Feedforward Compensation Research Articles

Research on anode pressure control and dynamic performance of proton-exchange membrane fuel cell system for vehicular application

Reasonable anode pressure supply can not only improve the performance output of proton-exchange membrane fuel cell (PEMFC), but also effectively protect the proton-exchange membrane (PEM). In this article, a practicable feedforward-based proportional-integrative (FFPI) anode pressure control strategy is designed and successfully applied to a 100 kW-class automotive PEMFC system, where the current and anode purging are considered as interferences to apply feedforward compensation. The fuel cell system test bench exhibits good dynamic performance in load change experiments, the cell voltage consistency, power generation efficiency and hydrogen utilization of the system remain at a high level. To further suppress interference and reduce overshoot, oscillation and pressure response hysteresis, a control-oriented model of this 100 kW PEMFC system is constructed, and a novel second-order fuzzy active disturbance rejection controller (SOFADRC) is designed. Simulation results indicate that compared to FFPI controller and second-order ADRC (SOADRC), the maximum absolute error (MAE), average absolute error (AAE), and root-mean-square error (RMSE) of SOFADRC all reach the minimum values respectively under the step-load and China light-duty vehicle test cycle (CLTC) operating conditions, which are 6.51 kPa, 0.09 kPa, 0.42 kPa and 8.95 kPa, 0.62 kPa, 1.35 kPa respectively, showing the outstanding control effect.

Optimized Trajectory Tracking for Robot Manipulators with Uncertain Dynamics: A Composite Position Predictive Control Approach

This study addresses the trajectory tracking control challenges of robot manipulators with uncertain dynamics. The aim is to achieve precise and smooth trajectory regulation through a novel composite position predictive control (PPC) scheme that integrates motion profile and disturbance preview techniques. First, we perform offline dynamics identification and feedforward compensation alongside a pre-defined motion profile. To handle the disturbances arising from uncertain dynamics, a super-twisting disturbance observer is designed, resulting in a dynamically compensated prediction model. Furthermore, the receding optimization operations for PPC are executed by solving an optimal solution associated with a joint angle tracking error. The combination of feedforward and feedback control improves the robot manipulator’s absolute positioning accuracy as opposed to the conventional model predictive control method, especially when dealing with uncertain dynamics. The effectiveness of the proposed control method is confirmed through trajectory tracking experiments conducted on a six-degree-of-freedom robot platform with varying end-effector loads. The experimental results demonstrate that the proposed PPC method enhances tracking accuracy by approximately 45% and 25% when compared to the traditional inverse dynamic control (IDC) and the robust IDC approaches, respectively.

Simple adaptive wing-aileron flutter suppression system

In this work a simple adaptive flutter suppression system is designed in order to increase the operative speed range of a wing-aileron aeroelastic plant. The equivalent beam finite element based aeroelastic modeling of a wing equipped with a trailing edge aileron in incompressible flow is presented and its state space representation is provided. The almost strictly passivity of the plant is assured by means of a parallel feed-forward compensator implementation and the invariant controller parameters are tuned using a population decline swarm optimization algorithm that minimize the integral of the time absolute error on the wing tip torsion angle. The performance of the closed loop system are studied carrying out numerical simulations that reproduce different flight scenarios with the presence of speed variations, disturbances, and gusts proving the effectiveness of the proposed simple adaptive flutter suppression architecture.

Battery Energy Storage System Damper Design for a Microgrid with Wind Generators Participating in Frequency Regulation

Ancillary frequency control schemes (e.g., droop control) are used in wind farms to improve frequency regulation in grids with substantial renewable energy penetration; however, droop controllers can have negative impacts on the damping of wind turbine torsional mode, thereby reducing the lifespan of the turbine gearbox. This paper presents a battery energy storage system (BESS) damper to improve the damping of torsional vibrations when using doubly fed induction generators (DFIGs) for frequency regulation in a microgrid. We formulated a linearized model comprising diesel generators, a wind turbine with five-mass drivetrain, and BESS. We also designed a feedforward compensator to deal with phase lag between the BESS damper signal and DFIG torque. The proposed BESS damper was shown to improve the torsional mode damping by moving the eigenvalues for torsional mode leftward to desirable locations on the complex plane. Dynamic simulations performed using MATLAB/SIMULINK demonstrated the efficacy of the proposed BESS torsional mode damping scheme in terms of torsional mode 1 damping performance and frequency response.

Fast terminal sliding mode control of agricultural robots with permanent magnet synchronous motor servo systems based on an extended state observer for path tracking

In response to the challenges in mobile robot path tracking using model predictive control, where the predictive model weakens the controller’s ability to respond to sudden changes in the reference path curvature and heading, this paper proposes a composite control strategy suitable for agricultural robots. The strategy combines the maximum torque per ampere control and an Extended State Observer (ESO). The paper initially establishes a mathematical model for a Permanent Magnet Synchronous Motor (PMSM) considering aggregated disturbances. It designs a position tracking controller based on a non-singular terminal sliding mode and convergence law. This controller, employing a non-cascaded structure, replaces traditional position and velocity loop controllers and is proven to be stable with finite-time convergence through Lyapunov’s theorem. To enhance the system’s disturbance rejection capabilities further, the paper introduces an ESO to estimate system disturbances and applies it for feedforward compensation. The paper concludes by providing stability proof for the overall PMSM servo system in agricultural robots. Finally, the paper conducts simulations and experimental verifications based on the designed controller, demonstrating that the controller exhibits excellent path tracking performance, fast convergence, and robustness against external disturbances.

Two Cascade Control Strategy of Generalized Electric Spring

With the gradually increase of the application of new energy in microgrids, Electric Spring (ES), as a new type of distributed compensation power electronic device has been widely studied. The Generalized Electric Spring (G-ES) is an improved topology, and the space limitation problem in the traditional topology is solved. Because of the mode of G-ES use in the power grid, a reasonable solution to the voltage loss of the critical section feeder is needed. In this paper, the voltage balance equation based on the feedforward compensation coefficient is established, and a two cascade control strategy based on the equation is studied. The first stage of the two cascade control strategy is to use communication means to realize the allocation of feedforward compensation coefficients, and the second stage is to use the coefficients to realize feedforward fixed angle control. Simulation analysis shows that the proposed control strategy does not affect the control accuracy of the critical load (CL), and effectively improves the operational range of the G-ES.

A 10-Gb/s low-power inverter-based optical receiver front-end in 0.13-[formula omitted] CMOS process

In the design of CMOS optical receivers, it is challenging to compromise the bandwidth, noise, and gain of the transimpedance amplifier (TIA). The inverter-based cascaded structure is often used in TIA designs. However, the traditional 3-stage TIA needs bandwidth expansion techniques to get good performance. In the designed front-end, we propose a 5-stage cascaded TIA that achieves performance improvement without using bandwidth expansion technique. Besides, the stability of the main amplifier (MA) is improved by adopting a feed-forward compensation technique. Designed in a 0.13-μm CMOS process, the proposed front-end exhibits a bandwidth of 8.9 GHz, a transimpedance gain of 123.5 dB Ω and an average input-referred noise of 12.2 pA/Hz. The whole circuit consumed 19.1 mW from mixed input of 1 V and 1.5 V supply voltages.

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%.

Optimal tracking control for unknown nonlinear systems with uncertain input saturation: A dynamic event-triggered ADP algorithm

This paper is devoted to proposing a novel dynamic event-triggered adaptive dynamic programming (ADP) algorithm to tackle the tracking control problem of unknown nonlinear systems with uncertain input saturation. Initially, an augmented system is established to address the investigated optimal tracking control issue with a discounted cost function. An online identifier based on neural networks (NNs) is designed to recover the unknown system dynamics. Furthermore, the controller design consists of two parts, including the optimal control policy for the nominal system and the NN-based feedforward compensator. In particular, the latter leverages adaptive NN techniques to cope with uncertain input constraints which can be deemed as uncertain saturation nonlinearities. A critic NN is constructed to obtain the approximate optimal control policy for the nominal system. Moreover, rather than traditional time-triggered and static event-triggered control schemes, an adaptive dynamic event-triggering condition is designed to determine the system sampling as well as the controller execution to further reduce transmission and computation burden. The introduction of an internal dynamic variable in possession of a positive constant lower bound can realize the dynamic adjustment of triggering threshold and effectively exclude Zeno behavior. Notice that the weights of identifier NN and critic NN are updated simultaneously, only at the event-triggering instants. Subsequently, the Lyapunov-based theoretical analysis is conducted to confirm the uniform ultimate boundedness of the tracking error and all the NN weight estimation errors. Eventually, the feasibility and effectiveness of the developed algorithm are validated by means of two simulation examples.

Fractional-Order Composite Adaptive Control of Robot Manipulators

The branch of system identification and control theory concerned about robot manipulators is adaptive control of non-linear multi-input multi-output dynamic systems which study constitutes a mature and well-founded discipline; nevertheless, there are fundamental problems open to date, one of them is to obtain a control law that is rich enough to guarantee its persistent excitation and consequently that the parametric error converges asymptotically to zero, while the tracking error converges globally asymptotically to zero. An extension of a globally convergent adaptive scheme control for a robot manipulator in the tracking of a determined trajectories with no consideration of the interaction with its environment is proposed, based on the fact that the parameter uncertainty is involved in both the tracking error and the identification error. The first control task is achieved by a feedback linearization technique that takes advantage of the structure of manipulator dynamics. The second task is achieved by a fractional order filtering technique to avoid the joint acceleration and enrich the regressor matrix in such way that is persistently exciting. Finally, the control law is expressed as feedforward compensation and a simple PD controller.

Three‐vector model‐free predictive control for permanent magnet synchronous motor

AbstractA three‐vector model‐free predictive control (TV‐MFPC) strategy is proposed to address the problem of model predictive control systems performance degradation caused by parameter mismatch and inverter dead‐zone. Firstly, an ultra‐local model is constructed for parameter mismatch and dead‐zone. Then, to improve the adaptive capability of the observer to parameter variations, sliding mode control is introduced, and an Improved Extended State Observer (IESO) is designed to estimate unknown disturbances in the ultra‐local model and provide feedforward compensation for the current loop. Finally, the proposed strategy is experimentally validated, and the results demonstrate that it reduces dependence on the model, suppresses the phase current distortion caused by dead‐zone, and improves the robustness of the control system.

Research on Bandwidth Improvement of Fine Tracking Control System in Space Laser Communication

Piezoelectric fast steering mirror (PZT FSM) is the core component of the fine tracking system for space laser communication, and its actuator is a piezoelectric ceramic. Consequently, there is a hysteretic nonlinear disturbance throughout the entire range of the FSM’s steering. To enhance the fine tracking system’s performance, this paper innovatively analyzes and verifies the effect of the PZT FSM hysteresis characteristics on the error suppression bandwidth of the fine tracking system. Firstly, the rate-dependent hysteresis model is established by serially connecting the Prandtl–Ishlinskii (P-I) model with the dynamic linear mode. The inverse model is designed as a feedforward controller, followed by the conduction of open-loop feedforward compensation experiments. Subsequently, we propose a compound control method based on the rate-dependent hysteresis mode and conduct a simulation analysis. Finally, the experimental platform for the fine tracking system is set up, and the optimization effect of compensating for hysteresis nonlinearity on the fine tracking system is verified. The experimental results show that the nonlinearity of the PZT FSM is improved by 30% in the middle- and high-frequency ranges, and the error suppression bandwidth of the fine tracking system is improved by 41.7%. This effectively enhances the fine tracking system’s error suppression capability.

High precision control of direct-drive permanent magnet linear motor with adaptive friction compensation

To improve the control precision and stability of Direct-Drive Permanent Magnet Linear Motor (DPMLM) with frictional nonlinearity and uncertain disturbances at a low speed, an adaptive robust integral sign error control algorithm based on friction compensation of dual nonlinear observer is proposed in this study. An improved LuGre friction model is established to describe the friction phenomena of the system. The dual nonlinear observer is designed to observe the internal frictional state of the model. A parameter adaptive law is designed to perform parameter estimation of structured uncertainty. Robust integral of the sign of the error (RISE) term is designed to overcome the frictional nonlinear disturbances. The friction nonlinearity and parameter uncertainty are compensated by feedforward compensation. The bounded stability of the proposed controller is proved by Lyapunov stability theory. As suggested by the experimental results, RMSE index decreased by 10.8% and the control precision was improved effectively by the proposed control algorithm at a low speed.

Data-Based Shape Self-Sensing of a Cable-Driven Notched Continuum Mechanism Using Multidimensional Intrinsic Force Information for Surgical Robot

Abstract The accurate shape-sensing capability of the continuum mechanism is fundamental to improve and guarantee the motion control accuracy and safety of continuum surgical robots. This paper presents a data-based shape self-sensing method for a cable-driven notched continuum mechanism using its multidimensional intrinsic force information, which mainly includes the multidimensional forces/torques and driving cable tensions. The nonlinear hysteresis compensation and the shape estimation of the notched continuum mechanism play significant roles in its motion control. Calibration compensation of the notched continuum mechanism is performed based on kinematic modeling to improve the accuracy of its preliminary motion control. The hysteresis characteristics of the continuum mechanism are analyzed, modeled, and compensated through considering the abundant dynamic motion experiments, such that a feedforward hysteresis compensation controller is designed to improve the tracking control performance of the continuum mechanism. Based on the kinematic calibration and hysteresis compensation, combined with the motor displacement, driving cable tensions, and six-dimensional forces/torques information of the continuum mechanism, a data-based shape self-sensing method based on particle swarm optimization back propagation neural network (PSO-BPNN) is proposed in this study. Experimental results show that this method can effectively estimate the loaded and unloaded shape of the notched continuum mechanism, which provides a new approach for the shape reconstruction of cable-driven notched continuum surgical robots.

Research on a Permanent Magnet Synchronous Motor Sensorless Anti-Disturbance Control Strategy Based on an Improved Sliding Mode Observer

This paper designs an improved sliding mode observer (ISMO) compound control scheme combined with a disturbance observer to solve the chattering and anti-disturbance problems of the traditional sliding mode observer (SMO) for permanent magnet synchronous motor (PMSM) in a sensorless control system. First, the sign function is replaced by an exponential type input function, and the fuzzy control rules are developed to automatically regulate the boundary layer control coefficient of the exponential input function, thereby changing the convergence characteristics of the exponential input function and improving the system observation accuracy. Then, the integral sliding mode surface and the quadratic radical term function of the square of the state variable are introduced to reduce system chattering. The proposed ISMO is proved using Lyapunov’s law to guarantee the whole system is stable. Based on the exponential input function and the integral sliding surface, an improved sliding mode disturbance observer (ISMDO) is constructed as a feed-forward compensator, which can optimize the dynamic performance of the improved observation system and ensure the strong robustness of the system by compensating the q-axis current. Finally, MATLAB/Simulink simulation and experimental platform verification have been carried out, which confirms the feasibility of the proposed composite control scheme.

Improved active disturbance rejection speed control for autonomous driving of high-speed train based on feedforward compensation.

Due to the complex and changeable train operation environment and the unstable and time-varying parameters, accurate modeling is limited. Therefore, a modified active disturbance rejection control algorithm based on feedforward compensation (FC-MADRC) is proposed targeting the speed control problem of trains under the circumstances of external disturbances, which reduces the dependence on the train model. Firstly, the state space equation is established based on the single-particle mathematical model of the train, and all the running resistances are regarded as disturbances. Secondly, the FC-MADRC algorithm is designed. Based on the terminal attractor function and the novel Sigmoid function, an improved tracking differentiator (ITD) is designed. An improved fal (nsfal) function with better smoothness is constructed by using the properties of the Dirac δ function, and an ameliorative nonlinear state error feedback (ANLSEF) and a modified extended state observer (IESO) are designed based on the nsfal function. Furthermore, based on the thought of PID, the integral term of error is introduced into ANLSEF for the nonlinear operation to reduce the steady-state error of train speed tracking. In order to promote the robustness and control accuracy of the system, the feedforward compensation term and disturbance compensation term are combined to perform dynamic compensation for disturbances in real time. Finally, the simulation is carried out with CRH380A train data. The results indicate that compared with conventional ADRC and 2DOF-PID, FC-MADRC has the more vital anti-disturbance ability and higher tracking accuracy. FC-MADRC has the advantages of solid anti-disturbance, fast response, and high tracking accuracy. Under the premise of external disturbance, it can still achieve accurate speed tracking under different road conditions.

Singular perturbation decomposition-based ride quality control of elastic aircraft

Considering the reduction of ride comfort under wind disturbance, the ride quality control method based on singular perturbation decomposition is proposed. For the dynamic model of elastic aircraft, the singular perturbation theory is used to decouple the model into the rigid-slow subsystem and the flexible-fast subsystem. Considering the additional time-varying disturbance and aerodynamic uncertainty of the rigid subsystem, the disturbance observer is designed to estimate disturbance effect and the neural network is used to deal with model uncertainty. The adaptive robust control is constructed using the composite estimation information as feedforward compensation and the tracking error of pitch rate and normal overload as feedback design. For the flexible subsystem, a nonsingular terminal sliding mode controller is designed to achieve active vibration suppression. The ride quality control law of elastic aircraft is obtained by combining the control inputs of the rigid and flexible subsystems, and the additional normal overload and elastic mode can be quickly restrained and converged. Based on Lyapunov stability analysis, the uniformly ultimate boundedness of the system is proved. The simulation results show that the proposed method can reduce the additional normal overload at the key positions of the aircraft under discrete gust and atmospheric turbulence, and the riding quality of elastic aircraft is effectively improved.

Research on improved sliding mode control of permanent magnet synchronous motor with disturbance compensation

In order to address the shortcomings of traditional sliding mode variable structure control in permanent magnet synchronous motor vector control, such as the state quantity’s inability to converge to 0 in limited time, too large chattering, too large load disturbance, poor dynamic performance, a non-singular fast terminal sliding mode control algorithm in speed loop is proposed. At the same time, a new approach rate is designed by comparing the advantages and disadvantages of different approach rates, which can restrain the natural chattering of sliding mode and improve the approach rate. On this basis, in order to increase response time, lessen chattering, and enhance the observer’s capacity for adaptation, a load torque observer is constructed, load disturbance is added to the speed loop as feedforward compensation, and a new approach rate is added to the observer. The simulation results manifest that the design successfully tracks load torque, enhances the system’s dynamic performance, and lessens the impact of sliding mode chattering.

Empirical Transfer Function Estimation With Differential Filtering and Its Application to Fine Positioning Control of Galvano Scanner

Fast and precise point-to-point (PTP) positioning control is a critical technology for improving the throughput and product quality of industrial machines. This study aims to investigate the frequency response function (FRF) estimation method with differential filtering (ETFE-Diff: empirical transfer function estimation with differential filtering) that uses the input and output signals of PTP motion during processing operation and to apply ETFE-Diff to a feedforward (FF) compensation adjustment. In this study, the principle of ETFE-Diff for FRF estimation was clarified, compared to the existing FRF estimation methods, i.e., empirical transfer function estimation and local rational modeling. Moreover, the effectiveness of ETFE-Diff for FF compensation adjustment in improving positioning accuracy of a galvano scanner as an industrial servo system was experimentally demonstrated.

A Two-Step Continuous-Control-Set MPC for Modular Multilevel Converters Operating With Variable Output Voltage and Frequency

This paper presents a new enhanced single-stage two-step Continuous Control Set Model Predictive Control (CCS-MPC) algorithm, for Modular Multilevel Converters (MMCs) operating with variable output frequency at the AC port. The proposed two-step CCS-MPC allows the implementation of feed-forward compensation of the one-step ahead perturbations at the AC side, using an algorithm based on the Moore-Penrose pseudoinverse matrix. Limitations of the current and voltages in the MMC clusters are implemented to avoid overmodulation and overcurrents, being the solution of the constrained CCS-MPC online solved using the active-set method. Without losing generality, the experimental validation of the single-stage CCS-MPC is performed using an 18-cell MMC prototype, driving a 7.5 kW cage induction machine operating in a wide speed range. A dSPACE MicroLabBox platform is used to control the MMC-based drive by implementing the CCS-MPC algorithm, the modulation stage and the active-set method. It is experimentally demonstrated that the compensation of the low-frequency perturbations allows a considerable reduction in the required circulating currents at the low-frequency operation of the drive. Reduction in the capacitor voltage ripple, improved efficiency, and a smaller Total Harmonic Distortion (THD) in the output currents are also achieved with the proposed single-stage two-step CCS-MPC.