Inverse Compensation Research Articles

Memory reduction of rate-dependent Prandtl–Ishlinskii compensators in applications on high-precision motion systems

Inverse compensation of rate-dependent Prandtl–Ishlinskii operators with finitely many thresholds can be carried out explicitly under some structural conditions. However, if the number of thresholds is very high, the formulas become complicated. A simple and elegant framework for the investigation of such problems encompasses a recently proposed extension of the class of rate-dependent Prandtl–Ishlinskii operators to the case of a whole continuum of play operators with time-dependent thresholds. Indeed, such a theory allows for reducing the number of necessary thresholds in the compensation procedure and estimating the errors of the memory-discrete compensation in terms of the distance between the thresholds and weights of the individual plays. Following these results, our goal in this work is the validation of these theoretical models via numerical simulations and experimental results. In particular, we show that high accuracy of the hysteresis compensation algorithm can be achieved even with a relatively small number of thresholds.

Adaptive Inverse Compensation Fault-Tolerant Control for a Flexible Manipulator With Unknown Dead-Zone and Actuator Faults

This study presents an adaptive inverse compensation control of a flexible single-link manipulator with an unknown dead-zone and actuator faults. First, a dead-zone inverse model and a smooth inverse operator are constructed to address dead-zone nonlinearity. Second, an adaptive fault-tolerant control is utilized to compensate for the partial loss of the effectiveness of the actuator. Third, the coupling errors of the dead-zone and faults are effectively removed and resolved by introducing an estimate for unknown bounds. Then, the direct Lyapunov theory is used to guarantee uniformly ultimately bounded stability in the controlled system. Finally, the simulations and experiments demonstrate the efficiency of the presented method.

Dynamic modelling and inverse compensation for coupled hysteresis in pneumatic artificial muscle-actuated soft manipulator with variable stiffness

The soft manipulator actuated parallel with contractile and extensor pneumatic artificial muscles (PAMs) can perform continuous operations and vary stiffness. Due to the hysteresis nonlinearity of PAMs and parallel driving, the manipulator shows coupled hysteresis with stiffness-dependent and rate-dependent characteristics, causing inaccurate positioning performance. This paper firstly proposes a comprehensive dynamic model consisting of two parts in series to predict the manipulator dynamical behaviors with coupled hysteresis. Furthermore, a decoupled inverse compensation and decoupling control on the basis of the proposed model is conducted on the soft manipulator. Experimental results and comparisons illustrate the high precision and strong generalization ability of the proposed model and the validity of the compensator in mitigating the coupled dynamic hysteresis of the system.

Modeling and inverse compensation of dynamic hysteresis in voice coil motors using an extended rate-dependent Prandtl-Ishlinskii model

The objective of this study is to examine the influence of input current frequency on the hysteresis behaviour of voice coil motors (VCMs). The present study introduces the extended rate-dependent Prandtl-Ishlinskii (ERDPI) model, which integrates the Fermi-Dirac distribution into the envelope function. Additionally, the model combines rate-dependent thresholds and rate-dependent Prandtl-Ishlinskii compensation. The model parameters are identified using the improved grey wolf optimizer algorithm. The analysis of the model responses and observed data across various frequencies of current input signals demonstrates the ERDPI model's ability to accurately capture the rate-dependent nonlinear hysteresis features of the VCM. To compensate for dynamic hysteresis in VCMs, an ERDPI inverse model is established, showcasing unique convergence when the model parameters are assigned specific values. Simultaneously, an ERDPI inverse model-based feedforward compensator is presented, demonstrating a significant reduction in the hysteresis rate of VCMs by a factor of magnitude, ensuring its stabilization below 5%.

Adaptive iterative learning control and inverse compensation of macro fiber composite system with hysteresis

Macro fiber composite (MFC) actuators, which exhibit excellent flexibility and strong deformability, are widely used in aerospace exploration, flexible robotics, and other applications. However, the rate-dependent hysteresis nonlinearity of the flexible beam driven by MFCs significantly affects positioning accuracy. This study utilizes a new Hammerstein model to describe the hysteresis of an MFC system and proposes a control strategy that combines adaptive iterative learning control and inverse compensation. The adaptive iterative learning control allows the learning gain parameters to be adjusted based on the tracking error. A high gain ensures the convergence speed of iterative learning, whereas a small learning gain provides high robustness and convergence accuracy. Trajectory tracking experiments are conducted to verify the effectiveness of the proposed control strategy. Compared with the case of direct inverse compensation, after the sixth iteration, the tracking maximum absolute error, relative error, and corresponding root mean square error of the proposed control strategy reduce by 73% on average. Moreover, compared with the case of P-type iterative learning control combined with inverse compensation, the metrics above reduce by 12% on average. The proposed control strategy offers higher tracking accuracy and greater practicality in the trajectory tracking of MFC systems.

Adaptive neural approximated inverse control for photovoltaic power generation servo systems with all states constrained

For PV power generation servo systems with motor hysteresis, an all-state constrained decentralized adaptive approximation inversion control strategy is suggested in order to further increase the tracking precision of photovoltaic (PV) panels to the sun and the efficiency of solar energy utilization. Firstly, the hysteresis phenomenon in the servo motors of photovoltaic panels is considered, and the hysteresis-approximate inverse compensator is first studied in the servo motors of photovoltaic panels. Secondly, to deal with the constrained states such as rotor angle, angular velocity, and stator current in practical servo motors, and to ensure all states are strictly confined within each set of constraints with pre-set tracking performance, the asymmetric barrier Lyapunov functions and the error transformed functions are designed. Finally, to test the effectiveness of the proposed control strategy, a hardware-in-the-loop simulation platform and a photovoltaic servo system for PV power generation are built.

Finite-Time Adaptive Quantized Motion Control for Hysteretic Systems With Application to Piezoelectric-Driven Micropositioning Stage

This study addresses the finite-time adaptive output feedback quantized control problem for nonstrict-feedback nonlinear systems subject to unknown hysteresis and time delays. An estimated inverse compensator (EIC) is constructed to mitigate hysteresis by resorting to a generalized Duhem model. A Lyapunov–Krasovskii functional is employed to deal with the uncertainties of time delays. However, the system performance deteriorates when the quantized signal is directly applied to the hysteretic system. To overcome this obstacle, this study proposes a new composite quantizer consisting of an adaptive state-estimation filter and a modified hysteretic quantizer, in which the former facilitates the state approximation by incorporating the feedback information of the system and the latter regulates the communication rate. With the proposed finite-time prescribed adaptive quantized control with an estimated inverse compensator (EIC–FPAQC) scheme, all solutions of the closed-loop systems are semiglobal practical finite-time stable and the tracking error can be ensured in a predefined accuracy. Finally, experiments are conducted on a piezoelectric-driven micropositioning stage to demonstrate the effectiveness of the proposed method.

Modelling and model-based tracking control of soft twisted and coiled actuators

Soft twisted and coiled actuators (STCAs) have promising prospects for application in the field of soft-bodied robots. However, the STCA has a complex energy conversion mechanism and a rate-dependent hysteresis nonlinearity, which brings great difficulties to its modelling and control. To solve these difficulties, this paper proposes modelling and model-based tracking control methods to realize the high-precision output-force tracking control of the STCA. First, a dynamics model framework is constructed, which consists of a thermo-electric model and a thermo-mechanical model, to describe the current-to-force process of the STCA. Among which, the thermo-mechanical model is established by combining the Bouc–Wen model and the linear dynamic system in series, which can accurately describe the rate-dependent hysteresis nonlinearity of the STCA. Then, a double closed-loop control architecture with a hysteresis inverse compensation controller is proposed, which can realize the timely control of the temperature and the compensation for the rate-dependent hysteresis nonlinearity of the STCA. Next, controller parameters are adjusted in the simulation environment to reduce the risk of the damage of the STCA and to obtain a satisfactory control effect. Finally, practical experiments are conducted, and root mean square errors of all experiment results are all less than 5.01%, which verifies the effectiveness of the proposed modelling and control methods.

High-Performance Flux Tracking Controller for Reluctance Actuator

To meet the ever-increasing demand for next-generation lithography machines, the actuator plays an important role in the achievement of high acceleration of the wafer stage. However, the voice coil motor, which is widely used in high-precision positioning systems, is reaching its physical limits. To tackle this problem, a novel way to design the actuator using the magnetoresistance effect is argued due to the high force densities. However, the strong nonlinearity limits its application in the nan-positioning system. In particular, the hysteresis is coupled with eddy effects and displacement, which lead to a rate-dependent and displacement-dependent hysteresis effect in the reluctance actuator. In this paper, a Hammerstein structure is used to model the rate-dependent reluctance actuator. At the same time, the displacement-dependent of the model is regarded as the interference with the system. Additionally, a control strategy combining inverse model compensation and the disturbance observer-based discrete sliding mode control was proposed, which can effectively suppress the hysteresis effect. It is worthy pointing out that the nonlinear system is transformed into a linear system with inversion bias and disturbance by the inverse model compensation. What is more, the sliding mode controller based on the disturbance observer is designed to deal with the unmodeled dynamics, displacement disturbances, and model identification errors in linear systems. Thus, the tracking performance and robustness to external disturbances of the system are improved. The simulation results show that it is superior to the PI controller combined with an inverse compensator and even to the discrete sliding mode controller connected with inverse compensator, confirming the effectiveness of the novel control method in alleviating hysteresis.

Deterministic Learning From Adaptive Neural Network Control for a 2-DOF Helicopter System With Unknown Backlash and Model Uncertainty

In this study, we develop a deterministic learning control approach using adaptive neural network (NN) for a two-degrees-of-freedom helicopter nonlinear system subject to unknown backlash and model uncertainty. First, by combining the backstepping and direct Lyapunov approaches, a novel adaptive NN control scheme with an inverse compensation method is proposed to address the input backlash nonlinearity, track specified trajectories, and stabilize the closed-loop system. Simultaneously, uncertain system dynamics are accurately identified and stored as learned knowledge in constant radial basis function NN weights, while satisfying partial persistent excitation. Subsequently, by extracting the learned knowledge, a learning-based controller is constructed to operate the same control tasks to achieve a superior control performance, less backlash nonlinearity, and minimal computational burden. Finally, the validity and efficacy of the proposed scheme are demonstrated through numerical examples and experiments.

Adaptive approximated inverse control for a class of multi-agent systems with unknown control directions

An adaptive approximated inverse control scheme for a class of high-order nonlinear multi-agent systems with unknown control directions is proposed in this paper. The adaptive control problem under the case it is not required to be identical for the unknown control directions is solved by some novel Nussbaum functions and a monotonously increasing sequence, leading our multiple Nussbaum functions reinforce rather than counteract each other. Also, it is introduced in the design and analysis like some integrable auxiliary signals and a novel contradiction argument. Moreover, hysteresis nonlinearity is counteracted by constructing its approximated inverse compensator. With these efforts, stability analysis ensures that all the closed-loop system signals are uniformly bounded, and it is successfully achieved the asymptotic convergence of tracking error to zero, circumventing the obstacle caused by the unknown control directions. Experiment results illustrate the effectiveness of the proposed scheme.

A novel control strategy for reproducing the floor motions of high-rise buildings by earthquake-simulating shake tables

To enable the experimental assessment of the seismic performance of full-scale nonstructural elements with multiple engineering parameters (EDPs), a three-layer testbed named Nonstructural Element Simulator on Shake Table (NEST) has been developed. The testbed consists of three consecutive floors of steel structure. The bottom two floors provide a space to accommodate a full-scale room. To fully explore the flexibility of NEST, we propose a novel control strategy to generate the required shake table input time histories for the testbed to track the target floor motions of the buildings of interest with high accuracy. The control strategy contains two parts: an inverse dynamic compensation via simulation of feedback control systems (IDCS) algorithm and an offline iteration procedure based on a refined nonlinear numerical model of the testbed. The key aspects of the control strategy were introduced in this paper. Experimental tests were conducted to simulate the seismic responses of a full-scale office room on the 21st floor of a 42-story high-rise building. The test results show that the proposed control strategy can reproduce the target floor motions of the building of interest with less than 20% errors within the specified frequency range.

Adaptive attitude control of UAV based on neural network compensation

Aiming at the problem of unmanned aerial vehicle (UAV) attitude control with insufficient robustness and anti-disturbance, this paper proposes a UAV attitude control method combining incremental nonlinear dynamic inverse (INDI) and neural network (NN) adaptive compensation. Firstly, the attitude model of UAV with time scale separation is proposed. On this basis, the INDI control law is derived, and the angular acceleration information required by INDI method is feedback by the filter. Then, the adaptive NN compensation structure is introduced to eliminate the uncertainty caused by model mismatch, sensor delay and external disturbance. Finally, the UAV simulation is carried out. The result shows that this method improves robustness and anti-disturbance of original INDI method without changing structure and parameters of controller, and has a certain adaptive compensation ability for system uncertainty.

Tracking control of a piezo-actuated compliant mechanism based on an improved Bouc–Wen hysteresis model with variable parameters

To deal with the challenges that the classical Bouc–Wen model fails to precisely characterize amplitude-dependent hysteresis and asymmetric hysteresis, an improved Bouc–Wen model with variable parameters is presented. The proposed model introduces asymmetric terms and parameter functions related to sinusoidal excitation amplitudes into the classical Bouc–Wen model. It has a relatively simple mathematic form and can be easily identified and applied for inverse feedforward compensation in real-time applications. By comparison with the classical Bouc–Wen model and other existing hysteresis models, the superiority of the proposed model has been verified. Furthermore, inverse hysteresis control and hybrid control combining the developed inverse control and proportional-integral feedback control are proposed. Several comparative experiments are conducted on a piezo-actuated micro-scanner. Results demonstrate that inverse control and hybrid control using the improved Bouc–Wen model with variable parameters can achieve better tracking performance and are meaningful in actual trajectory-tracking applications.

Distributed Robust Platooning Control for Heterogeneous Vehicle Group under Parametric Uncertainty and Hybrid Attacks

This study addresses the problem of distributed robust control resistance to vehicle parametric uncertainty and hybrid attacks in heterogeneous vehicular platoon systems. First, an invaded heterogeneous vehicular platoon system is modeled accompanied by the technique of inverse model compensation to deal with the problem of non-linearities in longitudinal dynamics, a nominal vehicle mass is introduced to tackle the problem of heterogeneity and variability of vehicle masses. Besides, we quantize the effects of false-data injection (FDI) and message-delay (MD) attacks and integrate them into the proposed heterogeneous platoon system. Additionally, to obtain the desired inner-vehicle stability of a heterogeneous platoon, an inner-vehicle <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${H}_\infty $</tex-math></inline-formula> distributed robust stable controller is presented based on Lyapunov-Krasovskii functional. Finally, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathcal{L}}_2$</tex-math></inline-formula> -based string stability criterion is put forward to weaken the attacks' effects when they propagate along with the platoon. To explain the superiority of the derived theory more directly, simulations with two different control methods about the heterogeneous platoon under hybrid attacks are provided. The results show that, compared to regular platoon control method, the proposed robust controller performs better in achieving the desired inter-vehicle spacing tracking and can maintain string stability of the platoon.

Safety-Critical Control Systems [About this Issue

This issue of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Control Systems</i> includes one feature and one lecture note. In the feature, “Runtime Assurance for Safety-Critical Systems: An Introduction to Safety Filtering Approaches for Complex Control Systems” <xref ref-type="list-item" rid="list-itemA1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[A1]</xref> , authors Kerianne L. Hobbs, Mark L. Mote, Matthew C.L. Abate, Samuel D. Coogan, and Eric M. Feron provide an introductory tutorial of runtime assurance (RTA) concepts and their role in ensuring safety of complex control systems. Assuming an undergraduate-level understanding of control theory and state-space concepts, this article provides theory and examples for RTA architectures, four types of RTA approaches, and a number of systems engineering and practical considerations for employing RTA in safety-critical, cyberphysical systems. In the “Lecture Notes” column, “The Prandtl-Ishlinskii Hysteresis Model: Fundamentals of the Model and Its Inverse Compensator,” <xref ref-type="list-item" rid="list-itemA2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[A2]</xref> Mohammad Al Janaideh, Mohammad Al Saaideh, and Xiaobo Tan introduce the mathematical formulations of the Prandtl–Ishlinskii (PI) and Generalized PI (GPI) hysteresis models as well as their analytical inverses. These models can effectively characterize hysteresis nonlinearities in various systems, especially smart-material actuators, and their inverses can be used in feedforward compensation to mitigate the effect of hysteresis. Numerical examples of the PI and GPI models as well as their analytical inverses are also included in the article. The intended audience includes graduate students and researchers from academia and industry interested in hysteresis modeling and compensation.

Data‐based learning control for optimization of nonlinear systems

Data‐based learning control for optimization of nonlinear systems

Design of model-based control strategies for a novel MISO PEM fuel cell control structure.

Voltage control is critical for the performance of proton exchange membrane fuel cells. However, accurately controlling voltage is challenging, specifically during the current variation. The present study proposes a novel multiple-input single-output (MISO) control structure for a proton exchange membrane fuel cell system to improve performance. Also, this study focuses on airflow optimization and hydrogen consumption optimization, as the literature focuses more on airflow optimization for compressor or pump performance. Firstly, using a genetic algorithm optimization technique, the fractional order model is realized from the existing integer order fuel cell model. Then, the proposed control structure aims to control the output cell voltage by regulating the air and hydrogen inlet rates by designing various model-based controllers for integer and fractional order models. The control performance is evaluated for set point tracking, disturbance rejection, inverse response rejection and time delay compensation. The simulation results show that the fractional order system is observed to give better results than the integer order system, with model predictive controller showing the best results for controlling the stack voltage.

Research on the identification and distribution of biofouling using underwater cleaning robot based on deep learning

The rapid identification of biofouling is of great significance to the intelligent operation and maintenance of large ships or other deep-sea and offshore projects. The biofouling's optical imaging properties and its dispersion and concealment bring challenges to the correct segmentation of its images. In this article, a filter-guided inverse dark channel inversion exposure compensation (FIDCE) algorithm is proposed for the ambiguous image by exploring an underwater optical imaging model partially over-exposed and partially under-exposed. Also, MFONet, a pixel-level segmentation model, is proposed. Its backbone feature extraction network includes MobileNetV2—a lightweight CNN model with the Sandglass block as the basic unit, and combines with ASPP enhanced feature extraction module, thereby enhancing the network perception field. In this model, SENet is also used to enhance the deep and shallow feature fusion. In addition, the underwater image evaluation functions are compared to verify the validity of the image processing algorithm mentioned above. According to the results, a more accurate and quicker effect can be observed in the fine-grained identification of biofouling using MFONet. Nowadays, such an algorithm can be used for a priori study of biofouling and the cleaning operation of the robot.

A Comparative Study between NMPC and Baseline Feedback Controllers for UAV Trajectory Tracking

Transport, rescue, search, surveillance, and disaster relief tasks are some applications that can be developed with unmanned aerial vehicles (UAVs), where accurate trajectory tracking is a crucial property to operate in a cluttered environment or under uncertainties. However, this is challenging due to high nonlinear dynamics, system constraints, and uncertainties presented in cluttered environments. Hence, uncertainties in the form of unmodeled dynamics, aerodynamic effects, and external disturbances such as wind can produce unstable feedback control schemes, introducing significant positional tracking errors. This work presents a detailed comparative study between controllers such as nonlinear model predictive control (NMPC) and non-predictive baseline feedback controllers, with particular attention to tracking accuracy and computational efficiency. The development of the non-predictive feedback controller schemes was divided into inverse differential kinematics and inverse dynamic compensation of the aerial vehicle. The design of the two controllers uses the mathematical model of UAV and nonlinear control theory, guaranteeing a low computational cost and an asymptotically stable algorithm. The NMPC formulation was developed considering system constraints, where the simplified dynamic model was included; additionally, the boundaries in control actions and a candidate Lyapunov function guarantees the stability of the control structure. Finally, this work uses the commercial simulator DJI brand and DJI Matrice 100 UAV in real-world experiments, where the NMPC shows a reduction in tracking error, indicating the advantages of this formulation.