant echo-state network (ESN) direct adaptive controller for uncertain interconnected nonlinear systems in pure-feedback form. The proposed controller addresses input saturation, actuator faults, external disturbances, and unavailable states for measurement. Unlike the existing works in the literature that adapts the ESN weights using the tracking error, our method employs the control error that is estimated using a fuzzy inference system, to derive the adaptation laws. The complexity explosion typically seen with recursive back-stepping and Dynamic Surface Control (DSC) designs is completely avoided. Our control scheme addresses four types of state-dependent actuator faults: bias, drift, loss of accuracy, as well as loss of effectiveness. The ESNs approximate unknown ideal control laws, while robust terms are added to enhance the stability of the closed-loop system. Stability analysis ensures that tracking errors converge, in finite time, to a small compact set near the origin due to the strictly positive real (SPR) property. Simulation results of a quadrotor UAV and a mathematical system are presented. A comparative analysis is performed with another approach to emphasize the effectiveness of the proposed method.

Enhancing robustness to forecast errors in availability control for airline revenue management

Enhancing robustness to forecast errors in availability control for airline revenue management

Covariate-adaptive randomization inference in matched designs

Abstract It is common to conduct causal inference in matched observational studies by proceeding as though treatment assignments within matched sets are assigned uniformly at random and using this distribution as the basis for inference. This approach ignores observed discrepancies in matched sets that may be consequential for the distribution of treatment, which are succinctly captured by within-set differences in the propensity score. We address this problem via covariate-adaptive randomization inference, which modifies the permutation probabilities to vary with estimated propensity score discrepancies and avoids requirements to exclude matched pairs or model an outcome variable. We show that the test achieves type I error control arbitrarily close to the nominal level when large samples are available for propensity score estimation. We characterize the large-sample behaviour of the new randomization test for a difference-in-means estimator of a constant additive effect. We also show that existing methods of sensitivity analysis generalize effectively to covariate-adaptive randomization inference. Finally, we evaluate the empirical value of combining matching and covariate-adaptive randomization procedures using simulations and analyses of genetic damage among welders and right-heart catheterization in surgical patients.

Augmented Kalman Estimator and Equivalent Replacement Based Taylor Series-LQG Control for a Magnetorheological Semi-Active Suspension

This research presents an augmented Kalman estimator and an equivalent replacement-based Taylor series (ERBTS)-linear quadratic Gaussian (LQG) control strategy to cope with the control accuracy and response delay of magnetorheological (MR) dampers for vehicle semi-active suspensions. The parameters in the MR model are identified from experimental measurements. Then, two main sources of control error, namely, modelling error and real-time variety of the MR damper output force, are defined as an integrated compound real-time variety. Subsequently, they are written into a differential equation with characteristics of the minimum system to augment the state equation of the semi-active suspension system. The augmented Kalman estimator is constructed to estimate the abovementioned compound real-time variety. To calculate an acceptable time-delay compensation predictive control force, an equivalent operation is implemented beforehand in the suspension comprehensive performance index by replacing a part of the squared time-delay control force with the corresponding predictive control force. Simulation results verify the effectiveness of the proposed augmented Kalman estimator, and the newly developed ERBTS-LQG controller almost achieves control effectiveness of the ideal time delay free semi-active suspension.

Establishment and Verification of the UAV Coupled Rotor Airflow Backward Tilt Angle Controller

At present, all the flight controllers of agricultural UAVs cannot accurately and quickly control the influencing factors of the UAV coupled rotor airflow backward tilt angle during the application process. To solve the above problem, a Rotor Airflow Backward Tilt Angle (RABTA) controller is established in this paper. The RABTA controller integrates advanced sensor technology with a novel algorithmic approach, utilizing real-time data acquisition and state–space analysis to dynamically adjust the UAV’s rotor airflow, ensuring precise control of the backward tilt angle. The control effect of the traditional flight controller and RABTA controller in the process of pesticide application and the corresponding operation effect are compared and analyzed. The comparison results show that the RABTA controller reduces the control error to less than 1 degree, achieving a 48.3% improvement in the uniformity of the distribution of pesticides droplets across the crop canopy, which means that the UAV field application effect is implemented and the innovation of the UAV field application control mode is realized.

An intelligent heating system based on the Internet of Things and STM32 microcontroller

Under the rapid growth of Internet of Things technology, many households are moving towards smart solutions. Addressing the inflexibility of temperature control in traditional heating systems, this research focuses on designing an intelligent heating system. To enhance flexibility and intelligence, an intelligent heating system based on the Internet of Things and STM32 microcontroller is proposed. Furthermore, the study identifies limitations of traditional proportional-integral-derivative control methods and establishes an optimization control model for heating system output temperature based on the Dynamic Matrix Control algorithm. Results indicate that the system's web interface successfully draws temperature curves, displaying clear data on detected temperature and humidity. The output temperature optimization control model shows a temperature rise of 2 °C and a temperature control error index of 0.0543 during the initial heating stage, and a control error index of 0.0353 during the mid-heating stage when the valve relative opening is close to 0. And the temperature control effect is better than traditional PID control, fuzzy PID control, genetic algorithm based PID control, and predictive feedback predictive control, without obvious indoor temperature overshoot phenomenon, which has certain advantages. In conclusion, the proposed system and model exhibit favorable application outcomes, offering technological support for the intelligent management of heating systems.

Alignment error modeling and control of a double-sided microlens array during precision glass molding

Double-sided microlens arrays (DSMLAs) include combinations of two single-sided MLAs to overcome positioning errors and greatly improve light transmissivity compared to other types of lenses. Precision glass molding (PGM) is used to fabricate DSMLAs, but controlling alignment errors during this process is challenging. In this paper, a mold assembly was manufactured with a novel combination of materials to improve the alignment accuracy of mold cores during PGM by using the nonlinear thermal expansion characteristics of the various materials to improve the DSMLA alignment accuracy. By establishing a mathematical model of the DSMLA alignment error and a thermal expansion model of the mold-sleeve pair, the relationship between the maximum alignment error of the DSMLA and the mold-sleeve gap was determined. This research provides a method to optimize the mold-sleeve gap and minimize the alignment error of the DSMLA. The measured DSMLA alignment error was 10.56 μm, which is similar to the predicted maximum alignment error. Optical measurements showed that the uniformity of the homogenized beam spot was 97.81%, and the effective homogeneous area accounted for 91.66% of the total area. This proposed method provides a novel strategy to improve the performance of DSMLAs.

Beyond the two-trials rule.

The two-trials rule for drug approval requires "at least two adequate and well-controlled studies, each convincing on its own, to establish effectiveness." This is usually implemented by requiring two significant pivotal trials and is the standard regulatory requirement to provide evidence for a new drug's efficacy. However, there is need to develop suitable alternatives to this rule for a number of reasons, including the possible availability of data from more than two trials. I consider the case of up to three studies and stress the importance to control the partial Type-I error rate, where only some studies have a true null effect, while maintaining the overall Type-I error rate of the two-trials rule, where all studies have a null effect. Some less-known -value combination methods are useful to achieve this: Pearson's method, Edgington's method and the recently proposed harmonic mean -test. I study their properties and discuss how they can be extended to a sequential assessment of success while still ensuring overall Type-I error control. I compare the different methods in terms of partial Type-I error rate, project power and the expected number of studies required. Edgington's method is eventually recommended as it is easy to implement and communicate, has only moderate partial Type-I error rate inflation but substantially increased project power.

Sliding mode controller with neural network compensation for tank

The control effectiveness of the all-electric tank stabilizers directly affects the firing accuracy of the tank gun. In order to improve the firing accuracy of the moving tank, this paper proposes a sliding mode control (SMC) strategy using neural network feed-forward compensation for the tank bidirectional stabilizers. SMC technology can quickly and effectively deal with uncertainties, unmodeled terms, and external disturbances in complex tank systems. Neural networks possess the advantage of approximating arbitrary continuous functions in finite time, realizing the estimation of SMC control errors with feed-forward compensation. The Lyapunov stability analysis proves that the designed controller can achieve asymptotic stabilization in finite time. Finally, both co-simulation and physical experiments are conducted. The results show that the proposed control strategy possesses better tracking speed and tracking accuracy compared to the conventional controller and exhibits strong robustness.

Repeated Context-Specific Actions Disrupt Feedforward Adjustments in Motor Commands in Younger and Older Adults.

The flexibility of the motor system to adjust a planned action before or during the execution of the movement in response to sensory information is critical for preventing errors in motor control. As individuals age, this function declines, leading to an increased incidence of motor errors. While sensory processing and cognitive decline are known contributors to this impairment, here, we test the hypothesis that repetition of context-specific planned actions interferes with the adjustment of feedforward motor commands. Younger and older participants were instructed to grasp and lift a T-shaped object with a concealed, off-sided center of mass and minimize its roll through anticipatory force control, relying predominantly on predictive model-driven planning (i.e., sensorimotor memories) developed through repeated lifts. We selectively manipulate the number of trial repeats with the center of mass on one side before switching it to the other side of the T-shaped object. The results showed that increasing the number of repetitions improved performance in manipulating an object with a given center of mass but led to increased errors when the object's center of mass was switched. This deleterious effect of repetition on feedforward motor adjustment was observed in younger and older adults. Critically, we show these effects on an internal model-driven motor planning task that relies predominantly on sensorimotor memory, with no differences in sensory inputs from the repetition manipulation. The findings indicate that feedforward motor adjustments are hampered by repetitive stereotyped planning and execution of motor behavior.

A Posteriori Error Control for Fourth-Order Semilinear Problems with Quadratic Nonlinearity

A Posteriori Error Control for Fourth-Order Semilinear Problems with Quadratic Nonlinearity

Exact post-selection inference for adjusted R squared selection

Post-selection inference is developed for regression coefficients selected by the widely used model selection technique of adjusted R2. Selective inference deals with the selection aspect by conditioning inference on the model selection event. In linear models, we obtain exact post-selection inference in finite samples. Extensions to logistic regression models are discussed. A simulation study illustrates the exact type I error control, unlike classical inference. Our tests provide higher power than data splitting approaches.

Memory Fusion Controller of Fractional-Order Systems With Sliding Memory Window Under Intermittent Sampled-Data Transmission.

This work proposes a memory fusion controller design methodology for sampled-data control of fractional-order (FO) systems with sliding memory window. Composed of finite-dimensional previous inputs, the devised controller is capable of handling hereditary effect and meanwhile enabling pseudo state to satisfy general integer-order (IO) discrete plant at sampling instants. Additionally, the asymptotical stability of controller and sampling error are further guaranteed. The developed fusion controller provides an "out-of-the-box" method for users who are not familiar with FO calculus and significantly facilitates the corresponding analysis. The above mentioned approach is thereafter employed in a more sophisticated case, that is, the coordination control of FO multiagent systems (MASs) subjects to intermittent sampled-data transmission. It is proved that the achievement of consensus only relates to the connectivity of communication graph. Numerical results are presented finally to substantiate the proposed control strategy.

Event-triggered simultaneous tracking control and stabilization of underactuated USVs

This study addresses the control problem of simultaneous tracking control and stabilization for underactuated unmanned surface vessels (USVs) with only two available inputs in the surge and yaw directions. Firstly, two velocity modifications are introduced to transform the error dynamics into two decoupled subsystems. Then, a novel error transforming scheme is presented to reduce the complexity of controller expressions and overcome the difficulties caused by underactuated dynamics. Based on the above transforming scheme and the event-triggered control (ETC) mechanism, a unified control method is designed to guarantee that the error states converge to a bounded neighborhood of the origin while excluding the Zeno behavior of the controllers. In addition, two radial basis function neural networks (RBFNNs) are applied to approximate the unknown dynamics and additional control errors caused by the ETC mechanism. Finally, comparative simulation experiments are performed to validate the effectiveness of the proposed controller.

Nondimensional analysis and experimental assessment of the PII1/2DD1/2 position control of a rotary axis

Fractional Order controllers, based on derivatives and integrals of non-integer order, represent a propitious and promising research area since they can improve the performance of mechatronic systems compared to traditional Integer Order PID controllers without requiring hardware upgrades, but only using their higher tuning flexibility. Nevertheless, Fractional Order controllers are still rarely applied in real automation tasks, since their tuning is more complex and in general requires a model-based optimization for full exploitation of their capabilities. Recently, the PII1/2DD1/2 controller has been proposed, extending the classical PID by introducing a half-integral and a half-derivative term. A Bode plot-based tuning method has been presented for this controller, deriving the PII1/2DD1/2 parameters from a given set of PID gains. This method enables a significant reduction in tracking error for position control of a mechatronic axis without the need for complex model-based optimizations. In this paper, the previous results of this PII1/2DD1/2 tuning methodology have been generalized by employing a nondimensional approach. This approach highlights the conditions under which the benefits of this Fractional Order controller over the PID are most significant. The findings have been validated through experimental tests on a rotary axis, confirming the correctness and usefulness of the method.

An adaptive fluid-solid coupling time step control algorithm for unsteady conjugate heat transfer

Unsteady simulation with conjugate heat transfer has to tackle the stiffness issue that originates from the wide disparate time scales between fluid and solid heat transfer. In the loosely coupled framework, challenges remain in determining the optimal coupling time step between the fluid and solid solvers, in terms of efficiency and accuracy. This study formulates a correlation for the coupling error, together with the Proportional-Integral-Derivative (PID) control method, to propose an adaptive fluid-solid coupling time step algorithm, which dynamically adjusts the coupling time step such that the computational efficiency can be improved without sacrificing accuracy. The proposed algorithm has been tested in a 1D conjugate heat transfer application with typical operating conditions of a real engine, and the performance of different controllers (I, PI, PID) has been analyzed and compared. Results show that with the closed-loop control, all controllers can ensure the error control below the user specified tolerance at the cost of reducing efficiency. Meanwhile, with the open-loop control which is of more interest to practical applications, controllers suffer from instability issues when a conventional algorithm based on integration error is employed. The proposed coupling-error-based algorithm successfully tackles the instability issues. Compared with the baseline algorithm of the constant coupling time step, the proposed algorithm can reduce approximately 40%–90% computational cost for the 1D cases considered.

Synchronous control of tri-motor vibration system considering an adaptive back-stepping control strategy

In oil and gas drilling engineering, the anti-phase synchronization of two co-rotating rotors in a tri-motor vibration system is easy to implement owing to its inherent coupling characteristics, which can lead to the exciting force generated by the vibrating system being very small. Thus, an adaptive back-stepping control strategy is proposed to apply in a tri-motor vibrating system and improve the processing capacity of drilling fluid shaker screens. The algorithm developed in this paper is that a complex higher-order nonlinear system is simplified into lower-order subsystems by the adaptive back-stepping control theory, and then the adaptive law for varying load torque is derived by combining with the adaptive theory to regulate the velocity of motors in a closed loop. Firstly, a dynamical model to describe the tri-motor vibration system is established, and the dynamical equations of the vibrating system are derived based on the Lagrange equation. Then, the speed error controller and the phase difference error controller are designed by combining the adaptive back-stepping control algorithm with the master–slave control (MSC) structure. According to Barbalat’s lemma, the stability of the control system is analyzed to verify the feasibility of the control method. Finally, the dynamical equations of the vibrating system are used to establish an electromechanical coupling control model of a tri-motor driven planar system through the Runge–Kutta method, and it verified the reliability of the control strategy, the robustness, and the stability of the control system, respectively, and intuitively revealed the synchronization characteristics of tri-motor vibration control system. The results demonstrate the phase difference among the three motors can be accurately stabilized at zero by a phase synchronization control strategy based on the adaptive back-stepping control method, and the method is simple and fast-speed and the control system has better robustness.

Visual cue spatial context affects performance of anticipatory postural adjustments

Past research indicates that anticipatory postural adjustment (APA) errors may be due to the incorrect selection of responses to visual stimuli. In the current study we used the Simon task as a methodological tool to challenge the response selection stage of processing by presenting visual cues with conflicting spatial context; in this case generating a step response to a left pointing arrow which appears to the participant's right side or vice versa. We expected greater mediolateral APA errors, delayed APA and step onset times, and greater lateral CoP displacement prior to stepping for visual cues with incongruent spatial contexts compared to cues with congruent. Thirteen healthy young adults completed step initiation trials (n = 40) from a force platform while whole-body kinematic motion was tracked. Participants were presented with arrows pointing to the left or right, indicating to step with the left or right limb, respectively. These arrows were presented on the same side as the desired step direction (congruent) or the opposite side (incongruent). Results revealed that incongruent trials resulted in significantly more incidences of mediolateral APA errors and greater mediolateral CoP deviations during the APA compared to congruent visual cue context trials. No effects were observed for the temporal outcomes, suggesting that young adults can maintain temporal execution of steps despite these motor control errors. This study demonstrates that the spatial context of visual information significantly impacts the success of response selection processes during step initiation, furthering our knowledge of how humans integrate visual information to initiate whole body movement.

A smart parametrisation for robust adaptive PI controller applied on renewable energy power generation systems under weak and uttermost weak grid conditions

The increasing penetration of renewable energy systems into the global energy matrix is a necessity for sustainable development. The benefits impact the well-being of humanity and the environment directly. However, these power generation systems demand reliable controllers to regulate the current injection into the electrical grid. Therefore, this paper presents a smart parametrisation for a robust adaptive proportional integral controller applied to grid-injected current control of a voltage inverter source with an LCL filter. Controller parameter optimisation and the initialisation of their gains are performed using the particle swarm optimisation algorithm. Three cost functions are assessed on controller parametrisation, considering the current tracking error minimisation and control action limitations: integral of absolute error (IAE) + integral of absolute control action (IAU), integral of square error (ISE) + IAU, and integral of the time-weighted absolute error (ITAE) + IAU. A framework for performance analysis and an in-depth discussion are presented, providing a systematic decision-support methodology for controller configuration. The function determined as the best one is implemented experimentally and compared with a designer’s experience-based robust adaptive proportional integral controller using a hardware-in-the-loop test bench, where the improvements in controller performance are corroborated, contributing to the enhancement of energy quality in renewable energy systems. In addition, the controller’s robustness is evaluated on a weak grid and an uttermost weak grid, where it presents high robustness and global stability.

Smooth Matérn Gaussian random fields: Euler characteristic, expected number and height distribution of critical points

This paper studies Gaussian random fields with Matérn covariance functions with smooth parameter ν>2. Two cases of parameter spaces, the Euclidean space and N-dimensional sphere, are considered. For such smooth Gaussian fields, we have derived the explicit formulae for the expected Euler characteristic of the excursion set, the expected number and height distribution of critical points. The results are valuable for approximating the excursion probability in family-wise error control and for computing p-values in peak inference.