Nonlinear Compensation Research Articles

Nonlinear Friction Compensation for PMLSM Using an Enhanced Adaptive Friction Observer

Nonlinear Friction Compensation for PMLSM Using an Enhanced Adaptive Friction Observer

Low-Complexity Fiber Nonlinearity Compensation Based on Operator Learning Over 12 075 km Single Model Fiber

Low-Complexity Fiber Nonlinearity Compensation Based on Operator Learning Over 12 075 km Single Model Fiber

Thermal Monitoring of Induction Motors With DC Injection Considering Inverter Nonlinearity Compensation Error

Thermal Monitoring of Induction Motors With DC Injection Considering Inverter Nonlinearity Compensation Error

An Innovative Nonlinear Bounded Component Analysis Algorithm Based on Multivariate Nonlinear Chirp Mode Decomposition

In complex and diverse practical application scenarios, the challenge of blind source separation under underdetermined and nonlinear conditions is often encountered. To address this challenge, this paper proposes an innovative underdetermined nonlinear bounded component analysis method. This method first employs Multivariate Nonlinear Chirp Mode Decomposition (MNCMD) to process and reconstruct the observed signals, transforming the original underdetermined problem into a positive definite problem. Subsequently, Gaussianization techniques are introduced as a means of nonlinear compensation, successfully converting the nonlinear model into an analyzable linear model, laying a solid foundation for subsequent signal separation. Finally, the signal is separated by the bounded component analysis method, which does not require the source signals to be independent of each other. To validate the effectiveness and superiority of the proposed algorithm, detailed simulation experiments were designed and implemented. The experimental results demonstrate that compared to traditional underdetermined blind source separation algorithms, the algorithm presented in this paper exhibits significant advantages in terms of universality, convergence speed, separation accuracy, and robustness. Furthermore, this paper successfully applies the algorithm to the blind extraction of fetal electrocardiogram (FECG) signals from real datasets. The experimental results show that the algorithm can rapidly and effectively extract clearer and more accurate FECG signals, demonstrating its great potential and value in practical applications.

Determination of the kinetic parameters of complex condensed phase reactions using a nonlinear model

This research introduces a novel nonlinear model that can be used in the modified Friedman method to determine activation energy via linear regression with a trial-and-error approach. Its application in a simulated reaction enhanced precision in activation energy determination and accurately captured the trend in activation energy variation with temperature compared to quadratic regression. The nonlinear model can also predict nonlinearity in compensation effect relationships, aiding in the assessment of pre-exponential factor, rate constant, and conversion function in complex reactions, ensuring more precise results compared to linear model. By incorporating random noise into the simulated reaction’s isoconversional kinetic data, which follows a normal distribution, we observed that both nonlinear and quadratic regressions produce comparable activation energy values. However, the nonlinear compensation effect approach yields more precise results for the pre-exponential factor, rate constant, and conversion function compared to the linear model in the presence of random errors. The novel approach based on the nonlinear model was applied, in conjunction with the linear and quadratic Friedman methods featuring linear compensation effect, to compute the kinetic parameters of a polymer coating on a commercial optical fiber as well as a polyethylene. Gnu Octave/MATLAB codes were also made available for estimating the kinetic parameters of complex reactions exhibiting a single peak in reaction rate curves using the linear, quadratic, and nonlinear Friedman methods, along with the application of linear and nonlinear compensation effects. These codes can be customized by users to estimate the kinetic parameters of their own kinetic data.

Origin and Compensation of Nonlinearity in Near-Navigation Grade Piezoresistive MEMS Gyroscope

Origin and Compensation of Nonlinearity in Near-Navigation Grade Piezoresistive MEMS Gyroscope

Research of neural network-based model for nonlinear temperature drift compensation of MEMS accelerometers.

Downhole instrumentation requires more and more accuracy of MEMS inertial sensors. However, in measurement while drilling, temperature drift phenomenon of the sensor will have a cumulative impact on the drill pipe attitude solution. After experimental testing, the output response of the accelerometer had strong local linear and global nonlinear characteristics. In this paper, we proposed a temperature compensation model based on tent chaotic mapping and sparrow search algorithm optimized back propagation (BP) neural network (Tent-SSA-BPNN). Sparrow search algorithm (SSA) was optimized by tent chaotic mapping, which was utilized to improve the uniformity and search ability of SSA populations. Then, the improved SSA was used to optimize the weight and bias parameters of the BP neural network for constructing the temperature compensation model. Finally, the trained compensation model is integrated into the microprogram control unit for real-time compensation testing. The experimental results show that after sacrificing a small amount of sampling frequency, the compensation model proposed in this article has good global compensation performance, and the mean absolute percentage error is reduced from 2% to 0.2% compared to the original output. The mean absolute error and root mean square error of the improved compensation model are all reduced compared to the pre-improved BP compensation model. This temperature-compensated modeling method has a reference value for low-cost and high-precision modeling in high temperature environments, while greatly saving time cost and measurement costs.

A Nonlinear Phase Compensation Method for FMCW Radar Based on VNCMD

A Nonlinear Phase Compensation Method for FMCW Radar Based on VNCMD

Performance Analysis of a 28 GHz Wideband Analog Radio-Over-Fiber Fronthaul With Channel Nonlinearity Compensation

Performance Analysis of a 28 GHz Wideband Analog Radio-Over-Fiber Fronthaul With Channel Nonlinearity Compensation

Hysteresis Compensation and Trajectory Tracking Control Model for Pneumatic Artificial Muscles

The optimum performance position control of pneumatic artificial muscles (PAM) is restricted by their in-built hysteresis and nonlinearity. The hysteresis is usually depicted by a phenomenological model, while the model mentioned above always only describes the hysteresis phenomenon under certain conditions. Thus, the universality of the compensator is due to its weakness in handling disparate outside conditions. Our research employs the FN–QUPI (feedforward neural network–quadratic unparallel Prandtl–Ishlinskii) model to depict the phenomenon of pressure-displacement hysteresis in PAMs. This model has high-precision expression and generalization ability for the PAM hysteresis phenomenon. According to this, an inverse model of the QUPI operator is established as a feedforward control while combining with the feedback control of incremental PID-type iterative learning. The results show that due to the hysteresis of PAM, the compound control of feedforward control and iterative learning has better tracking performance than the ordinary PID compound control in terms of convergence rate and stability. According to the mean absolute error (MAE) and root mean square error (RMSE) of the tracking process, it can be seen that the control model can achieve accurate nonlinear compensation, and the control system shows excellent robustness to different input signals.

A Composite Control Method Based on Model Predictive Control and a Disturbance Observer for the Acquisition, Tracking, and Pointing System

Nonlinear disturbances, such as friction, backlash, and base vibration, are the main factors restricting the further improvement of the dynamic performance of the Acquisition, Tracking and Pointing (ATP) system. However, the modeling and compensation of the time-varying nonlinearities are still challenging. In this paper, a compound control algorithm based on model predictive control (MPC) and a Proportional Multiple-Integral State-Augmented Kalman Filter (PMISAKF) disturbance observer is proposed to improve the disturbance rejection performance. Specifically, a PMISAKF is used to estimate the system’s state and external disturbances in real-time. These observed disturbances are then treated as known external inputs at the current or future time points and incorporated into the predictive model of MPC. This allows the optimization process to take these external factors into consideration, enabling the calculation of an optimal control strategy and achieving high pointing and tracking accuracy for nonlinear time-varying disturbances. Experimental results show that the proposed method can effectively improve the system’s anti-interference ability and tracking accuracy.

Simulation of Motion Nonlinear Error Compensation of CNC Machine Tools With Multi-Axis Linkage

In order to solve the problem of nonlinear error for a dual rotary table five-axis CNC machine tool due to the linkage of rotary and translational axes, the simulation of motion nonlinear error compensation for a multi-axis linkage CNC machine tool is proposed. The adjacent points in the tool position file are selected as the tool position points for building the model, and then the nonlinear error model resolved by the harmonic function is established according to the error distribution in the classical post-processing. The nonlinear error between the two tool position points is quickly predicted by the analytical expression of this model, and the real-time error compensation of the intermediate interpolation points is realized. Finally, MALTLAB simulation analysis is performed on the tool position file of an impeller part machining to verify the effectiveness of the proposed algorithm. The experimental results show that it can be seen from the distribution curve of the nonlinear error that it is about 10% after compensation as before compensation, thus verifying the effectiveness of the nonlinear error compensation mechanism. The correctness of the nonlinear error analysis and compensation method and the effectiveness of post-processing are verified.

A nonlinear hysteresis compensation control of hydro-viscous drive in air transport operations

The Hydro-Viscous Drive (HVD) speed regulating system finds extensive application in air transport transmission systems to regulate the stepless speed or conduct overload protection. However, its intrinsic hysteretic behaviors, such as the asymmetric hysteretic and dead zone, could introduce inaccuracy and delay in control applications, posing challenges to system regulation. This paper investigates a Nonlinear Hysteresis Compensation Control (NHCC) that consists of two parts to control the HVD output speed by operating the valve under different engine operating conditions. In the first part, the Inverse Hysteresis Compensator (IHC) based on major loop data is introduced for the asymmetric hysteresis characterization and compensation of the HVD speed control system of the power generation and distribution, which aims to reduce the hysteresis and dead zone effect and expand the effective input range. In the second part, the Active Disturbance Rejection Controller (ADRC) is employed to mitigate the hysteresis effects of the compensated system and remove the steady-state error, which allows real-time compensation of the estimated perturbations as state feedback to achieve the required performance. An experimental laboratory station has been fabricated to evaluate the proposed method. The test results show that the NHCC method can regulate the fan speed to the desired value (|e|< 45 r/min at steady state) and broaden the effective input range to the full range under different engine conditions. Besides, the proposed control method can reduce the non-linearity of the input and output curves (from 18% to 4%) and compensate for the asymmetric hysteresis (from 38% to 5%).

Compensation of system nonlinearity in the measurement of acoustic nonlinearity parameters

The acoustic nonlinearity parameter is determined from the amplitudes of the fundamental and second harmonic component of the acoustic wave propagating through the material. However, the generally used through-transmission based measurements with PZT transducers contain high system nonlinearity, so that the received second harmonic component includes an extra component caused by the transducer, which significantly decrease the reliability of measurements. In this study, we proposed a novel method to reduce the system nonlinearity in the conventional through-transmission based measurements by adding a simple process in which the transmitting and receiving transducers are in direct contact without a specimen. The proposed method was experimentally validated for two materials, Al6061-T6 and Fused Silica. For both materials, several specimens with different thickness were prepared, and the measurement results showed that the magnitude of the second harmonic was proportional to the thickness, but there was an offset due to system nonlinearity. On the other hand, after applying the proposed technique, the offsets were greatly reduced, and furthermore, this performance was maintained even when the transducer setup was changed, and the ratio of nonlinearity parameters measured for the two materials was in good agreement with the known literature value.

Inversion-based fuzzy adaptive control with prespecifiable tracking accuracy for uncertain hysteretic systems

Inversion-based control strategies have the outstanding effectiveness in the compensation for hysteresis nonlinearities. However, when the plant is driven by smart material-based actuator with the information absence of hysteresis output, there still exist technical gaps in the construction of hysteresis inverse controller. Therefore, this study aims to address this gap by proposing a novel control scheme. Technically, a novel hysteresis inverse algorithm, significantly reducing computational burden, has been proposed based on a novel adaptive estimation method for the unknown parameter (density function) in the Preisach-typed hysteresis nonlinearity. Furthermore, the inverse compensation error, the nonlinearities and uncertainties appeared in the controlled plant are accommodated by the innovative adaptive fuzzy feedback controller. With our scheme, it can ensure that the closed-loop stability, predefined steady-stated tracking performance and the convergence of algorithms including inverse algorithm and adaptive updating laws. In addition to theoretical analysis, we have validated the effectiveness of the proposed control scheme through simulation comparisons and experimental results conducted by the real-life piezoelectric actuator.

A Study of Friction Nonlinearity and Compensation for Turntable Servo Systems

In view of the worse dynamic performance and steady-state accuracy caused by nonlinear friction in turntable servo systems, challenges are posed in precise positioning tasks. However, most of the existing research ignores the effect of friction on system performance. Therefore, it is of great significance to analyze the nonlinear characteristics of the transmission mechanism and study compensation strategies for improving the control quality of non-direct drive turntable servo systems. Therefore, an improved active disturbance rejection control (ADRC) based on state feedback compensation is proposed in this paper to optimize the accuracy of the turntable servo system and improve the robustness of the system under nonlinear friction conditions. Firstly, friction is modeled and analyzed through offline identification, which is the basis for nonlinear friction compensation. Subsequently, the two methods of friction compensation are compared. Since feedforward compensation is prone to under-compensation and over-compensation, it is highly dependent on the parameters, while the traditional ADRC compensation method has poor dynamic performance under gap conditions. Therefore, the advantages of ADRC and state feedback are combined together to reduce the steady-state error and optimize the control performance of the system. Lastly, the effectiveness of the proposed compensation method is verified and compared through simulations and experiments. The method is able to comprehensively compensate the gap and friction nonlinearities, and the experimental steady-state error is reduced from 0.55° to 1/3 (0.19°), which improves the load-side positioning accuracy. Finally, a conclusion can be drawn that the new compensation method can improve the parameter adjustability, speed estimation precision, and system robustness.

Self-Commissioning of Synchronous Reluctance Motor at Standstill With Inverter Nonlinearity and Resistance Error Compensation

Self-Commissioning of Synchronous Reluctance Motor at Standstill With Inverter Nonlinearity and Resistance Error Compensation

Unseeded one-third harmonic generation in optical fibers

We propose a new concept to generate efficient one-third harmonic light from an unseeded third harmonic process in optical fibers. Our concept is based on the dynamic constant (Hamiltonian) of the nonlinear third harmonic generation in optical fibers and includes a periodic array of nonlinear fibers and phase compensation elements. We test our concept with a simulation of the nonlinear interaction between the fundamental and third harmonic modes of a realistic optical fiber, demonstrating high-efficiency one-third harmonic generation. Our work opens a new approach to achieving the so far elusive one-third harmonic generation in optical fibers.

Identification of nonlinear system model and inverse model based on conditional invertible neural network

In applications such as adaptive inverse control, internal model control, and active noise control, the identification accuracy of the system model and the inverse model directly affects the performance. However, it is not easy to identify inverse models for nonlinear systems. Moreover, existing methods require two identification calculations to obtain the system model and the inverse model. Therefore, an identification method of nonlinear system model and inverse model based on conditional invertible neural network (cINN) is proposed. The invertible structure of cINN enables simultaneous approximation of complex nonlinear functions and simultaneous acquisition of the corresponding inverse functions. Consequently, both the nonlinear system model and the inverse model can be identified concurrently through cINN. Moreover, the identification performance of the cINN-based method is validated and applied to disturbance cancellation in a classical nonlinear simulation system. Finally, the inverse model of the actuator is identified by cINN, and the inverse model is applied to the nonlinear compensation of the actuator.

Vibration suppression of series elastic actuator used for robotic grinding based on reconstructed hybrid optimized input shaper

Vibration suppression of the series elastic actuator (SEA) used for robotic compliant grinding is significant for guaranteeing force control accuracy and stability, which is important for material removal accuracy and surface quality improvement. This paper establishes a multi-feedforward control system to achieve vibration suppression for the SEA. The hybrid optimized input shaper (HOIS), instruction reconstructor, and nonlinear friction compensation model are seamlessly integrated into the multi-feedforward control method. Combination optimization principles are employed to formulate the HOIS, ensuring selectable performances on vibration suppression, response speed, and model stability when addressing the residual vibration caused by elastic elements. Base on the inherent delay characteristics of the shaper, a head/tail compression and intermediate shift instruction reconstruction (CSIR) method is introduced to address the tracking error induced by the time-delay of the shaper. Additionally, the friction compensator is established based on the Stribeck model and integrated into the feedforward control system to mitigate the vibration caused by the low-speed viscous friction. The effectiveness of the proposed method is verified by experimental tests. Results show that the HOIS has rich optional performance and can effectively suppress vibration. The tracking error caused by the shaper is effectively mitigated through the reconstruction of instructions. Grinding tests on curved parts are conducted on the robotic grinding system with SEA used as the force-control system. Compared with the benchmark method, the developed reconstructed hybrid optimized input shaper reduces the average force tracking error, the average absolute grinding depth error, and average roughness by 26.6 %, 22.5 %, and 21.5 % respectively. At the natural frequency of the grinding system, the amplitude of the frequency spectrum with the developed multi-feedforward method decreases by 48.13 %.