Feedforward Feedback Research Articles

Optimal Design and Control of a Two-Speed Planetary Gear Automatic Transmission for Electric Vehicle

Multispeed transmissions are helpful for improvement of the economy and drivability of electric vehicles (EVs). In this paper, we propose a two-speed transmission based on dual planetary gear mechanism, in which shifts are realized by torque transfer between two brakes located on ring gears. To synthesize the dynamic and economic performances of the vehicle, a multiobjective optimization problem is constructed for gear ratio optimization and Pareto-optimal solutions of gear ratio combinations are obtained by Nondominated sorting genetic algorithm-II (NSGA-II). In particular, the minimum electric energy consumption of the EV is calculated with a fast Dynamic Programming (DP) in each iteration. Following this, a constant-output-torque control (COTC) scheme is adopted for the torque phase and inertia phase of gearshift process to ensure constant output torque on the wheel. To enhance transient responses, the feedforward–feedback controller structure is applied and a disturbance observer is integrated to improve robustness. Simulation results demonstrate that the two-speed transmission has much better performance in terms of acceleration time and energy economy compared to the fixed-ratio transmission, and the proposed gearshift control method is able to achieve fast and smooth gear shift robustly while maintaining constant output torque.

Research on the Combination Technology of Smart Meter Status Evaluation and Acquisition Function Based on Big Data Technology

In order to study the key technologies suitable for large-scale intelligent electric energy meter automatic verification, the discrete verification mode is discussed from three aspects of data interaction, scheduling control, and intelligent diagnosis. First, build an application architecture for automated verification data from the perspective of multi-dimensional perception, use WCF and MQ communication modes to build a data interaction architecture, and propose a feedforward feedback integrated control mechanism; second, based on the automated verification control architecture, establish a space-time vertical and horizontal scheduling control strategy; finally. According to the category of automatic verification equipment, classify abnormal / fault categories and establish a fault knowledge base. Based on the intelligent diagnosis process of “alarm-discovery-diagnosis-processing-learning” mechanism, build an intelligent diagnosis application framework. The proposed method effectively guarantees the accuracy and reliability of the interactive data, realizes the automatic verification of space-time cooperative operation, and can effectively improve the daily operation and maintenance efficiency of large-scale automated verification.

Feedforward feedback iterative learning control method for the multilayer boundaries of oversaturated intersections based on the macroscopic fundamental diagram

The feedback control based on the model and method of iterative learning control, which in turn is based on the macroscopic fundamental diagram (MFD), mostly belongs to the classification of single-layer boundary control method. However, the feedback control method has the problem of time delay. Therefore, a feedforward feedback iterative learning control (FFILC) method based on MFD of the multi-layer boundary of single-area oversaturated intersections is proposed. The FFILC method can improve the effectiveness of boundary control and avoid the time-delay problem of feedback control. Firstly, MFD theory is used to determine the MFD of the control area; the congestion zone and the transition zone of the control area are identified; and the two-layer boundary of the control area is determined. Then, the FFILC controllers are established at the two-layer boundary of the control area. When the control area enters into a congestion state, the control ratio of traffic flow in and out of the two-layer boundary is adjusted. The cumulative number of vehicles in the control area continues to approach the optimal cumulative number of vehicles, and it maintains high traffic efficiency with high flow rates. Finally, The actual road network is taken as the experimental area, and the road network simulation platform is built. The controller of the feedforward iterative learning control (FILC) is selected as the comparative controller and used to analyse the iterative effect of FFILC. Improvements in the use of traffic signal control indicators for the control area are analysed after the implementation of the FFILC method. Results show that the FFILC method considerably reduces the number of iterations, and it can effectively improve convergence speed and the use of traffic signal evaluation indicators for the control area.

An Adaptive Fuzzy Predictive Controller with Hysteresis Compensation for Piezoelectric Actuators

Piezoelectric actuators (PEAs) are the pivotal components of many nanopositioning systems because of their superiorities in bandwidth, mechanical force, and precision. Unfortunately, the intrinsic nonlinear property, hysteresis, makes it difficult to achieve the precise control of PEAs. Considering this drawback, diversified feedback control approaches have been studied in the literature. Inspired by the idea that the involvement of feedforward terms can upgrade the tracking performance, our previous conference paper proposed a novel feedforward–feedback control approach (model predictive control with hysteresis compensation). Following the previous work, an adaptive fuzzy predictive controller with hysteresis compensation is further studied in this paper. The major improvement of the proposed method is the employment of adaptive fuzzy model, by which the dynamic model of PEAs is able to adjust in real time, resulting in a better control performance. To validate the effectiveness of the proposed method, extensive experiments are conducted on a Physik Instrumente P-753.1CD piezoelectric nanopositioning stage. Comparisons with several existing control approaches are carried out, and the root mean square tracking error of the proposed method is reduced to 30% of that under the previously proposed neural network model–based predictive control, when tracking 100 Hz sinusoidal reference.

Sparse identification of contrast gain control in the fruit fly photoreceptor and amacrine cell layer

The fruit fly’s natural visual environment is often characterized by light intensities ranging across several orders of magnitude and by rapidly varying contrast across space and time. Fruit fly photoreceptors robustly transduce and, in conjunction with amacrine cells, process visual scenes and provide the resulting signal to downstream targets. Here, we model the first step of visual processing in the photoreceptor-amacrine cell layer. We propose a novel divisive normalization processor (DNP) for modeling the computation taking place in the photoreceptor-amacrine cell layer. The DNP explicitly models the photoreceptor feedforward and temporal feedback processing paths and the spatio-temporal feedback path of the amacrine cells. We then formally characterize the contrast gain control of the DNP and provide sparse identification algorithms that can efficiently identify each the feedforward and feedback DNP components. The algorithms presented here are the first demonstration of tractable and robust identification of the components of a divisive normalization processor. The sparse identification algorithms can be readily employed in experimental settings, and their effectiveness is demonstrated with several examples.

Vibrational mono-/bi-resonance and wave propagation in FitzHugh–Nagumo neural systems under electromagnetic induction

In this paper, an modified FitzHugh–Nagumo (FHN) neural model was employed to investigate the vibrational resonance (VR) phenomenon, the collective behaviors, and the transmission of weak low-frequency (LF) signal driven by high-frequency (HF) stimulus under the action of different electromagnetic induction in single FHN neuron and feed-forward feedback network (FFN) system, respectively. For the single FHN system, by increasing the amplitude of HF stimulus, the phenomena of vibrational mono-/bi-resonance are observed, and the input weak signal and output of system are synchronized, and the information of the weak LF signal is amplified. For the FFN system, the phenomena of vibrational mono-/bi-resonances are also occurred, both frequency and amplitude of the HF stimulus play an important role in the vibrational bi-resonances and transmission of weak LF signal in the FHN neural FFN.

A Trajectory Tracking Control of a Robot Actuated With Pneumatic Artificial Muscles Based on Hysteresis Compensation

This paper presents a new trajectory tracking control strategy for a novel lower-limb rehabilitant robot, AirGait, which is a parallel mechanism with three degrees of freedom and is actuated with four pneumatic artificial muscles (PAMs). Compared with the existing control methods, the feature of this approach is that it combines the feedforward/feedback (FF) controller based on the length-pressure hysteresis compensation of PAMs with a joint space control method on the trajectory tracking control of a parallel mechanism. For the controller design, the inverse and forward kinematic formulas of AirGait are developed firstly based on the structure analysis. Then, a lower-limb kinematic model of humans is developed and the human-like trajectory of time is obtained by using a Fourier series method. Finally, the control scheme is introduced and the trajectory tracking control of AirGait with two reference input signals is presented. The comparative experimental results indicate that the performance of this control approach is strong and this robot holds great promise for assisting lower-limb locomotion.

Feedback Passivation of Linear Systems With Fixed-Structure Controllers

This letter addresses the problem of designing an optimal output feedback controller with a specified controller structure for linear time-invariant (LTI) systems to maximize the passivity level for the closed-loop system, in both continuous-time (CT) and discrete-time (DT). Specifically, the set of controllers under consideration is linearly parameterized with constrained parameters. Both input feedforward passivity (IFP) and output feedback passivity (OFP) indices are used to capture the level of passivity. Given a set of stabilizing controllers, a necessary and sufficient condition is proposed for the existence of such fixed-structure output feedback controllers that can passivate the closed-loop system. Moreover, it is shown that the condition can be used to obtain the controller that maximizes the IFP or the OFP index by solving a convex optimization problem.

Frequency-Dependent Schroeder Allpass Filters

Since the introduction of feedforward–feedback comb allpass filters by Schroeder and Logan, its popularity has not diminished due to its computational efficiency and versatile applicability in artificial reverberation, decorrelation, and dispersive system design. In this work, we present an extension to the Schroeder allpass filter by introducing frequency-dependent feedforward and feedback gains while maintaining the allpass characteristic. By this, we directly improve upon the design of Dahl and Jot which exhibits a frequency-dependent absorption but does not preserve the allpass property. At the same time, we also improve upon Gerzon’s allpass filter as our design is both less restrictive and computationally more efficient. We provide a complete derivation of the filter structure and its properties. Furthermore, we illustrate the usefulness of the structure by designing an allpass decorrelation filter with frequency-dependent decay characteristics.

Therapeutic targeting of the E3 ubiquitin ligase SKP2 in T-ALL

Timed degradation of the cyclin-dependent kinase inhibitor p27Kip1 by the E3 ubiquitin ligase F-box protein SKP2 is critical for T-cell progression into cell cycle, coordinating proliferation and differentiation processes. SKP2 expression is regulated by mitogenic stimuli and by Notch signaling, a key pathway in T-cell development and in T-cell acute lymphoblastic leukemia (T-ALL); however, it is not known whether SKP2 plays a role in the development of T-ALL. Here, we determined that SKP2 function is relevant for T-ALL leukemogenesis, whereas is dispensable for T-cell development. Targeted inhibition of SKP2 by genetic deletion or pharmacological blockade markedly inhibited proliferation of human T-ALL cells in vitro and antagonized disease in vivo in murine and xenograft leukemia models, with little effect on normal tissues. We also demonstrate a novel feed forward feedback loop by which Notch and IL-7 signaling cooperatively converge on SKP2 induction and cell cycle activation. These studies show that the Notch/SKP2/p27Kip1 pathway plays a unique role in T-ALL development and provide a proof-of-concept for the use of SKP2 as a new therapeutic target in T-cell acute lymphoblastic leukemia (T-ALL).

Information Propagation and Public Opinion Evolution Model Based on Artificial Neural Network in Online Social Network

Abstract This paper proposes a new information dissemination and opinion evolution IPNN (Information Propagation Neural Network) model based on artificial neural network. The feedforward network, feedback network and dynamic evolution algorithms are designed and implemented. Firstly, according to the ‘six degrees separation’ theory of information dissemination, a seven-layer neural network underlying framework with input layer, propagation layer and termination layer is constructed; secondly, the information sharing and information interaction evolution process between nodes are described by using the event information forward propagation algorithm, opinion difference reverse propagation algorithm; finally, the external factors of online social network information dissemination is considered, the impact of external behavior patterns is measured by media public opinion guidance and network structure dynamic update operations. Simulation results show that the proposed new mathematical model reveals the relationship between the state of micro-network nodes and the evolution of macro-network public opinion. It accurately depicts the internal information interaction mechanism and diffusion mechanism in online social network. Furthermore, it reveals the process of network public opinion formation and the nature of public opinion explosion in online social network. It provides a new scientific method and research approach for the study of social network public opinion evolution.

The role of teacher feedback–feedforward and personal best goal setting in students’ mathematics achievement: a goal setting theory perspective

Teacher feedback in mathematics is a potential strategy for redressing declining international trends in high school mathematics achievement. Effective feedback comprises corrective information (feedback) and guidance for improvement (feedforward)—referred to herein as ‘feedback–feedforward’. Relatively little research has examined the motivational mechanisms linking teacher feedback to achievement, with even less linking feedback–feedforward to achievement. Goal setting theory proposes that goal setting, especially growth-oriented (e.g. personal best) goal setting, may mediate the positive impact of feedback on achievement. Using structural equation modelling, this study (N = 362 Australian students) builds on this to examine if (a) teacher feedback–feedforward and PB goal setting predicted achievement in mathematics and (b) PB goal setting mediated the link between teacher feedback–feedforward and achievement. Mathematics teacher feedback–feedforward predicted PB goal setting, PB goal setting predicted achievement, and PB goal setting fully mediated the link between feedback–feedforward and achievement. Implications for mathematics teacher feedback–feedforward and goal-setting interventions are discussed.

Model-Based Nonlinear Control of the Dielectric Elastomer Actuator With High Robustness and Precision

Abstract Dielectric elastomers (DEs) is one of the promising artificial muscle for soft robots and flexible devices. As one of the key issues for practical applications, the control of DE actuators remains challenging due to the large actuation, electromechanical coupling, and viscoelastic dissipation. Feedforward control and proportional integral derivative (PID) feedback control are recently studied for the control of DE actuators. The control performance is still limited due to the complex dynamic behavior of DE actuators with both nonlinearities and modeling uncertainties. This paper proposes a model-based feedback control for DE actuator, considering nonlinearity of large deformation, electromechanical coupling, and the modeling uncertainties. A nonlinear motion model is proposed and verified by parameter identification experiments. Based on the nonlinear model, we demonstrate a robust control strategy including nonlinear model compensation and robust feedback to decrease the tracking error. The experimental results verify that the control strategy possesses excellent validity to the DE actuator with improved performance compared to the previous strategy of feedforward and PID feedback control. The system design and control strategy of this paper may guide the future design and application of DE actuators, soft robots, and flexible devices.

Dynamic analysis of a simplified flexible manipulator with interval joint clearances and random material properties

Flexible manipulator is an emerging technique in aerospace engineering, especially in the assembly, testing and maintenance of space stations. Dynamic analysis of a flexible manipulator with multiple clearance joints and hybrid uncertainties is a great challenge as compared to traditional flexible manipulator. To solve the problem, a dynamics model for a simplified flexible manipulator with interval clearance joints and random material properties was established. In this model, the Lankarani–Nikravesh contact force model was used to construct the clearance joint, while a combined feedforward–feedback control strategy based on a PID controller was applied to control the flexible manipulator. In addition, the clearance sizes and the Young’s moduli of the flexible parts were described by interval variables and random fields, respectively. To solve the dynamics model, a general methodology, based on the Karhunen–Loeve expansion and Kriging model, was presented. Finally, numerical examples were employed to demonstrate the validity of the proposed approach. The simulation results indicate that the joint clearance, the flexibility of the components and the uncertainties have great impacts on the kinematic accuracy and dynamic behaviors of the flexible manipulator, while hybrid uncertainties result in worse kinematic accuracy and more complex dynamic behaviors.

Investigation and Modeling of Combined Feedforward and Feedback Control Schemes to Improve the Performance of Differential Mode Active EMI Filters in AC–DC Power Converters

In this paper, the models of active differential mode electromagnetic interference input filters for ac–dc converters are first investigated. The models are developed for active filters with single feedback (FB) and single feedforward (FF) configurations, which include an active filter, passive filters, and the noise model for a converter. To increase the gain of the single active filter, the models for FB–FF, FF–FB, parallel FB, and series feedback (SFB) configurations are developed and investigated. Loop gains and insertion gains for all of these configurations are derived and compared. Stability has been analyzed based on the loop gains and a compensation technique is introduced to ensure stability. Based on the comparison among all these control configurations, it has been found that the SFB configuration can achieve the highest noise reduction. The loop gains and insertion gains of an active filter prototype with the SFB configuration are measured using a network analyzer to validate the developed model and compensation technique. Finally, spectrum measurements with a commercial boost power factor correction ac–dc converter have been conducted to verify the developed modeling and control techniques as well as the predicted performance for the SFB configuration.

An adaptive PID control method to improve the power tracking performance of solar photovoltaic air-conditioning systems

In order to increase the utilization of solar energy to lower the effect of photovoltaic power output fluctuations on power grids, an adaptive PID control method to improve the power tracking performance of solar photovoltaic air-conditioners is proposed in this paper. In this method, a dynamic temperature set point of the indoor zone is generated at each control time step based on the difference between the air-conditioning load and photovoltaic generation. The theoretical analysis shows that the proposed control is essentially a feedforward feedback control where the air-conditioning system can predictably adjust the compressor frequency based on the knowledge of how the solar radiation affects the cooling load. A case study in Shanghai shows that the proposed control method is able to significantly improve the power tracking performance of the photovoltaic air-conditioner, where the Solar Fraction (SF) and the Self-Consumption Ratio (SCR) are improved from 82.74% to 88.11% and from 70.12% to 74.42% respectively, without violating the indoor thermal comfort, where Mean Absolute Error (MAE) from the temperature set point is decreased from 0.197 to 0.168. The tuning of the power difference factor ko will lead to monotonic changes of SF &SCR and a minimum MAE. When ko is well tuned, the proposed control can realize desirable system improvements of both the power tracking ability and the indoor temperature control accuracy. The idea of this paper could be easily modified to benefit other solar cooling applications and conventional air-conditioners.

Study on multi-loop control strategy of three-shaft gas turbine for electricity generation

PurposeThis paper aims to develop a dynamic performance model of three-shaft gas turbine for electricity generation and to study a multi-loop control strategy of three-shaft gas turbine for electricity generation.Design/methodology/approachIn this paper, the dynamic performance model of the three-shaft gas turbine is established and developed. A novel approach, variable partial differential coefficient deviation linearization method is used to simulate the dynamic performance of the three-shaft gas turbine. Single-loop control system, feed-forward feedback control system and cascade system are assessed to control the engine during transient operation.FindingsA novel approach, variable partial differential coefficient deviation linearization method is used to simulate the dynamic performance of the three-shaft gas turbine. According to the results shown, the cascade control system is most satisfactory due to its fastest response and the best stability and robustness.Originality/valueThe method of variable partial linearization is adopted to make the dynamic simulation of the model achieve higher precision, better steady state and less computation time. This paper provides a theoretical study for the multi-loop control system of a marine three-shaft gas turbine.

Optimal Preview Control for Linear Discrete-Time Periodic Systems

In this paper, the optimal preview tracking control problem for a class of linear discrete-time periodic systems is investigated and the method to design the optimal preview controller for such systems is given. Initially, by fully considering the characteristic that the coefficient matrices are periodic functions, the system can be converted into a time-invariant system through lifting method. Then, the original problem is also transformed into the scenario of time-invariant system. Later on, the augmented system is constructed and the preview controller of the original system is obtained with the help of existing preview control method. The controller comprises integrator, state feedback, and preview feedforward. Finally, the simulation example shows the effectiveness of the proposed preview controller in improving the tracking performance of the close-loop system.

Control strategy of optimal deployment for spacecraft solar array system with initial state uncertainty

A control strategy combining feedforward control and feedback control is presented for the optimal deployment of a spacecraft solar array system with the initial state uncertainty. A dynamic equation of the spacecraft solar array system is established under the assumption that the initial linear momentum and angular momentum of the system are zero. In the design of feedforward control, the dissipation energy of each revolute joint is selected as the performance index of the system. A Legendre pseudospectral method (LPM) is used to transform the optimal control problem into a nonlinear programming problem. Then, a sequential quadratic programming algorithm is used to solve the nonlinear programming problem and offline generate the optimal reference trajectory of the system. In the design of feedback control, the dynamic equation is linearized along the reference trajectory in the presence of initial state errors. A trajectory tracking problem is converted to a two-point boundary value problem based on Pontryagin’s minimum principle. The LPM is used to discretize the two-point boundary value problem and transform it into a set of linear algebraic equations which can be easily calculated. Then, the closed-loop state feedback control law is designed based on the resulting optimal feedback control and achieves good performance in real time. Numerical simulations demonstrate the feasibility and effectiveness of the proposed control strategy.

Data-based approach for feedback–feedforward controller design using closed-loop plant data

Feedforward control of measurable disturbances is a useful complement to feedback control because feedforward control performs control actions before a disturbance response occurs in a process output. In contrast to the typical model-based design approach, a novel data-based method for designing both the feedback and feedforward controllers is presented in this paper. The controller design directly exploits closed-loop plant data and does not require an identification of process and disturbance models. A feedback proportional–integral–derivative controller and feedforward lead-lag compensator are sequentially designed on the basis of the closed-loop response data for a set-point change and for a disturbance input, respectively. Because the controller design process uses plant data integrals, the proposed method is robust against measurement noise. Moreover, the proposed design method can be applied to improve existing underperforming feedback and feedforward controllers using routine closed-loop operating data. Simulation studies demonstrated that the proposed method outperforms existing methods in designing a feedback–feedforward control system.