Feedforward-feedback Control Research Articles

Feedforward–Feedback Controller Based on a Trained Quaternion Neural Network Using a Generalised HR Calculus with Application to Trajectory Control of a Three-Link Robot Manipulator

This study derives a learning algorithm for a quaternion neural network using the steepest descent method extended to quaternion numbers. This applies the generalised Hamiltonian–Real calculus to obtain derivatives of a real–valued cost function concerning quaternion variables and designs a feedback–feedforward controller as a control system application using such a network. The quaternion neural network is trained in real-time by introducing a feedback error learning framework to the controller. Thus, the quaternion neural network-based controller functions as an adaptive-type controller. The designed controller is applied to the control problem of a three-link robot manipulator, with the control task of making the robot manipulator’s end effector follow a desired trajectory in the Cartesian space. Computational experiments are conducted to investigate the learning capability and the characteristics of the quaternion neural network used in the controller. The experimental results confirm the feasibility of using the derived learning algorithm based on the generalised Hamiltonian–Real calculus to train the quaternion neural network and the availability of such a network for a control systems application.

Intelligent feedforward hysteresis compensation and tracking control of dielectric electro-active polymer actuator

Dielectric electro-active polymer (DEAP) actuator has been considered potentially in recent decades for many applications, especially in intelligent bio-inspired robotics. However, the viscoelastic properties including rate-dependent and asymmetrical hysteresis, creep and the uncertainties under different operating conditions are still limiting its further development. In this paper, a feedforward-feedback tracking control approach is developed. Firstly, a long short term memory (LSTM) neural network combined with empirical mode decomposition (EMD), which has the information of reference as input and the control signal as output, is constructed using the data collected from the DEAP actuator. Thus, the well trained LSTM model can precisely capture the inverse hysteresis dynamics of the DEAP actuator, which can be used as a feedforward compensator to eliminate the hysteresis nonlinearities. Then, a conventional proportional-integral-derivative feedback controller is combined to compensate for the uncertainties and creep effect. To verify the effectiveness of the proposed feedforward compensator, comparative experiments on prediction of control signal and compensation of hysteresis among the traditional artificial back propagation neural network model, the inverse rate-dependent Prandtl-Ishlinskii model and the proposed LSTM-based compensator are conducted. The results validate that the LSTM-based compensator can precisely predict the control signal and eliminate the hysteresis with best performance indexes. Moreover, the tracking control experiments further validate the effectiveness of the proposed feedforward-feedback approach.

Positional Accuracy Improvement of an Industrial Robot Using Feedforward Compensation and Feedback Control

Abstract Industrial robots play an important role in the transformation and upgrading of aviation manufacturing technology by their advantages of high flexibility, high efficiency, and low costs compared to the conventional computerised numerical control machine. However, due to the serial structure design of the main body, the industrial robot has low absolute positional accuracy, which limits its popularization and application in the field of high-precision manufacturing. The purpose of this paper is to develop a robot accuracy improvement method using feedforward compensation and joint closed-loop feedback correction considering the influence of joint backlash. The experimental results show that the positional error of the compensated robot by the proposed method is reduced from 0.76 mm to 0.18 mm under no-load conditions, and the hole's positional accuracy is to 0.25 mm in the robotic drilling experiment.

A Disturbance-Observer-Based Feedforward-Feedback Control Strategy for Driveline Launch Oscillation of Hybrid Electric Vehicles Considering Nonlinear Backlash

Sudden torque change caused by the TM (traction motor) of HEV (hybrid electric vehicle) can lead to severe driveline oscillation, furthermore the backlash between engaging components in the driveline could increase the vibration amplitude. This paper focuses on the driveline oscillation of a series-parallel hybrid vehicle on launch condition. An analytic model of driveline torsional vibration considering disturbance brought by nonlinear backlash is established and simplified for the convenience of controller design. Based on the simplified model, a feedforward control strategy is proposed to suppress HEV launch vibration. However, the control effect will deteriorate sharply with the increase of equivalent backlash in the driveline system. To improve vibration attenuation performance, a feedback compensator is established and incorporated into the control system, with a robust sliding mode observer which is designed to estimate unknown disturbances as feedback signal. Numerical simulation is applied to validate vibration attenuation performance of the feedforward-feedback hybrid control strategy. Simulation results demonstrate that the feedforward-feedback control strategy has better robustness to cancel the unwanted effects of disturbance brought by nonlinear backlash compared with other control methods, which can be regarded as an effective approach to attenuate driveline oscillation and thus improve ride comfort on launch condition.

Information-Aware Lyapunov-Based MPC in a Feedback-Feedforward Control Strategy for Autonomous Robots

This letter proposes a feedback-feedforward control scheme that combines the benefits of an online active sensing control strategy (the feedforward control component) to maximize the information needed for correctly executing the desired task, with a Lyapunov-based control strategy (the feedback control component) that guarantees an asymptotic convergence towards the task itself. To quantify the amount of the collected information along the planned trajectories, the smallest eigenvalue of the Constructability Gramian is adopted as a metric and optimized, for generating the feedforward control component, within a Lyapunov-based Model Predictive Control framework (LMPC). The latter indeed allows to systematically handle the closed-loop stability and robustness properties of a Lyapunov-based nonlinear control law, and, at the same time, to reduce the estimation uncertainty and, thus, increase the task execution performance. To show the effectiveness of our method, we consider three case studies where a unicycle equipped with suitable onboard sensors has to perform three classical tasks in mobile robotics: path following, point-to-point motion, and trajectory tracking.

Flow-Induced Force Modeling and Active Compensation for a Fluid-Tethered Multirotor Aerial Craft during Pressurised Jetting

This paper presents an investigation of the fluid–structure interaction (FSI) effects on the stability of a quadrotor attached to a flexible hose conveying and ejecting pressurised fluid from an onboard nozzle. In this study, an analytical solution is derived to obtain the time and spatial responses of the free end, which could affect the quadrotor’s stability. First, the flow-induced force model was simulated at the hose plane to find out the contributing disturbances prior to the physical connection with the unmanned aerial vehicle (UAV). Thereafter, the flow-induced forces were introduced to the UAV dynamics model as disturbances to study the FSI response during flight. Physical experiments were conducted to compare the analytical responses of the UAV prior to and during ejection. The presented findings of the perturbations due to the FSI effect from the pressurised fluid flowing through the flexible hose to the free end and the jet reaction at the UAV nozzle will be used for the employment of a combined feedforward-feedback (FF-FB) quadrotor control strategy for a stable ejection phase. The proposed strategy shows an average improvement of 61.14% (x-axis) and 22.46% (z-axis) in terms of active position compensation during ejection as compared to a standard feedback (FB) control loop only.

Design of Active Continuous Variable Transmission Control System with Planetary Gear

Planetary gearboxes have been employed in various applications due to their advantages of maintaining a high speed-reduction ratio, such as in wind power applications which have now become a prevailing green energy resource. A traditional power transmission machine of the wind turbine has a fixed gear-ratio mechanical gearbox for speed-increasing transmission. The purpose of this study is to propose an active continuous variable transmission (ACVT) control system with a planetary gear to apply to the wind turbine. The planetary gear holds three terminals, i.e., the ring gear, the planet carrier, and sun gear, and the motions of three terminals can be controlled purposely by the servomotors to achieve ACVT. The three different transmission types of the proposed ACVT can be operated. The dynamic characteristic of the planetary gear is expressed in block diagram form, and a pseudo derivative feedback feed-forward controller of the velocity control loop is designed for the required performance. The results can be used to verify the effect of the proposed ACVT with the planetary gear.

Feedforward-Feedback Based Adaptive Control Algorithm for Power Quality Improvement of Grid Integrated Photovoltaic System

Integration of renewable energy systems such as wind and solar energy with the regular grid via inverters has shown to be a dependable approach for delivering end customers with consistent and high-quality power. Controlling and synchronizing generated power with the grid requires integrating the control algorithm in a Grid Integrated Photovoltaic (GIPV) system. A feedforward-feedback (FFFB) based adaptive control technique for the 3-stage dual-stage GIPV system is proposed in this research. The fuzzy logic controller is employed for DC-link voltage feedback control in the proposed feedforward-feedback adaptive control algorithm, and an additional feedforward loop is introduced to improve the GIPV system's performance. The efficiency of the suggested control algorithm in managing inverter operation while maintaining power quality is demonstrated. Furthermore, the system response of providing reactive power compensation, harmonic compensation, and load balancing for varied loads is explored using MATLAB and the Simulink tools.

Research on Tension Control System of Reciprocating Winding

When the enameled wire is winded onto the poles of the motor stator or rotor, the winding quality hugely relies on the control precision of the tension. Therefore, it is necessary to control the tension of the enameled wire in winding process. A tension control system is built with single chip microcomputer, the encoder and the servo motor. The PID feedback controller and feedforward controller are combined to form feedforward feedback controller, which using feedback information of swing angle deviation and feedforward information of wire frame position to adjust the pay off speed dynamically and control tension of enameled wire further. A procedural experimental modelling method is discussed in order to identify the feedforward model. The experiment is performed, it is found that in the typical situation of setting tension 1500 g, the tension fluctuation rate of the PID controller with feedforward model is only 2%, which is far better than that of pure PID controller with a fluctuation rate of 14%. The result shows that the proposed experimental modelling method hosts the characteristics of good accuracy, universality and applicability.

Adaptive sliding mode control with hysteresis compensation-based neuroevolution for motion tracking of piezoelectric actuator

In this paper, an adaptive sliding mode control with hysteresis compensation-based neuroevolution (ACNE) is proposed for precise motion tracking of the piezoelectric actuator (PEA) in the presence of uncertainties, disturbances, and nonlinearity hysteresis characteristics. Firstly, a new memetic differential evolution (MeDE) algorithm is proposed to optimize the weights of a 3-layer neural network (called neuroevolution or NE). In MeDE, a differential evolution algorithm is used as a global search scheme and the Jaya algorithm is used as local search exploitation. Secondly, an inverse hysteresis model of PEA is identified by the neuroevolution model to provide a feed-forward NE control signal to compensate for the hysteresis behavior of PEA system. Thirdly, an adaptive neural sliding mode control plus feedforward NE (ACNE) control is designed to enhance the quality control and guarantee asymptotical stability for PEA system. Based on the Lyapunov method, the stability of the closed-loop system is analyzed and proved. Finally, the experimental Thorlabs piezoelectric actuator (PEA) is set up to verify the robustness and effectiveness of the proposed approach. Results show that the identified MeDE-Neuroevolution model has successfully applied to model the inverse hysteretic of PEA system and the performance of MeDE has better than Jaya, DE, and PSO in terms of best, worst, average, and standard deviation. Furthermore, in motion tracking control, the performance of proposed ACNE control has more accurate than a classical PID control, a feedforward control, a hybrid feedback–feedforward control, and an adaptive neural sliding mode control without a compensator.

A feedforward-feedback-based reactor power decoupling control strategy for multi-modular nuclear power plants

Multi-modular nuclear power plants (MMNPPs) comprise of several SMRs working in parallel, a common steam header and one or more steam consuming units. The reactor units in this type of plant may share different portion of load and their power may interfere with each other due to a coupling effect through the common steam header. Hence, the load following characteristics of MMNPPs are different from that of single reactor plants. Investigation results show that the coupling effect between load following reactors (LFRs) and fixed power reactors (FPRs) affects the operation of the latter ones during load following or power compensation process. This is not favorable due to the extra movement of the control rods and valves may increase the risk of related accidents. In this research, a setpoint feedforward-feedback header steam pressure control method is proposed to decouple the power output of LFR units and FPR units. Control performance of the decoupling control method is evaluated and made comparison to that of conventional header steam pressure control method. Results demonstrated that the proposed reactor power decoupling control method shows better control performance than the conventional control method, with faster load following speed of the LFR and smaller overshoots of the header steam pressure, eventually keeping the FPR barely disturbed during the transients. This proposed reactor power decoupling control strategy will improve the operation performance, safety and reliability of the MMNPPs.

Combined Feedback Feedforward Control of a 3-Link Musculoskeletal System Based on the Iterative Training Method.

The investigation and study of the limbs, especially the human arm, have inspired a wide range of humanoid robots, such as movement and muscle redundancy, as a human motor system. One of the main issues related to musculoskeletal systems is the joint redundancy that causes no unique answer for each angle in return for an arm's end effector's arbitrary trajectory. As a result, there are many architectures like the torques applied to the joints. In this study, an iterative learning controller was applied to control the 3-link musculoskeletal system's motion with 6 muscles. In this controller, the robot's task space was assumed as the feedforward of the controller and muscle space as the controller feedback. In both task and muscle spaces, some noises cause the system to be unstable, so a forgetting factor was used to a convergence task space output in the neighborhood of the desired trajectories. The results show that the controller performance has improved gradually by iterating the learning steps, and the error rate has decreased so that the trajectory passed by the end effector has practically matched the desired trajectory after 1000 iterations.

Modelling and simulation of dual heating of substrate with centralized temperature control for anaerobic digestion process

Biogas is a gaseous biofuel derived from the anaerobic digestion of waste products and is used in automobiles, residential heating, power generation, etc. The production of biogas depends on many factors, but temperature affects it most and plays a very crucial role. Therefore, any deviation from the optimum temperature is the main cause of concern in these plants. Temperature disturbance due to insertion of fresh feed and heat loss is very usual in biogas generation plants. Commonly, a single heating strategy along with PID based temperature control scheme is used in these plants. This paper focuses on the implementation of a dual heating strategy to handle temperature disturbances and heat loss. In addition, a blend of the feedforward-feedback control scheme is proposed to make the control robust, efficient and fast. Furthermore, a novel, Fuzzy-dual PID controller is also proposed to make the overall control centralized. The effectiveness of the proposed system has been tested at various realistic operating conditions. It is observed that the proposed dual heating with fuzzy-PID controller reduces the peak overshoot percentage, rise time and settling time for the digester temperature control. Results also prove the capability of the proposed fuzzy-PID controller in handling temperature disturbances. The overall system is modelled and simulated in a MATLAB/Simulink environment.

The impact of objective functions on control policies in closed-loop control of grasping force with a myoelectric prosthesis

Objective. Supplemental sensory feedback for myoelectric prostheses can provide both psychosocial and functional benefits during prosthesis control. However, the impact of feedback depends on multiple factors and there is insufficient understanding about the fundamental role of such feedback in prosthesis use. The framework of human motor control enables us to systematically investigate the user-prosthesis control loop. In this study, we explore how different task objectives such as speed and accuracy shape the control policy developed by participants in a prosthesis force-matching task. Approach. Participants were randomly assigned to two groups that both used identical electromyography control interface and prosthesis force feedback, through vibrotactile stimulation, to perform a prosthesis force-matching task. However, the groups received different task objectives specifying speed and accuracy demands. We then investigated the control policies developed by the participants. To this end, we not only evaluated how successful or fast participants were but also analyzed the behavioral strategies adopted by the participants to obtain such performance gains. Main results. First, we observed that participants successfully integrated supplemental prosthesis force feedback to develop both feedforward and feedback control policies, as demanded by the task objectives. We then observed that participants who first developed a (slow) feedback policy were quickly able to adapt their policy to more stringent speed demands, by switching to a combined feedforward-feedback control strategy. However, the participants who first developed a (fast) feedforward policy were not able to change their control policy and adjust to greater accuracy demands. Significance. Overall, the results signify how the framework of human motor control can be applied to study the role of feedback in user-prosthesis interaction. The results also reveal the utility of training prosthesis users to integrate supplemental feedback into their state estimation by designing training protocols that encourage the development of combined feedforward and feedback policy.

An enhanced feedback-feedforward control scheme for process industries

Abstract In this article, a unified control scheme is proposed for dead-time compensation and disturbance rejection via feedback and feedforward controller. The objectives of this work are suggested in two folds, first tuning of fractional order feedback controller via delayed Bode’s ideal transfer function instead of conventional Bode’s ideal transfer function with the benefits of dead time compensator and second feedforward controller for disturbance rejection. An existing method is utilized for comparison with the proposed scheme. To examine the efficacy of the proposed method robustness test is also carried out via sensitivity analysis. For quantifiable evaluation of the proposed scheme Integral Absolute Error (IAE) and Integral Square Error (ISE) are utilized. For the usefulness of the proposed scheme, two practical problems are demonstrated in this paper. The limpidity and instinctive appeal of the proposed scheme make it beautiful for industrial applications.

Iterative Data-Driven Control for Closed Loop with Two Unknown Controllers

Iterative idea is combined with data-driven control and is used to design the feedforward controller and feedback controller simultaneously. Consider one closed loop system with two controllers, the classical model-based control holds on the condition of known plant. To alleviate the modeling process for plant, data-driven control is applied to design the two controllers. After these two controllers are parametrized by two unknown parameter vectors, the iterative idea is introduced to identify these two parameter vectors. Furthermore, for more general case of controllers, the closed relations between controllers and expected transfer functions are derived. Then, the iterative idea is also introduced to achieve the controller design. To be of benefit for latter stability analysis, some equities are derived for output-input sensitivity functions with three kinds of disturbances. Generally, after formulating the problem of the controller design as one model-matching problem, the purpose of this paper is threefold. First, we derive that, in case of two parametrized controllers, the iterative idea is performed to identify these two unknown parameter vectors, even when parameters converge to their true values. Second, we show how to design the two controllers iteratively for more general forms and find the closed relations between these controllers and expected closed loop transfer functions. Third, we provide some heuristic considerations on output-input sensitivity functions, which are of benefit for our stability analysis on data-driven control. Finally, one example is given to show the feasibility of our proposed theories.

Stochastic optimal feedforward-feedback control determines timing and variability of arm movements with or without vision.

Human movements with or without vision exhibit timing (i.e. speed and duration) and variability characteristics which are not well captured by existing computational models. Here, we introduce a stochastic optimal feedforward-feedback control (SFFC) model that can predict the nominal timing and trial-by-trial variability of self-paced arm reaching movements carried out with or without online visual feedback of the hand. In SFFC, movement timing results from the minimization of the intrinsic factors of effort and variance due to constant and signal-dependent motor noise, and movement variability depends on the integration of visual feedback. Reaching arm movements data are used to examine the effect of online vision on movement timing and variability, and test the model. This modelling suggests that the central nervous system predicts the effects of sensorimotor noise to generate an optimal feedforward motor command, and triggers optimal feedback corrections to task-related errors based on the available limb state estimate.

Control of a Rehabilitation Robotic Device Driven by Antagonistic Soft Actuators

Stroke is becoming a widely concerned social problem, and robot-assisted devices have made considerable contributions in the training and treatment of rehabilitation. Due to the compliance and continuous deformation capacity, rehabilitation devices driven by soft actuators are attached to widespread attention. Considering the large output force of pneumatic artificial muscle (PAM) and the biological musculoskeletal structure, an antagonistic PAM-driven rehabilitation robotic device is developed. To fulfill the need for control of the proposed device, a knowledge-guided data-driven modeling approach is used and an adaptive feedforward–feedback control approach is presented to ensure the motion accuracy under large deformation motion with high frequency. Finally, several simulations and experiments are carried out to evaluate the performance of the developed system, and the results show that the developed system with the proposed controller can achieve expected control performance under various operations.

Active Loading Control Design for a Wearable Exoskeleton with a Bowden Cable for Transmission

Exoskeletons with a Bowden cable for power transmission have the advantages of a concentrated mass and flexible movement. However, their integrated motor is disturbed by the Bowden cable’s friction, which limits the performance of the force loading response. In this paper, we solve this problem by designing an outer-loop feedforward-feedback proportion-differentiation controller based on an inner loop disturbance observer. Firstly, the inner loop’s dynamic performance is equivalent to the designed nominal model using the proposed disturbance observer, which effectively compensates for the parameter perturbation and friction disturbance. Secondly, based on an analysis of the stability of the inner loop controller, we obtain the stability condition and discuss the influence of modeling errors on the inner loop’s dynamic performance. Thirdly, to avoid excessive noise from the force sensors being introduced into the designed disturbance observer, we propose the feedforward-feedback proportion-differentiation controller based on the nominal model and pole configuration, which improves the outer loop’s force loading performance. Experiments are conducted, which verify the effectiveness of the proposed methods.

Hybrid controller of a linear piezoelectric walking stage relying on stack/shear piezoelectric actuators

This paper proposes a hybrid control strategy of a novel linear piezoelectric walking stage based on two sorts of piezoelectric actuators, which takes the load variation into account. The proposed stage consists of two parallel 4-bar lever amplification mechanisms with flexure hinges actuated by piezoelectric stacks to heighten the vertical distance (that is more tolerable to the assembly discrepancy), two compression springs (that is able to maintain a fixed linear position without powering), and two shear piezoelectric actuators (that can achieve longer and equivalent to walking motion) in a small form factor. The proposed stage has two operating modes, namely a coarse positioning mode with a more extensive travel range and a fine positioning mode with a nanometer-level resolution, to possess excellent performance for the linear piezoelectric walking stage of load variations. One multimodal switching controller and one feedforward-feedback controller conduct the coarse mode and fine mode, respectively. The optimal frequency for a specific load is obtained through a backpropagation neural network in the multimodal switching control. In the feedforward-feedback control, the inverse mathematical model based on the Bouc-Wen hysteresis model is used to mitigate the hysteresis effect in the feedforward part while the proportional–integral–derivative controller in the feedback part handles the external system disturbances. Experimental results show the proposed hybrid coarse/fine mode control strategy's effectiveness to satisfy an efficient and accurate positioning task.