Feedforward Scheme Research Articles

Co-contraction embodies uncertainty: An optimal feedforward strategy for robust motor control.

Despite our environment often being uncertain, we generally manage to generate stable motor behaviors. While reactive control plays a major role in this achievement, proactive control is critical to cope with the substantial noise and delays that affect neuromusculoskeletal systems. In particular, muscle co-contraction is exploited to robustify feedforward motor commands against internal sensorimotor noise as was revealed by stochastic optimal open-loop control modeling. Here, we extend this framework to neuromusculoskeletal systems subjected to random disturbances originating from the environment. The analytical derivation and numerical simulations predict a characteristic relationship between the degree of uncertainty in the task at hand and the optimal level of anticipatory co-contraction. This prediction is confirmed through a single-joint pointing task experiment where an external torque is applied to the wrist near the end of the reaching movement with varying probabilities across blocks of trials. We conclude that uncertainty calls for impedance control via proactive muscle co-contraction to stabilize behaviors when reactive control is insufficient for task success.

Cross-speed spindle motor bearings fault diagnosis combined with multi-space variable scale adaptive filter and feedforward hybrid strategy

Cross-speed spindle motor bearings fault diagnosis combined with multi-space variable scale adaptive filter and feedforward hybrid strategy

Utilizing artificial neural network system to predict the residual valve after endoscopic posterior urethral valve ablation.

To build, train, and assess the artificial neural network (ANN) system in estimatingthe residual valve rate after endoscopic valve ablationand compare the data obtained with conventional analysis. In a retrospective cross-sectional study between June 2010 and December 2020, 144 children with a history of posteriorurethral valve (PUV) who underwent endoscopic valve ablation were enrolled in the study. MATLAB software was used to design and train the network in a feed-forward backpropagationerror adjustment scheme. Preoperative and postoperative data from 101 patients (70%) (training set) were utilized to assessthe impact and relative significance of the necessity for repeatedablation. The validated suitably trained ANN was used to predict repeated ablation in the next 33 patients (22.9%) (test set)whose preoperative data were serially input into the system. To assess system accuracy in forecasting the requirement for repeatablation, projected values were compared to actual outcomes. The likelihood of predicting the residual valve was calculatedusing a three-layered backpropagating deep ANN using preoperativeand postoperative information. Of 144 operated cases, 33 (22.9%) had residual valvesand needs to repeated ablation. The ANN accuracy, sensitivity, and specificity for predicting the residual valve were 90.75%,92.73%, and 73.19%, respectively. Younger age at surgery, hyperechogenicity of the renal parenchyma, presence of vesicoureteralreflux (VUR), and grade of reflux before surgery were among the most significant characteristics that affectedpostoperative outcome variables, the need for repeated ablation, and were given the highest relative weight by the ANN system. Conclusions: The ANN is an integrated data-gathering tool for analyzing and finding relationships among variables as a complex non-linear statistical model. The results indicate that ANN is a valuable tool for outcome prediction of the residualvalveafter endoscopic valve ablation in patients with PUV.

A Novel Feedforward Scheme for Enhancing Dynamic Performance of Vector-Controlled Dual Active Bridge Converter with Dual Phase Shift Modulation for Fast Battery Charging Systems

This paper proposes a novel feedforward control scheme to achieve a very smooth transition from Constant Current (CC) to Constant Voltage (CV) charging modes, the commonly used method for electric vehicle charging applications. Furthermore, a three-loop model-independent Linear Active Disturbance Rejection Control (LADRC)-based system is proposed, replacing the traditional two-loop Proportional-Integral (PI) control system. The extra loop performs a decoupled dq vector control of the inductor current, which is typically not used in single-phase Dual Active Bridge (DAB) systems. This additional loop not only facilitates the optimal determination of both internal and external phase shift angles of a Dual-Phase Shift (DPS) modulator but also lowers the peak input current of the converter, allowing for lower-rated switches. Numerical simulations using MATLAB/Simulink demonstrate the robustness of the proposed control strategy against both input voltage disturbances and load disturbances during the transition from CC to CV charging modes. Hence, the dynamic performance of the charging system is significantly improved with minimal controller effort.

EXPERIMENTAL ASSESSMENT OF CONSUMER UNBALANCE AND NONLINEARITY EFFECTS IN THREE-PHASE NETWORKS WITHOUT NEUTRAL CONDUCTOR

The present study proposes a feedforward angle droop strategy based on a discrete-time mathematical model for operation dispatchable distributed generation (DG) units connected directly or through voltage source converters (VSC) to the microgrid. These units should supply their local and common loads. Droop coefficients should be increased to improve power division between different DG units. Also, a time delay in systems’ state variables and controller input is a disruptive parameter for the control system's performance. These two parameters have negative effects on network stability. To ensure the stability of the closed-loop system, a predictive sliding mode controller can be used based on discrete-time systems. However, this strategy cannot reduce the chattering phenomenon. This paper discusses the effect of time delay and increases in droop angle coefficients on the whole system and how these impacts can be eliminated. So, a combination of integral sliding mode controller (ISMC), composite nonlinear feedback (CNF), and sliding mode learning control (SMC), which is called hybrid SMC, is used with an angle droop controller.

THE EFFECTS OF HYBRID SLIDING MODE LEARNING CONTROL AND FEEDFORWARD ANGLE DROOP CONTROLLER WITH HIGH DROOP GAIN IN HYBRID MICROGRID WITH LOAD UNCERTAINTY AND NONLINEARITY

The present study proposes a feedforward angle droop strategy based on a discrete-time mathematical model for operation dispatchable distributed generation (DG) units connected directly or through voltage source converters (VSC) to the microgrid. These units should supply their local and common loads. Droop coefficients should be increased to improve power division between different DG units. Also, a time delay in systems’ state variables and controller input is a disruptive parameter for the control system's performance. These two parameters have negative effects on network stability. To ensure the stability of the closed-loop system, a predictive sliding mode controller can be used based on discrete-time systems. However, this strategy cannot reduce the chattering phenomenon. This paper discusses the effect of time delay and increases in droop angle coefficients on the whole system and how these impacts can be eliminated. So, a combination of integral sliding mode controller (ISMC), composite nonlinear feedback (CNF), and sliding mode learning control (SMC), which is called hybrid SMC, is used with an angle droop controller.

Hydrodynamic response on trajectory tracking of a tethered underwater robot system under hybrid control algorithms of umbilical cable and propellers

A hydrodynamic control model is established in attention to coupling relationships among cable, propellers and robot body of a tethered underwater robot system. In this model the governing equations of umbilical cable and the robot are firstly introduced, then supplementary conditions are coupled into the equations to forming the dynamic mathematical model; finally a hybrid control strategy based on feed-forward negative feedback method for the cable and PID rule for the propellers are integrated in the mathematical model for composing the whole hydrodynamic control model. Both the mathematical model and the control algorithms are proved to be effective and reliable through comparing simulation with the experimental data in existed references. Based on the numerical model constructed in this paper, trajectory tracking of a tethered underwater robot system in different motion combinations are numerically simulated through computational fluid dynamics method. In the numerical simulations, finite difference method is used to solving the kinematic parameters of the mathematical model, while finite volume method is applied on calculating the hydrodynamic forces under a hybrid control manipulations. The robot motion in vertical direction is determined primary by feed-forward negative feedback strategy of adjusting the cable length, while the horizontal movement of the robot is controlled mainly through PID algorithm; The hydrodynamic loading on the robot body are influenced by the flow fields around the robot.

In silico validation of a customizable fully-autonomous artificial pancreas with coordinated insulin, glucagon and rescue carbohydrates

Artificial pancreas systems should be designed considering different patient profiles, which is challenging from a control theory perspective. In this paper, a flexible-hybrid dual-hormone control algorithm for an artificial pancreas is proposed. The algorithm handles announced/unannounced meals by means of a non-interacting feedforward scheme that safely incorporates prandial boluses. Also, a coordination strategy is employed to distribute the counter-regulatory actions, which can be delivered as a continuous glucagon infusion via an automated pump, as an oral rescue carbohydrate recommendation, or as a rescue glucagon dose recommendation to be administrated through a glucagon pen. The different configurations of the proposed controller were evaluated in silico using a 14-day virtual scenario with random meal intakes and exercise sessions, achieving above 80% time-in-range and low time spent in hypoglycemia.

Repetitive control with feedforward scheme for periodic vibration displacement control of mold driven by servo motor

Repetitive control with feedforward scheme for periodic vibration displacement control of mold driven by servo motor

An optimal feedforward circuit with null audio susceptibility in feedback‐controlled buck converter

AbstractThe ability to manage abrupt disturbances in input supply voltage and sudden fluctuations in load represents a fundamental requirement for DC‐DC buck converters. While feedback control systems typically address such transients, certain applications necessitate even faster transient responses. This paper introduces an input voltage feedforward scheme (IVFS) integrated with voltage mode feedback controllers to enhance closed‐loop performance. Relevant transfer functions resulting from the inclusion of the feedforward circuit are derived, and an optimal condition for audio susceptibility nullification is identified. This scheme ensures an additional attenuation in magnitude of audio susceptibility, in contrast to the buck converter without feedforward. In the time domain, the incorporation of IVFS results in a 20% reduction in percentage overshoot for a 12‐8‐12 step in supply voltage. Additionally, for a 12‐15‐12 step, the percentage overshoot and settling time are completely eliminated. Various active and passive parasitics are included in modeling of the buck converter to aid in designing the feedforward and feedback controller. A Type‐III controller is employed as a feedback controller to assess the closed‐loop performance. A hardware prototype is built to demonstrate the efficacy of the proposed feedforward scheme. The implementation of the entire circuit is straightforward due to its requirement of only a few discrete components, namely, analog multiplier, and error amplifier.

Data‐driven iterative learning cooperative trajectory tracking control for multiple autonomous underwater vehicles with input saturation constraints

AbstractThis paper investigates the cooperative trajectory tracking (CTT) control problem of multiple autonomous underwater vehicles (AUVs). The multi‐AUV system is characterized by uncertain dynamics, being subjected to the impact about input saturation constraints and unmeasurable disturbances. First, a neural network‐based data‐driven control algorithm is proposed for the multi‐AUV system with unmeasurable disturbances and model parameters uncertain. The radial basis function neural network is employed to estimate the primary pseudo parameters of an equivalent data model, established through dynamic linearization methods. Subsequently, an iterative learning control approach based on adaptive gain is designed to act as a feedforward scheme along the iteration axis to enhance the tracking accuracy within a time constraint. Third, to prove that the resulting CTT control system fulfills the bounded stability under the proposed control approach, a formal stability analysis is provided. Finally, a simulation case study is conducted to illustrate the effectiveness of the proposed CTT control approach.

Control strategy of bidirectional DC/DC converter under variable load state

To improve the stability of DC/DC converters under multiple load conditions, the control strategy of Buck-Boost bidirectional DC/DC converters under variable load conditions is studied in this paper, as well as the droop control strategy for parallel operation of two machines. Based on the topology and basic control strategy of the DC/DC converter, a load current feedforward compensation strategy to achieve ideal stability of output voltage during load switching is studied; then charging and discharging control switching strategy under external power supply is proposed, which achieves fast switching between charging and discharging by sampling battery current and load voltage; finally, a current sharing control method based on inductance current droop is proposed for the parallel operation of two machines, achieving the control effect of equal current sharing between the two modules. The effectiveness and feasibility of the control strategy were verified through prototype experiments.

Power quality improvement of wind turbines using doubly-fed induction machines.

A novel approach to modelling doubly-fed induction machines facing (distorted) ac power networks is presented. It takes place in the frequency-domain and is based on frequency coupling matrices using tensor algebra. Benefits of this comprehensible and fast modelling approach are demonstrated by establishing a feed-forward control signal that reduces stator current harmonics and torque ripple thereby improving the network compatibility and reducing the stress on the mechanical drive train. Dynamic simulations using PSCAD/EMTDC and MATLAB/Simulink are presented demonstrating the effectiveness of the proposed modelling approach and the feed-forward compensation scheme.

Application of a feedforward control-based intervention for preventing hypothermia in trauma patients in a pre-hospital emergency setting.

To investigate the efficacy of a feedforward control-based intervention strategy for preventing hypothermia among trauma patients during pre-hospital emergency care. We conducted a retrospective analysis comparing trauma patients treated before and after implementing the intervention, with 40 cases in each group. All patients received emergency care from the Fuzhou Emergency Center on the scene. Multivariate analysis was used to explore the risk factors for hypothermia. The effective rate, incidence of adverse reactions, quality of body temperature management, medical staff's knowledge, attitudes, and behaviors regarding mild hypothermia prevention, coagulation function, treatment time at various stages, prognosis score, and treatment situation were compared between the two groups. The adverse reactions, intervention methods, and degree of cognitive improvement were influencing factors for hypothermia. The effective rate (92.50%) in the feedforward control group was higher than that in the non-feedforward control group (65.00%), with a lower incidence of adverse reactions (2.50%). The temperature management quality score of the feedforward control group (6.23±0.62) was higher. The feedforward control group achieved a higher quality score for temperature management (6.23±0.62) and exhibited a greater understanding of hypothermia prevention among trauma patients (P<0.05). Compared to the non-feedforward control group, the feedforward control group showed improved coagulation function, better performance in treatment time at each node, and higher prognosis scores. The intervention model based on feedforward control can effectively improve the standard of pre-hospital emergency care and prevent the incidence of hypothermia in trauma patients.

A Hybrid Active Damping Strategy for Improving the Adaptability of LCL Converter in Weak Grid

In a weak grid, the line impedance variation will cause the resonant frequency of the LCL filter to shift towards lower frequencies, thus reducing the quality of the grid-connected current and affecting the power grid stability. To solve this problem, a hybrid active damping strategy with feedforward compensation is proposed for the neutral point clamped (NPC) LCL grid-connected inverter system. In order to reshape the output conductance of the grid-connected system, suppress the resonance spikes of the LCL filter and improve the adaptability of the grid-connected system to the weak grid. A first-order low-pass filter is designed in the grid-connected current loop, and an active damping control of grid-connected current based on a first-order high-pass filter is also proposed. Compared with the conventional capacitive current active damping, no additional sensors are required, and the use of a differential is avoided, which reduces the high-frequency noise. The use of passive resistors is reduced, which reduces the power loss of the grid-connected system. In addition, a point of common coupling (PCC) voltage feedforward strategy based on a low-pass filter is designed to suppress the background higher harmonics at PCC and improve the quality of grid-connected current. In this work, the robustness of the system is analyzed when the parameters of the LCL filter change. Finally, the virtual space vector modulation strategy is used to balance the neutral voltage of the DC bus. Simulation and experimental results show that the control strategy can effectively improve the adaptability of the system to the weak power grid, improve the quality of grid-connected current, and demonstrate strong stability. The THD can be decreased by 0.2% at least, and the improvements are more significant with larger line impedance; the THD is only 2.33% even at 10 mH line impedance.

Designing Parameters to Reshape the Inverter Output Impedance Based on the D-Split Method under Weak Grid Conditions

Novel energy generation technologies, such as high-permeability wind power and photovoltaic systems, exhibit inherent intermittency, randomness, and volatility. During periods of energy production from these sources, the impedance of the electrical grid frequently experiences significant fluctuations. Consequently, the grid manifests characteristics akin to a weak grid, highlighting the challenges associated with integrating renewable energy sources with variable output profiles. These fluctuations lead to a shift in resonance spikes of the LCL-type filter towards the lower frequency band, consequently impacting the stability of inverter grid connections. To mitigate this issue, the present study introduces a novel approach integrating the D-split method and a proportional–differential feedforward strategy. This combination aims to reshape the inverter’s output impedance, ensuring its consistent stability even amidst significant fluctuations in grid impedance. In this research, the optimal controller parameters for systems operating under robust grid conditions are initially determined. This selection process involves the application of the D-split method, supported by graphical visualization. Additionally, the amplitude and phase angle margin tester is employed while considering the system bandwidth as a limiting constraint. Moreover, accounting for the influence of grid impedance, the D-split method is reapplied to ascertain the most suitable values for the proportional–differential feedforward parameters. This adjustment aims to enhance the adaptability of the grid-connected inverter specifically in scenarios characterized by a weak grid. The hardware-in-the-loop (HIL) simulation results demonstrate that despite the presence of harmonics within the grid and a short circuit impedance ratio (SCR) of 3, the inverter maintains grid stability and effectively suppresses harmonics. Moreover, the total harmonic distortion factor (THD) of the grid-connected current remains below 5%.

Adaptability enhancement of fractional‐order LLCL‐type grid‐connected inverter to weak grid based on grid voltage weighted feedforward scheme

SummaryGrid‐connected inverter with a novel fractional‐order LLCL filter has the advantage of high grid current tracking accuracy and low total harmonic distortion without passive or active dampers, and the grid voltage full feedforward scheme is an effective method to improve the quality of grid current. However, due to the digital control delay, the suppression of the grid current harmonics is weakened, and the inverter output impedance has an additional negative phase shift, which deteriorates the stability of grid‐connected systems under weak grid. In this paper, a weighted grid voltage fractional‐order feedforward control scheme is proposed based on a quasi‐proportional resonant (QPR) controller, which introduces two weighting factors in the full feedforward path of the grid voltage and compensates the phase lag of the output impedance by designing appropriate weighting factors. Then, an improved scheme combining the QPR controller with a phase compensator is presented. As a result, the suppression ability of grid current harmonics and the adaptability to grid impedance variations are enhanced. Simulation and experimental results verify the effectiveness of the proposed grid voltage fractional‐order weighted feedforward scheme.

A hybrid feedforward scheme for LCL grid-connected inverters

A hybrid feedforward scheme for LCL grid-connected inverters

A framework based on generational and environmental response strategies for dynamic multi-objective optimization

Due to the dynamics and uncertainty of the dynamic multi-objective optimization problems (DMOPs), it is difficult for algorithms to find a satisfactory solution set before the next environmental change, especially for some complex environments. One reason may be that the information in the environmental static stage cannot be used well in the traditional framework. In this paper, a novel framework based on generational and environmental response strategies (FGERS) is proposed, in which response strategies are run both in the environmental change stage and the environmental static stage to obtain population evolution information of those both stages. Unlike in the traditional framework, response strategies are only run in the environmental change stage. For simplicity, the feed-forward center point strategy was chosen to be the response strategy in the novel dynamic framework (FGERS-CPS). FGERS-CPS is not only to predict change trend of the optimum solution set in the environmental change stage, but to predict the evolution trend of the population after several generations in the environmental static stage. Together with the feed-forward center point strategy, a simple memory strategy and adaptive diversity maintenance strategy were used to form the complete FGERS-CPS. On 13 DMOPs with various characteristics, FGERS-CPS was compared with four classical response strategies in the traditional framework. Experimental results show that FGERS-CPS is effective for DMOPs.

A Novel Energy Management Control Scheme for a Standalone PV System in a DC Nanogrid

Distributed energy resources (DERs), such as photovoltaic (PV) sources, together with storage systems, such as battery energy storage systems (BESS), are increasingly present and necessary in our electricity distribution networks. Furthermore, the need for efficient use of energy from DERs, especially in developing countries and remote communities, must be addressed with the development of nanogrids (NGs), particularly DC NGs, and standalone PV systems with adequate control strategies. This paper investigates the control and dynamic operation of a standalone PV system. It consists mainly of three DC–DC power converters for the PV source interface, battery management, and load voltage control. A two-level modulation scheme is applied to each of these converters to switch them ON and OFF. A maximum power point tracking (MPPT) closed-loop voltage control system is implemented to make sure that the PV operates at optimum power regardless of the irradiance level or temperature, while battery voltage and load-side voltage control are also implemented to indirectly provide the required load power. The control of each of the converters is achieved by deriving their small-signal models using a state-space approach from which various control objectives are implemented. The DC-link is clamped by a BESS which acts as a backup source to provide power to the DC load in the absence of sufficient power from the PV panel. The dynamic operation of the whole system is enhanced by proposing a robust feedforward scheme that improves the response of the system in the presence of disturbances. The models are analyzed and implemented using PLECS, and numerical simulations are performed to validate the developed models and control schemes.