Initial Error Research Articles

Fixed-time Disturbance Observer-Based Finite-Time Backstepping Control for Hypersonic Vehicle

Due to the severe flight environment, hypersonic vehicles are susceptible to external disturbances and aerodynamic parameter uncertainties, dramatically challenging the precise control and stable tracking. This paper presented a fixed-time disturbance observer (FTDO) to obtain better tracking performance for hypersonic vehicles. The FTDO can improve the tracking performance under external disturbances and aerodynamic parameter perturbations, whose observation time is bounded and does not depend on the initial errors. The backstepping control can guarantee finite-time convergence with the finite-time control technique. A second-order filter is designed to process the complexity explosion problem in the traditional backstepping control and ensure the tracking system’s finite-time stability. Then with Lyapunov theory, it is proved that the control system is stable and convergent in finite time. Finally, three numerical simulation results based on typical conditions are given to show the effectiveness and advantage of the developed control scheme, and external disturbances can be estimated accurately within two seconds. These results have reference value for flight controller design of hypersonic and other aircraft in complex disturbance environments.

Sensitivity of Intrinsic Error Growth to Large-Scale Uncertainty Structure in a Record-Breaking Summertime Rainfall Event

Abstract It is recognized that the atmosphere’s predictability is intrinsically limited by unobservably small uncertainties that are beyond our capability to eliminate. However, there have been discussions in recent years on whether forecast error grows upscale (small-scale error grows faster and transfers to progressively larger scales) or up-amplitude (grows at all scales at the same time) when unobservably small-amplitude initial uncertainties are imposed at the large scales and limit the intrinsic predictability. This study uses large-scale small-amplitude initial uncertainties of two different structures—one idealized, univariate, and isotropic, the other realistic, multivariate, and flow dependent—to examine the error growth characteristics in the intrinsic predictability regime associated with a record-breaking rainfall event that happened on 19–20 July 2021 in China. Results indicate upscale error growth characteristics regardless of the structure of the initial uncertainties: the errors at smaller scales grow fastest first; as the forecasts continue, the wavelengths of the fastest error growth gradually shift toward larger scales with reduced error growth rates. Therefore, error growth from smaller to larger scales was more important than the growth directly at the large scales of the initial errors. These upscale error growth characteristics also depend on the perturbed and examined quantities: if the examined quantity is perturbed, then its errors grow upscale; if there is no initial uncertainty in the examined quantity, then its errors grow at all scales at the same time, although its smaller-scale errors still grow faster for the first several hours, suggesting the existence of the upscale error growth. Significance Statement This study compared the error growth characteristics associated with the atmosphere’s intrinsic predictability under two different structures of unobservably small-amplitude, large-scale initial uncertainties: one idealized, univariate, and isotropic, the other realistic, multivariate, and flow dependent. The characteristics of the errors growing upscale rather than up-amplitude regardless of the initial uncertainties’ structure are apparent. The large-scale errors do not grow if their initial amplitudes are much bigger than the small-scale errors. This study also examined how the error growth characteristics will change when the quantity that is used to describe the error growth is inconsistent with the quantity that contains uncertainty, suggesting the importance of including multivariate, covariant uncertainties of state variables in atmospheric predictability studies.

Three-axis high-accuracy spacecraft attitude estimation via sequential extended Kalman filtering of single-axis magnetometer measurements

Magnetometer is a highly advantageous sensor for determining a spacecraft’s attitude. This article provides a solution to the problem of spacecraft attitude estimation using magnetometer measurements only. To ensure full observability of spacecraft attitude states, it is necessary to use at least two types of sensors. Consequently, utilizing a single sensor, such as the magnetometer, poses a significant challenge for any attitude estimation algorithm, including the extended Kalman filter (EKF). Moreover, implementing the EKF algorithm, or any other attitude estimation algorithm, is computationally intensive. To address these issues, an algorithm has been developed that estimates spacecraft attitude angles and attitude rates using a sequential extended Kalman filter (SEKF). This algorithm offers numerous benefits over those found in the literature such as high accuracy, low computational resource requirements, the ability to converge even with large initial attitude and angular velocity estimation errors, and the ability to function even if two of the three measurement channels of the magnetometer are not functioning. With these benefits, the developed SEKF algorithm is capable of operating in all spacecraft operational modes, delivering accurate performance and computation time. In spite of measurements with large noise values, the high accuracy achieved by the SEKF algorithm enables the magnetometer to serve as the sole source of attitude information, even if one or two magnetometer measurement channels are not functioning.

Jigless assembly of an industrial product by a universal robotic hand mounted on an industrial robot

AbstractThis paper demonstrates that “completely-jigless” assembly of a model product that requires fitting accuracy at the level of industrial products is possible by using a universal hand with four parallel stick fingers mounted on a conventional position-control-based industrial robot. Assuming that each part is taken out of the parts bin and temporarily placed on the work table, the accuracy required for precise fitting cannot be achieved with a vision sensor alone. Introducing an appropriate grasping strategy, the initial position error of the part is absorbed by self-alignment in the process of grasping. Once the alignment is completed, the pose of the grasped part is fixed and jigless assembly is possible with a conventional industrial robot, which has high repeatability. In this paper, we use a gear unit as an example of an industrial product and present some grasping strategies with the universal hand. We also propose some subsequent assembly strategies for shafts and gears. Using those grasping and assembly strategies, it is shown that jigless assembly of the gear unit was successfully completed in the experiment. Although the target product in this paper is specific, the assembly elements in this product, such as shaft screwing, bearing insertion, and gear meshing, are also included in many other products. Therefore, the methods shown in this paper can be applied to other products.

Error Evolutions and Analyses on Joint Effects of SST and SL via Intermediate Coupled Models and Conditional Nonlinear Optimal Perturbation Method

A seasonal predictability barrier has long been noticed in ENSO forecasting with numerical models. Previous studies explored the impact of seasonal optimal initial perturbation evolutions in sea surface temperature anomalies (SSTA) on ENSO forecasting using the intermediate coupled model (ICM) via the conditional nonlinear optimal perturbation (CNOP) method. In this paper, we investigate the joint effects of SSTA and sea level anomalies (SLA) from the perspective of the optimal growth initial error (OGE). After determining the four seasonal OGEs about SSTA and SLA (i.e., SSTA-OGE, SLA-OGE and Joint-OGE), we first demonstrate the patterns, evolutions and the resulting spring predictability barrier (SPB) of the above OGEs. Then, we analyze the mechanism of OGE evolutions and SPB. Finally, we conduct observing system simulation experiments to determine the best (economic) observation network. Our experimental results indicate that the ENSO evolution error induced by SSTA-OGE and Joint-OGE presents season dependency, but SLA-OGE has no impact on ENSO evolution. Moreover, Joint-OGEs induce error evolutions and the SPB with more significant intensity than SSTA-OGEs and SLA-OGEs. From mechanism analyses, the evolutions of SSTA-OGEs are mainly dominated by Bjerknes feedback. Further, the evolution dynamics of Joint-OGEs primarily contain the continuous heating between the upper ocean combined with Bjerknes feedback and thermal diffusion in response to the discharge process. In addition, comprehensive and economical sensitive areas are identified through Joint-OGE, including the central-eastern equatorial Pacific and the western and north-eastern tropical Pacific boundary, which contribute to the ENSO prediction benefits reaching 58.31% on average.

Analysis of displacement fields with large deformations using an improved spectral digital image correlation method

In this paper, the systematic study of the analysis and measurement of displacement fields with large deformations based on an improved spectral digital image correlation (SDIC) method is conducted. As a shape function including displacement gradients has been successfully introduced, the SDIC method can deal with deformation issues involving different strains. We use the large image Fast Fourier Transform (FFT) to calculate full-field gray gradients instead of the subset image FFT. Therefore, all the gray gradients on image grids can be computed before formation of the Hessian matrix and the iterative optimization process. This can improve the efficiency and accuracy of the SDIC method. To analyze deformation with large strain, an easy-to-use and effective initial displacement estimation method for the seed point is introduced based on the correlation searching technique. The final displacements and displacement gradients of the region of interest (ROI) are then calculated by the subsequent iterative optimization algorithm, which can converge when the initial displacement error is within the range of ± 3 pixels. A set of images with noise ranging from 0 to 4 standard deviations and strains ranging from 0 to 0.6 are simulated and analyzed. The error analysis confirmed that the improved SDIC method performs well in anti-noise and can accurately analyze the displacement field. Finally, the silicone rubber specimens were tested in compression, tension and bending and analyzed to further confirm the validity of the improved SDIC method in the measurement of large deformations.

Gyro-Free Inertial Navigation Systems Based on Linear Opto-Mechanical Accelerometers.

High-sensitivity uniaxial opto-mechanical accelerometers provide very accurate linear acceleration measurements. In addition, an array of at least six accelerometers allows the estimation of linear and angular accelerations and becomes a gyro-free inertial navigation system. In this paper, we analyze the performance of such systems considering opto-mechanical accelerometers with different sensitivities and bandwidths. In the six-accelerometer configuration adopted here, the angular acceleration is estimated using a linear combination of accelerometers' read-outs. The linear acceleration is estimated similarly but requires a correcting term that includes angular velocities. Accelerometers' colored noise from experimental data is used to derive, analytically and through simulations, the performance of the inertial sensor. Results for six accelerometers, separated by 0.5 m in a cube configuration show noise levels of 10-7 m s-2 and 10-5 m s-2 (in Allan deviation) for time scales of one second for the low-frequency (Hz) and high-frequency (kHz) opto-mechanical accelerometers, respectively. The Allan deviation for the angular velocity at one second is 10-5 rad s-1 and 5×10-4 rad s-1. Compared to other technologies such as MEMS-based inertial sensors and optical gyroscopes, the high-frequency opto-mechanical accelerometer exhibits better performance than tactical-grade MEMS for time scales shorter than 10 s. For angular velocity, it is only superior for time scales less than a few seconds. The linear acceleration of the low-frequency accelerometer outperforms the MEMS for time scales up to 300 s and for angular velocity only for a few seconds. Fiber optical gyroscopes are orders of magnitude better than the high- and low-frequency accelerometers in gyro-free configurations. However, when considering the theoretical thermal noise limit of the low-frequency opto-mechanical accelerometer, 5×10-11 m s-2, linear acceleration noise is orders of magnitude lower than MEMS navigation systems. Angular velocity precision is around 10-10 rad s-1 at one second and 5×10-7 rad s-1 at one hour, which is comparable to fiber optical gyroscopes. While experimental validation is yet not available, the results shown here indicate the potential of opto-mechanical accelerometers as gyro-free inertial navigation sensors, provided the fundamental noise limit of the accelerometer is reached, and technical limitations such as misalignments and initial conditions errors are well controlled.

Analytical Uncertainty Propagation and Robust Trajectory Optimization for Continuous-Thrust Relative Motion

Analytical continuous-thrust uncertainty propagation is derived under the control error and the initial navigation error for the linear relative motion near elliptic orbits. Two continuous-thrust control error models are considered: control-linear error model and thrust-magnitude-direction error model. The analytical uncertainty propagation expression is obtained by using the thrust series expansion with respect to eccentric anomaly. Based on the derived analytical uncertainty propagation, the robust energy-optimum and fuel-optimum orbital rendezvous problems are formulated as nonlinear programming ones. In particular, the robust energy-optimum rendezvous problem under the control-linear error model is converted into solving a one-dimensional equation. Several numerical examples indicate that the derived analytical uncertainty propagation is accurate and that the developed robust trajectory optimization approach is computationally efficient.

Stress tensor similarity index based on Euclidean distance for numerical back analysis of in situ stress fields

Evaluating the similarity between measured and predicted stress states is critical for the numerical back analysis of three-dimensional in situ stress fields. However, conventional nontensor approaches process stress components or the magnitude and orientation of principal stresses separately, which may yield contradictory results among multiple indices. To address this issue, we propose the Euclidean distance similarity (EDS), which is a dimensionless single index based on spatial distance. As a case study, EDS was applied to a site with complex geological conditions for verification. EDS can be derived in the form of stress component or principal stress. EDS exhibited a positive correlation with the initial difference and magnitude error of principal stresses and changed periodically with the Euler angle of rotation around the principal stress. Moreover, three magnitude errors and three Euler angles had different effects on EDS. Finally, EDS can be classified into five levels (i.e., extremely high, high, normal, low, and very low) according to the magnitude errors of principal stresses and Euler angles, where the “normal” level is recommended as a general error standard. The thresholds of EDS differ according to the initial stress level. These results are a valuable contribution to the similarity evaluation of stress tensors.

An improved particle filter indoor fusion positioning approach based on Wi-Fi/ PDR/ geomagnetic field

The existing indoor fusion positioning methods based on Pedestrian Dead Reckoning (PDR) and geomagnetic technology have the problems of large initial position error, low sensor accuracy, and geomagnetic mismatch. In this study, a novel indoor fusion positioning approach based on the improved particle filter algorithm by geomagnetic iterative matching is proposed, where Wi-Fi, PDR, and geomagnetic signals are integrated to improve indoor positioning performances. One important contribution is that geomagnetic iterative matching is firstly proposed based on the particle filter algorithm. During the positioning process, an iterative window and a constraint window are introduced to limit the particle generation range and the geomagnetic matching range respectively. The position is corrected several times based on geomagnetic iterative matching in the location correction stage when the pedestrian movement is detected, which made up for the shortage of only one time of geomagnetic correction in the existing particle filter algorithm. In addition, this study also proposes a real-time step detection algorithm based on multi-threshold constraints to judge whether pedestrians are moving, which satisfies the real-time requirement of our fusion positioning approach. Through experimental verification, the average positioning accuracy of the proposed approach reaches 1.59 m, which improves 33.2% compared with the existing particle filter fusion positioning algorithms.

Assessment of predictability of the Loop Current in the Gulf of Mexico from observing system experiments and observing system simulation experiments

This study presents results from numerical model experiments with a high-resolution regional forecast system to evaluate model predictability of the Loop Current (LC) system and assess the added value of different types of observations. The experiments evaluate the impact of surface versus subsurface observations as well as different combinations and spatial coverage of observations on the forecasts of the LC variability. The experiments use real observations (observing system experiments) and synthetic observations derived from a high-resolution independent simulation (observing system simulation experiments). Model predictability is assessed based on a saturated error growth model. The forecast error is computed for the sea surface height fields and the LC frontal positions derived from the forecasts and control fields using two metrics. Estimated model predictability of the LC ranges from 2 to 3 months. Predictability limit depends on activity state of the LC, with shorter predictability limit during active LC configurations. Assimilation of subsurface temperature and salinity profiles in the LC area have notable impact on the medium-range forecasts (2–3 months), whereas the impact is less prominent on shorter scales. The forecast error depends on the uncertainty of the initial state; therefore, on the accuracy of the analysis providing the initial fields. Forecasts with the smallest initial error have the best predictive skills with reliable predictability beyond 2 months suggesting that the impact of the model error is less prominent than the initial error. Hence, substantial improvements in forecasts up to 3 months can be achieved with increased accuracy of initialization.

Short Circuit Failure Detection in Induction Motor Using Wavelet Transform and Fuzzy C-Means

Induction motors need to be monitored regularly because it involves the company's productivity. The induction motor monitoring method in this study uses a motor current variable which is transformed using the Discrete Wavelet Transform. Discrete Wavelet Transform (DWT) is used in this study because the results are satisfactory for detecting a short circuit in the stator winding of an induction motor. Of the many types and levels of discrete wavelet transforms, the haar wavelet transform at the third level is used in this study. Furthermore, the results of the discrete wavelet transform are processed using the Fuzzy C-means method. Fuzzy C-Mean (FCM) is the grouping approach that each part has a member degree of cluster according to the fuzzy logic algorithm. Motor modeling is shown in this article as normal condition, final fault current, and initial fault current. For this analysis, a combination of wavelet transform and Fuzzy C-means is used to classify motor currents into three motor states. The motor current is processed by Haar DWT level 3 to generate a high frequency signal. Then the high frequency signal is processed to get the energy signal. The energy signal is then fed to Fuzzy C-means to identify its condition. The results show that fuzzy C-means produces an error of 0% for the normal case, 33.3% for the initial error case and 0% for the final error case.

Covariance Matching Based Adaptive Attitude Estimation of a Nano- Satellite Using SVD-Aided EKF

Aims: Tuning the system noise covariance Q matrix for the single-frame method aided Kalman filtering algorithm. Background: The covariance matching procedure of the measurement noise covariance, namely the R matrix, was processed in singular value decomposition (SVD), which is one of the single-frame methods. Objective: Develop the R and Q double covariance matching rule for the single-frame method aided Kalman filtering algorithm. Method: The matching procedure of the measurement noise covariance, namely the R matrix, is processed in singular value decomposition (SVD), which is one of the single-frame methods. The second matching rule is defined in the second stage of the proposed EKF design. Result: The matching rules are run simultaneously, which makes the filter capable of being robust against initialization errors, system noise uncertainties, and measurement malfunctions at the same time without an external filter design necessity. Conclusion: A single-frame method aided Kalman filtering algorithm based double covariance matching rule is presented in this paper. First, the measurement noise covariance matching is introduced using the SVD method that processes the R-adaptation inherently for the filtering stage. Second, the system noise covariance matching is described so as to have double covariance matching at the same time during the estimation procedure. The SVD-Aided AEKF becomes R- and Q-adaptive simultaneously by applying the Q-adaptation rule to the intrinsically R-adaptive SVD-aided EKF.

Initial In-Flight Error Rates for 16-MB SRAM as Flying on the Double Asteroid Redirection Test (DART) Mission

Initial In-Flight Error Rates for 16-MB SRAM as Flying on the Double Asteroid Redirection Test (DART) Mission

Impacts of Mountain Topography and Background Flow Conditions on the Predictability of Thermally Induced Thunderstorms and the Associated Error Growth

Abstract Thermally induced thunderstorm simulations were conducted with the Weather Research and Forecasting (WRF) Model in an idealized configuration to investigate the associated error growth and predictability. We conducted identical twin experiments with different topography and background winds to assess the impacts of these factors. The results showed that mountain topography restrains error growth at the early stage of convection development. This topographic effect is sensitive to mountain geometry and background winds: it was more noticeable in cases with higher and narrower mountains and difficult to see without background wind. The topographic effect and its sensitivity resulted from the different natures of convection initiation. However, the topographic effect became less apparent when moist convection continued growing and triggered rapid error growth. The predictability of thunderstorms is then limited at the timing after the convective activity reached its maximum. A smaller initial error or starting a simulation at a later time did not break this timing of predictability limit. Mountain topography also limitedly affected the timing of the maximum convective activity and the predictability limit. In contrast, background flows changed the convection evolution and the following predictability. The predictability limit assessed by rainfall suggested other aspects of the topographic effect. The domain-scale rainfall distribution and the intense accumulated rainfall can be adequately captured in the presence of mountains.

An Improved In-Motion Coarse Alignment Method for SINS/GPS Integration with Initial Velocity Error Suppression.

The integrated system with the strapdown inertial navigation system (SINS) and the global positioning system (GPS) is the most popular navigation mode. It has been used in many navigation fields. Before the integrated system works properly, it must determine the initial attitude for SINS. In SINS/GPS-integrated systems, the navigational velocity can be used to carry out the initial alignment when the system is installed in the in-motion vehicle. However, the initial velocity errors are not considered in the current popular in-motion alignment methods for SINS/GPS integration. It is well-known that the initial velocity errors must exist when the initial velocity is obtained from the GPS outputs. In this paper, an improved method was proposed to solve this problem. By analyzing the original observation vectors in the in-motion coarse alignment method, an average operation was used to construct the intermediate vectors, and the new observation vector can be calculated by subtracting the intermediate vector from the original observation vector. Then, the initial velocity errors can be eliminated from the newly constructed observation vector. Thus, the interferences of the initial velocity errors for the initial alignment process can be suppressed. The simulation and field tests are designed to verify the performance of the proposed method. The tests results showed that the proposed method can obtain the higher accurate results than the current methods when the initial velocity is considered. Additionally, the results of the proposed method were similar to the current methods when the initial velocity errors were not considered. This shows that the initial velocity errors were eliminated effectively by the proposed method, and the alignment accuracy were not decreased.

Age-related changes to electroencephalographic markers of visuomotor error processing and learning in prism adaptation

Aging is associated with changes in cognitive function, including declines in learning, memory, and executive function. Prism adaptation (PA) is a useful paradigm to measure changes in explicit and implicit mechanisms of visuo-motor learning with age, but the neural correlates are not well understood. In the present study, we used PA to investigate visuo-motor learning and error processing in older adults. Twenty older adults (56–85 yrs) and 20 younger adults (18–33 yrs) underwent a goal-oriented reaching task while wearing prism goggles as continuous EEG was recorded to examine neural correlates of error detection. We examined behavioural measures of PA, as well as ERP components previously found associated with the early and late phases of adaptation to visual distortion caused by the prism goggles. Our results indicate important age-related behavioural and neurophysiological differences. Older adults reached more slowly than younger adults but showed the same accuracy throughout the prism exposure. Older adults also displayed larger aftereffects, indicating preserved visuomotor adaptation. EEG results indicated similar initial error processing in older and younger adults, as measured by the feedback error related negativity (FRN). As seen previously in young adults, the P3a and P3b declined over the prism exposure phase in both groups. Older adults displayed reduced P3a amplitude compared to the younger group in the early phase of adaptation, however, suggesting reduced attentional orienting. Finally, the older group exhibited a greater P3b amplitude compared to the younger group in the later phases of adaptation, potentially a marker of enhanced context updating underlying spatial realignment, leading to their larger aftereffect. Implications for age-related learning differences and clinical applications are discussed.

Traveling-wave vibrations of disc-drum rotors with PSC under mistuning-coupled conditions

This study emphasizes the considerations of both strain-dependent characteristics for partial coatings in the rotatory state and multiple mistuning-coupled conditions, both of which are difficult to find in reported studies. Due to the initial manufacturing error and the coupling loosening, the mistuning coupling does exist. It can induce local vibration exceeding standards, so the vibration-suppression demand is strong. The damping effect of strain-dependent coatings is significant, and compared with coatings on entire surfaces, partial coatings are easier to meet actual needs. Therefore, this study seems to have a certain significance and innovation. Firstly, the existing spring technology is improved to simulate connection-angle mistuning, connection-stiffness mistuning, and arc-length mistuning. Considering the centrifugal and gyroscopic effect of the disc and drum, the dynamic model of disc-drum rotors with partial strain-dependent coatings (PSC) under multiple mistuning-coupled conditions is created. Secondly, the multi-zone decomposing method is developed for presenting a united method of introducing the strain-dependent characteristics of partial coatings in the rotatory state, and the matching method of solving the nonlinear model is also provided. The validity of the model is verified. Finally, traveling-wave vibration analyses show that the Coriolis effect seriously affects the number of frequency-curve veering and the evolution of the vibration modes in veering regions. Moreover, the mistuning coupling induces the increase of frequency-curve veering, and also significantly influences vibration modes, towards of frequency curves, and response-distribution situations.

Instantaneous velocity determination and positioning using Doppler shift from a LEO constellation

To provide backup and supplementation for the Global Navigation Satellite System (GNSS), Doppler shift from Low Earth Orbit (LEO) satellites can be used as signals of opportunity to provide positioning, navigation, and timing service. In this contribution, we first investigate the model and performance of instantaneous velocity determination and positioning with LEO satellites. Given a LEO constellation with 288 satellites, we simulate Doppler shift observations at nine multi-GNSS experiment stations. Owing to the lower orbit, the performance of LEO velocity determination is much more sensitive to the initial receiver position error than that of GNSS. Statistical results show that with the initial receiver position error increased from 0.1 to 10 m, the Root Mean Square Errors (RMSEs) increase from 0.73 to 2.65 cm/s, 0.68 to 2.96 cm/s, and 1.67 to 4.15 cm/s in the east, north, and up directions, respectively. The performances with GPS are compared with GPS + LEO, and it is found that LEO Doppler shift observations contribute to GPS velocity determination. As for LEO Doppler positioning, even if more than 30 visible LEO satellites are available, the position dilution of precision values can reach several hundreds. Assuming that the error of LEO Doppler measurements is 0.01 m/s, the instantaneous Doppler positioning accuracy can achieve about a few meters, which is comparable to that of GNSS pseudorange positioning. A constant velocity model is adopted for state transition. Static LEO Doppler positioning results show that an accuracy at centimeter to decimeter level can be achieved after solution convergence. For a static simulated kinematic positioning test, the RMSEs range from a few decimeters to several meters in different regions by giving different constraints. For a dynamic positioning test, the RMSEs are about 2–3 m in high latitude region.

Chaotic van der Pol Oscillator Control Algorithm Comparison

The damped van der Pol oscillator is a chaotic non-linear system. Small perturbations in initial conditions may result in wildly different trajectories. Controlling, or forcing, the behavior of a van der Pol oscillator is difficult to achieve through traditional adaptive control methods. Connecting two van der Pol oscillators together where the output of one oscillator, the driver, drives the behavior of its partner, the responder, is a proven technique for controlling the van der Pol oscillator. Deterministic artificial intelligence is a feedforward and feedback control method that leverages the known physics of the van der Pol system to learn optimal system parameters for the forcing function. We assessed the performance of deterministic artificial intelligence employing three different online parameter estimation algorithms. Our evaluation criteria include mean absolute error between the target trajectory and the response oscillator trajectory over time. Two algorithms performed better than the benchmark with necessary discussion of the conditions under which they perform best. Recursive least squares with exponential forgetting had the lowest mean absolute error overall, with a 2.46% reduction in error compared to the baseline, feedforward without deterministic artificial intelligence. While least mean squares with normalized gradient adaptation had worse initial error in the first 10% of the simulation, after that point it exhibited consistently lower error. Over the last 90% of the simulation, deterministic artificial intelligence with least mean squares with normalized gradient adaptation achieved a 48.7% reduction in mean absolute error compared to baseline.