Feedforward-feedback Control Research Articles

An integrated feedforward-feedback control structure utilizing a simplified global gravitational search algorithm to control nonlinear systems

This paper presents an integrated feedforward-feedback control structure to control nonlinear dynamical systems. This intelligent control system exploits a modified recurrent wavelet neural network (MRWNN) in the feedforward (FF) and the feedback (FB) loops of the control structure. Specifically, the MRWNN is proposed to boost the approximation performance of a previously reported network by employing two amendments to the original structure. To optimize the parameters of both the FF and the FB controllers, an enhanced version of the gravitational search algorithm (GSA) is developed to improve the searching capability of the original algorithm. In particular, two modifications were adopted, including the removal of two control parameters related to the gravitational constant in the original algorithm and the utilization of the global best solution to constitute the next generation of agents. Hence, the proposed algorithm is called the simplified global gravitational search algorithm (SGGSA), which has demonstrated better optimization performance compared to those of other techniques, including the original GSA. By conducting several evaluation tests using different nonlinear time-variant dynamical systems, the effectiveness of the proposed control structure was confirmed in terms of control precision and robustness against external disturbances. In addition, the MRWNN has exhibited a superior control performance compared with other related controllers.

Improving model-based compensation method for real-time hybrid simulation considering error of identified model

Time delay is a critical and unavoidable problem in real-time hybrid simulation. An accurate and effective compensation method for time delay is necessary for the safety of real-time hybrid simulation and the reliability of test results. Generally, a model-based compensation method can be adopted, which is derived from the identified transfer function by assuming the latter can accurately represent the real plant. However, there must be some differences between the transfer function and the real plant. To facilitate the development of real-time hybrid simulation, we proposed a two-stage feedforward compensation method considering the error between the transfer function identified and the real plant. The compensation strategy proposed in this study was not only based on the transfer function but also introduced an error model as a second-stage compensation into a compensator to realize the synchronization of command and measurement. To verify the efficiency of the proposed method, comparisons in time domain and frequency domain with the feedforward compensator in a model-based feedforward–feedback control method were carried out. Compared with the feedforward compensator, the two-stage method achieved better tracking performance, especially in the high-frequency bandwidth. The test results verified that for a band-limited white noise of 0–30 Hz, the phase lag of the actuation system can be limited to ±5°. Finally, the two-stage method was applied to a real-time hybrid simulation of a two-story frame to illustrate its compensation effect on time delay.

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.

Fast finite-time tracking control for multi-agent systems with mismatched disturbances under directed communication graphs

In this paper, the fast finite-time consensus problem on multi-agent systems is considered. Multi-agent systems are affected by mismatched disturbances and the communication graph is directed. By a sliding mode observer and a fast nonsingular terminal sliding mode control method, the feedforward-feedback controller is presented for multiple second-order systems and multiple high-order systems to achieve consensus, respectively. The proposed control method can provide short convergence time and improved control input without strong chattering and singularity phenomenon. Finally, simulations are given to verify the availability of the methods.

Real-Time Hybrid Testing of Strut-Braced Wing Under Aerodynamic Loading Using an Electrodynamic Actuator

Real-Time Hybrid Simulation (RTHS) is an experimental framework that allows the testing of components or substructures under realistic, dynamic boundary conditions, by imposing the reactions calculated from a model of the rest of the assembly through one or more actuators. In the context of rapid prototyping of mechanical components, RTHS could be used to explore the design space of a device while at the same time physically validating its interaction with other components of the final assembly from the early stages of the design-to-production cycle. In this work, RTHS was applied for the first time to the investigation of aerodynamic gust loading alleviation devices in a highly flexible strut-braced wing. The model wing was taken as the physical substructure and tested in a low-speed wind tunnel equipped with gust generators. The load alleviation device was simulated through a real-time feedforward-feedback controller, and its response imposed via an electro-mechanical linear actuator, in contrast with the hydraulic actuators more commonly used in standard RTHS. The controller-actuator subsystem was studied in detail to assess and minimise errors at the physical interface with the wing. The behaviour of the electromechanical subsystem showed a strong dependence on the characteristics of the numerical substructure and the frequency of excitation, and resulted in a significant discrepancy between the simulated and real displacements at the interface between the actuator and the wing. A narrow-band feedforward displacement control scheme based on a model of this subsystem alone was therefore developed and shown to significantly reduce synchronisation errors at the interface.

Feedforward control for wave disturbance rejection on floating offshore wind turbines

Floating offshore wind turbines allow wind energy to be harvested in deep waters. However, additional dynamics and structural loads may result when the floating platform is being excited by wind and waves. In this work, the conventional wind turbine controller is complemented with a novel linear feedforward controller based on wave measurements. The objective of the feedforward controller is to attenuate rotor speed variations caused by wave forcing. To design this controller, a linear model is developed that describes the system response to incident waves. The performance of the feedback-feedforward controller is assessed by a high-fidelity numerical tool using the DTU 10MW turbine and the INNWIND.EU TripleSpar platform as references. Simulations in the presence of irregular waves and turbulent wind show that the feedforward controller effectively compensates the wave-induced rotor oscillations. The novel controller is able to reduce the rotor speed variance by 26%. As a result, the remaining rotor speed variance is only 4% higher compared to operation in still water.

Cooperative ecological cruising using hierarchical control strategy with optimal sustainable performance for connected automated vehicles on varying road conditions

Facing the increasingly severe energy and environmental problems, platooning of multiple vehicles has great potential for sustainability of intelligent transportation systems. This study develops a novel hierarchical ECACC strategy with energy-saving achievements by conducting the ecological velocity trajectory planning of the leading vehicle based on dynamic programming and car-following driving of the rest of the vehicles in the platoon based on combined feedforward-feedback control. The main purpose is to reduce the energy consumption of multiple vehicles in intelligent transportation systems with consideration of trip time and car-following performance. The optimal velocity planning uses an innovative integrated longitudinal and lateral vehicle dynamics approach where both the lateral stability and energy efficiency in continuous curved roads are guaranteed at the same time. The results show that the proposed ECACC in energy-optimal mode can reduce energy consumption by up to 38.1% compared to conventional energy-optimal fixed speed cruising. Moreover, the connected automated vehicle platoon presents better car-following performance with string stability as opposed to the Cooperative Adaptive Cruise Control based on feedback control and model predictive control. The results indicate the great potential of the proposed ECACC strategy to further improve the sustainability of intelligent transportation systems.

Direct Yaw-Moment Control of Electric Vehicle With in-Wheel Motor Drive System

With the development of redundant driving system, vehicles' maneuverability and stability have great optimized potential. In this research, an electric off-road vehicle is served as controlled objective and a dynamic direct yaw-moment controller is proposed to realize the smooth accommodation of vehicle longitudinal-lateral motion under various transport conditions via controlling In-wheel motor (IWM) drive system. A data fusion technology is employed to enhance the control accuracy and responsiveness via amending the metrical data of INS and GPS primarily, and the discrete measured information are transmitted to a unscented particle filter with fine resampling (UPF-FR) that estimates the sideslip angle and yaw rate synchronously. Then, a feedforward-feedback control system is constructed to improves the controlled vehicle's maneuverability and stability based on the calculated active yaw moment. It is noteworthy that a nonlinear finite time feedback control system is utilized to acquire minimum error between the actual vehicle status and the desired vehicle status. After that, the IWM drive system execute the active yaw moment through modulating every motor's alternate current independently. In the end, some specific simulation and real tests are conducted to verify the performance of hierarchical control system.

Control of Specific Growth Rate in Fed-Batch Bioprocesses: Novel Controller Design for Improved Noise Management

Accurate control of the specific growth rate (µ) of microorganisms is dependent on the ability to quantify the evolution of biomass reliably in real time. Biomass concentration can be monitored online using various tools and methods, but the obtained signal is often very noisy and unstable, leading to inaccuracies in the estimation of μ. Furthermore, controlling the growth rate is challenging as the process evolves nonlinearly and is subject to unpredictable disturbances originating from the culture’s metabolism. In this work, a novel feedforward-feedback controller logic is presented to counter the problem of noise and oscillations in the control variable and to address the exponential growth dynamics more effectively. The controller was tested on fed-batch cultures of Kluyveromyces marxianus, during which μ was estimated in real time from online biomass concentration measurements obtained with dielectric spectroscopy. It is shown that the specific growth rate can be maintained at different setpoint values with an average root mean square control error of 23 ± 6%.

Distributed Finite-Time Optimization for Disturbed Second-Order Multiagent Systems.

In this article, the distributed finite-time optimization problem is investigated for second-order multiagent systems with disturbances. To solve this problem, a feedforward-feedback composite control framework is established, which contains two main stages. In the first stage, for disturbed second-order individual systems with generally strongly convex cost functions, a composite finite-time optimization control scheme is proposed based on the combination of adding a power integrator and the finite-time disturbance observer techniques and the use of the cost functions' gradients and Hessian matrices. In the second stage, based on the result of the first stage, a distributed composite finite-time optimization control framework is built for disturbed second-order multiagent systems with quadratic-like local cost functions. This framework involves a kind of finite-time consensus algorithm, some novel distributed finite-time estimators designed for each agent to estimate the velocity, the gradient and Hessian matrix for the local cost function of any other agent, and some optimization terms in the form of the optimization controllers proposed in the first stage and based on the estimates from the distributed estimators. The finite-time convergence of the closed-loop systems is rigorously proved. The simulation results illustrate the effectiveness of the proposed control framework.

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.

Design and analysis of an engine-start control strategy for a single-shaft parallel hybrid electric vehicle

The key point of a single-shaft parallel hybrid electric vehicle is the ability to start the engine while propelling the vehicle by utilizing a single traction motor. To pursue better driving comfort, an accurate engine-start process should be designed. For the purpose of smoothly starting the engine, this paper proposes a seven-phase engine-start control strategy with the assistance of the slipping status of the separating clutch and shifting clutch. A detailed drivetrain model of a single-shaft parallel hybrid electric vehicle is established to support the designed control strategy with consideration of the dynamic characteristics of the engine, motor and clutch. With the support of the hydraulic circuit established in this paper, feedforward-feedback controller based on proportional-integral (PI) algorithm is developed to track the demanded pressure for separating clutch. Simulation and test results validate that the proposed engine-start control strategy is feasible and effective for improving driving comfort and ensuring the drivability of the target vehicle. The key design parameters of the control strategy are optimized for the minimal equivalent fuel consumption. Test results indicate that the designed control strategy can effectively improve the fuel economy of engine-start control process compared with conventional strategy.

Distributed Finite-Time Optimization for Integrator Chain Multiagent Systems With Disturbances

In this article, the distributed finite-time optimization problem is studied for integrator chain multiagent systems with mismatched and matched disturbances and quadraticlike local cost functions. The agents’ models are permitted to be heterogeneous with different orders ranging from first-order to higher order forms. To solve the problem, a nonsmooth embedded control framework is established, which consists of two parts. In the first part, by using nonsmooth control theory and designing some distributed finite-time estimators to estimate the gradients of the agents’ local cost functions, a distributed finite-time optimal signal generator with fractional powers is constructed, of which the output signals converge to the minimizer of the global function in finite time. In the second part, by embedding the generator into the feedback loop, taking its output signals as the local optimal reference outputs for the agents, and combining nonsmooth control and finite-time disturbance observer techniques together, some feedforward-feedback composite tracking controllers are designed for the agents to track their local optimal reference outputs in finite time. Under the proposed control framework, all the agents’ outputs converge to the minimizer of the global cost function in finite time and the distributed finite-time optimization goal is achieved. Numerical simulations demonstrate the effectiveness of the proposed control framework.

A feedforward-feedback hybrid control strategy towards ordered utilization of concentrating solar energy

In this work, a feedforward-feedback hybrid control strategy was proposed for concentrating solar thermal technology. Inside, the feedforward unit was designed to counteract the measured disturbance (mainly originated from the changeable weather), and the feedback unit employing proportional-integral controller to counteract some unmeasured and non-linear disturbances, e.g. local cloud cover, local defocus and mirror damage. For disturbance-rejecting and setpoint-tracking regulating issues, the hybrid strategy is superior to the single control mode in improving responsiveness, reducing steady-state error, as well as weakening overshoot. A feedforward-relaxed hybrid scheme allows enough tolerance for feedforward implementation and simultaneously helps determine the optimal regulation. For a closed power plant, the regulated operation contributes to additional 0.5%–1% daily efficiency in four typical meteorological days. Above all, the high-performance control strategy in energy-supply side lays the foundation for scheduling and dispatching the available energy and leaves enough room for the global energy management and market balance. It is suggested to incorporate feedforward information to enhance regulation performance, meet multi-grade energy demands, as well as turn the feedback-dominated regulation tide.

Method of Synthetic Motion for Testing Single Particle Tracking Microscopes

The study of cells and molecules at the nanometer scale necessitates the development of methods that can localize and track single particles, collectively known as single particle tracking microscopes (SPTM). However successful these techniques are, it is difficult to compare them directly, especially as one is most interested in their performance on a specific experimental setup. In this work, we demonstrate the use of synthetic motion (SM) as a basis for testing, calibrating, optimizing, and comparing SPTM techniques. SM, first described by Michael Saxton, is a method of moving a fixed fluorophore along a trajectory, by a stochastic motion model realization. This allows a single particle to move with a known ground-truth in the acquired image of an SPTM, allowing for comparison to the estimated positions, trajectory, and motion model parameters. Having the ground truth then allows for detailed and precise experimental analysis and comparison of specific SPTMs with very different detection and signal processing. However, when implementing SM, one must take into account the physical limitations of the actuation modality used to achieve motion on the microscope. Piezo-actuated microscope stages make a great platform for SM due to its large actuation range, precise positioning, and high speeds. However, achieving accurate and precise positioning requires overcoming several challenges that arise due to the nature of the piezo-actuators. The effects of controller quantization, actuator slew rate, amplifier input slew rate, and bandwidth limitations on SM are analyzed. Additionally, a feedforward-feedback control system can increase the bandwidth of the stage while maintaining the precise and accurate motion needed for the fast acquisition rates of modern SPTMs and for producing intraframe motion-blur. With these challenges overcome, SM becomes a powerful tool for the experimental analysis of SPT algorithms, hardware, and microscope instrument designs.

Triiodothyronine secretion in early thyroid failure: The adaptive response of central feedforward control.

Defined by thyroid-pituitary feedback control, clinical diagnosis of hypothyroidism and hyperthyroidism has become synonymous with TSH measurement. We combined in silico analysis and in vivo data to explore the central influences on thyroidal T3 production. A system of five coupled first-order nonlinear parameterised ordinary differential equations (ODEs) is used to model the feedback control of TSH and TRH by thyroid hormones together with the feedforward control of thyroidal T3 secretion and enzymatic T4-T3 conversion. Dependencies of the stable equilibrium solutions of this ODE system, that is the homeostasis of the underlying physiological process, on the system parameters were investigated whether they accounted for clinical observations. During the modelled transition to hypothyroidism, central control imposed an increasing influence in maintaining serum FT3 levels, compared to peripheral conversion efficiency. Numerical continuation analysis revealed dependencies of T3 production on different elements of TSH feedforward control. While T4-T3 conversion provided the main T3 source in euthyroidism, this was overtaken by increasing glandular T3 secretion when thyroid reserve declined. The computational results were in good agreement with data from untreated patients with autoimmune thyroiditis. Dependencies revealed in the expression of control differ in thyroid health and disease, using a physiologically based mathematical model of combined feedback-feedforward control of the hypothalamic-pituitary-thyroid regulation. Strong T3-protective mechanisms of the control system emerge with declining thyroid function, when glandular T3 secretion becomes increasingly influential over conversion efficiency. This has wide-ranging implications for the utility of TSH in clinical decision-making.

Design of VRFT Based Feedback-feedforward Controllers for Enhancing Disturbance Rejection on Non-minimum Phase Systems

AbstractIn this work, Virtual Reference Feedback Tuning (VRFT) based feedback-feedforward controllers are designed for non-minimum phase systems to enhance the disturbance rejection. In model based design methods the feedforward controller is inverse of the plant model reduces the controller performance in the presence of uncertainties. The novelty of the work lies in to design VRFT based controllers independently for the feedforward structure, which decouples set point tracking and disturbance rejection. The optimal filter selection and algorithms to design the controllers were proposed with non-measurable disturbance signal using open loop experimental data. These controllers are applied to discrete time non-minimum phase Flexible Transmission System (FTS) with no load, half load and full load conditions. The simulation study on FTS with feedback and feedback plus feedforward structures evaluates the effectiveness of proposed controllers. The performance indices like Integral Absolute Error (IAE), Integral Square Error (ISE), Total Variance (TV), VRFT Objective Function (JVR) and Fragility Index (FI) are used to compare the controller’s performance. The simulation results indicate that proposed feedback plus feedforward controller is superior to feedback controller.

Optimal Estimation and Tracking Control for Variable-speed Wind Turbine with PMSG

Designing an optimal tracking controller and system observer is a nonlinear problem in the variable-speed wind energy conversion system (WECS). In this paper, an adjusted feedforward and feedback optimal controller with extended Kalman filter (EKF) is introduced to estimate and control the permanent magnet synchronous generator (PMSG) for the maximum power point tracking (MPPT) with several disturbances of the wind speed. An augmented model of wind turbine and PMSG is expanded, and then the parameters of the optimal controller and estimator are obtained. The dynamic stability of the closed-loop system with feedback-feedforward controller (FFC), EKF and speed controller are analyzed. To compare the dynamic performance of EKF and FFC with the conventional controllers, numerical results are demonstrated with the disturbances of the wind speed and faults in power grid.

Feedforward and H∞ Feedback Robotic Force Control in a 1-dof Physical Interaction Using a Nonlinear Human Model

Robot interacting with flexible object, and particularly with human in movement, is an increasing topic in several applications of today. It is particularly interesting and challenging in term of modeling and control because of the human variability, uncertainty and non-linearity. In the current work, we propose an original approach to model and control the interaction between a 1-dof robotic system and a human. While the human model includes a nonlinear biomechanical model, the nonlinearity is accounted and compensated for in a feedforward controller. Then a robust H∞ feedback control is added in order to allow the closed-loop satisfy some specified performances under possible uncertainties and under external disturbance such as noise. The feedforward-feedback control is afterwards extensively verified in simulation which confirm its efficiency.

A New VRFT Approach for IMC-PID Feedback-Feedforward Controller Design based on Robustness

This paper presents, design of feedback-feedforward controllers based on Internal Model Controller (IMC) strategy using data based Virtual Reference Feedback Tuning (VRFT) approach. For minimum phase discrete systems, some VRFT approaches have been proposed to design IMC based PID controllers in the literature. However, the approaches determine whether the system is robust or not. In this paper, a new VRFT approach is investigated. The IMC based PID controllers are designed by scaling controller tuning parameters for specified robustness in addition of non-measurable disturbance signal. Different control structures, based on feedback and feedback plus feedforward and IMC filter, are evaluated both concerning regulatory and servo control. The simulation results show that, the proposed method enhance the performance for feedforward structures with filter based on same degree of robustness in terms of Integral Square Error (ISE), Total Variance (TV), VRFT Objective Function (JVR).