Parameters Of Feedforward Controller Research Articles

Adaptive prediction for effluent quality of wastewater treatment plant: Improvement with a dual-stage attention-based LSTM network

The accurate effluent prediction plays a crucial role in providing early warning for abnormal effluent and achieving the adjustment of feedforward control parameters during wastewater treatment. This study applied a dual-staged attention mechanism based on long short-term memory network (DA-LSTM) to improve the accuracy of effluent quality prediction. The results showed that input attention (IA) and temporal attention (TA) significantly enhanced the prediction performance of LSTM. Specially, IA could adaptively adjust feature weights to enhance the robustness against input noise, with R2 increased by 13.18%. To promote its long-term memory ability, TA was used to increase the memory span from 96 h to 168 h. Compared to a single LSTM model, the DA-LSTM model showed an improvement in prediction accuracy by 5.10%, 2.11%, 14.47% for COD, TP, and TN. Additionally, DA-LSTM demonstrated excellent generalization performance in new scenarios, with the R2 values for COD, TP, and TN increasing by 22.67%, 20.06%, and 17.14% respectively, while the MAPE values decreased by 56.46%, 63.08%, and 42.79%. In conclusion, the DA-LSTM model demonstrated excellent prediction performance and generalization ability due to its advantages of feature-adaptive weighting and long-term memory focusing. This has forward-looking significance for achieving efficient early warning of abnormal operating conditions and timely management of control parameters.

Facilitated adaptation via structural learning increases bimanual interference.

Bimanual coordination is an essential feature of the motor system, yet interactions between the limbs during independent control remain poorly understood. Interference between the two hands, or the assimilation of movement characteristics between the two effectors, can be induced by perturbing one arm (e.g., via visuomotor rotation) and then measuring the effects in the contralateral limb. In this study, we sought to further determine the role adaptation plays in bimanual interference using a structural learning paradigm to alter feedback regulation in reaching. We trained healthy participants to counter 60 unique random rotations in right hand visual feedback over 240 reaches. Following this, we assessed feedforward and feedback measures of interference in a bimanual reaching task where the right hand was exposed to a fixed visual feedback rotation while the left hand reached without visual feedback. We found that participants who had been exposed to the structural training task in the right hand showed increased left hand interference during the first 20 trials of the test task. Moreover, interference was greater in feedback, rather than feedforward control parameters. The results further suggest that structural learning enhances bimanual interference via sensory feedback upregulation.

Mechanism analysis and suppression method of high frequency harmonic resonance in VSC-HVDC

This paper introduces the basic situation of high frequency harmonic resonance in Guangxi on April 10, 2017, which is based on the Luxi back-to-back voltage source converter based high voltage direct current transmission (VSC-HVDC) project in China. Firstly, the harmonic amplification mechanism and its key links in the event are analyzed in detail and verified by simulation. Secondly, the relationship among high frequency harmonic resonance, voltage feedforward control loop and system harmonic impedance is obtained, and the formulas among voltage feedforward control parameters, system harmonic impedance and high frequency harmonic resonance frequency amplitude are deduced. Then, a method to suppress high frequency harmonic resonance by adding a reasonably filter to the voltage feedforward control loop is proposed. Finally, the effectiveness of the proposed method in this paper is verified by simulation and field operation.

Rotation Angle Control Strategy for Telescopic Flexible Manipulator Based on a Combination of Fuzzy Adjustment and RBF Neural Network

The length of flexible manipulators with a telescopic arm alters during movement. The dynamic parameters of telescopic flexible manipulators exhibit significant time-varying characteristics owing to variations in length. With an increase in the manipulators’ length, the nonlinear terms caused by flexibility in the manipulators’ dynamic equations cannot be ignored. The time-varying characteristics and nonlinear terms of telescopic flexible manipulators cause fluctuations in rotation angles, which affect the operation accuracy of end-effectors. In this study, a control strategy based on a combination of fuzzy adjustment and an RBF neural network is utilized to improve the control accuracy of flexible telescopic manipulators. First, the dynamic equation of the manipulators is established using the assumed mode method and Lagrange’s principle, and the influence of nonlinear terms is analyzed. Subsequently, a combined control strategy is proposed to suppress the fluctuation of the rotation angle in telescopic flexible manipulators. The variation ranges of the feedforward PD controller parameters are determined by the pole placement strategy and length of the manipulators. Fuzzy rules are utilized to adjust the controller parameters in real-time. The RBF neural network is utilized to identify and compensate the uncertain part of the dynamic model of the flexible manipulators. The uncertain part comprises time-varying parameters and nonlinear terms. Finally, numerical simulations and prototype experiments prove the effectiveness of the combined control strategy. The results prove that the proposed control strategy has a smaller standard deviation of errors. Therefore, the combined control strategy is more suitable for telescopic flexible manipulators, which can effectively improve the control accuracy of rotation angles.

Iterative Feedback Tuning for regulatory control systems with measurable disturbances

Abstract The study proposes Iterative Feedback Tuning (IFT) with measurable disturbances for disturbance attenuation using regulatory control data. The control system has a feedforward controller from the measurable disturbances as well as a feedback controller. The variance of the closed-loop output is used for the cost criterion. The gradient of the cost criterion in terms of both feedback and feedforward controller parameters are estimated via spectral analysis for single set of regulatory control data. The proposed method has a feature that it makes full use of measurable disturbances. In addition, it has a feature that it does not require a specific experiment for estimating the gradient of the cost criterion. These features facilitate the application of IFT to industrial processes. That is because they usually use an optimal set point value from the aspect of economical evaluation, and require to keep the set point a constant value. A numerical simulation shows the proposed method effectively tune both feedback and feedforward controller parameters.

Data-Driven Iterative Trajectory Shaping for Precision Control of Flexible Feed Drives

With recent advances on material processing technologies, design requirements on modern manufacturing equipment are shifting toward achieving higher speed and accuracy rather than stiffness and load capacity. Motion systems used in additive or ultraprecision manufacturing equipment need to be designed to achieve greater dynamic positioning accuracy. As a result, feedforward (FF) control becomes an effective tool. This article presents a novel and practical FF controller design methodology to widen tracking bandwidth of precision motion systems suffering from structural dynamics and friction-induced disturbances. The proposed feedforward controller is designed to, first, compensate for the closed-loop servo dynamics to widen the command tracking bandwidth, second, mitigate unwanted vibrations, and third, compensate friction disturbance-induced positioning errors. The FF controller is structured in a trajectory prefilter form where the FF compensation signal is injected into the reference trajectory rather than torque command. This facilitates the FF controller to be applied conveniently without intervention with the servo controller. FF controller parameters are identified (tuned) automatically through machine in-the-loop-iterations. Parameter identification is posed as a convex optimization problem to realize safe and reliable auto-tuning. The proposed FF controller and its parameter tuning methodology are tested experimentally on an industrial scale multiaxis machine tool and its performance is validated.

Iterative Tuning of Feedforward Controller with Precise Time-Delay Compensation for Precision Motion System

The accuracy of feedforward control model including system time-delay significantly affects the position tracking performance in a precision motion system. In this paper, an iterative tuning method for feedforward control with precise time-delay compensation is proposed. First, considering system time-delay from actuator, sensor, calculation, and communication in real platform, a feedforward control model with time-delay compensation is established, and a nonlinear objective function with time-delay is designed based on the measured data of a finite time task, to minimize the position tracking error. Second, in order to deal with both the nonlinear objective function and also unknown disturbances and noise in the real system, an optimization strategy combining the Gauss–Newton iterative (GNI) scheme and instrumental variable (IV) is proposed to realize the unbiased estimation of the feedforward parameters and precise delay time. Finally, with the identified feedforward control parameters, the precise system time-delay which is a nonintegral multiple of the sampling period is compensated accurately for the feedforward control with accurate path planning time-shift in the implementation. The effectiveness of the proposed feedforward parameter tuning and precise time-delay compensation scheme is verified by the simulation and also experimental result on a precision motion platform with obvious position tracking performance improvement.

Three-axis coupled flight control law design for flying wing aircraft using eigenstructure assignment method

Due to elimination of horizontal and vertical tails, flying wing aircraft has poor longitudinal and directional dynamic characteristics. In addition, flying wing aircraft uses drag rudders for yaw control, which tends to generate strong three-axis control coupling. To overcome these problems, a flight control law design method that couples the longitudinal axis with the lateral-directional axes is proposed. First, the three-axis coupled control augmentation structure is specified. In the structure, a “soft/hard” cross-connection method is developed for three-axis dynamic decoupling and longitudinal control response decoupling from the drag rudders; maneuvering turn angular rate estimation and subtraction are used in the yaw axis to improve the directional damping. Besides, feedforward control is adopted to improve the maneuverability and control decoupling performance. Then, detailed design methods for feedback and feedforward control parameters are established using eigenstructure assignment and model following technique. Finally, the proposed design method is evaluated and compared with conventional method by numeric simulations. The influences of control derivatives variation of drag rudders on the method are also analyzed. It is demonstrated that the method can effectively improve the dynamic characteristics of flying wing aircraft, especially the directional damping characteristics, and decouple the longitudinal responses from the drag rudders.

Output feedback reinforcement learning based optimal output synchronisation of heterogeneous discrete‐time multi‐agent systems

This study proposes a model-free distributed output feedback control scheme that achieves synchronisation of the outputs of the heterogeneous follower agents with that of the leader agent in a directed network. A distributed two degree of freedom approach is presented that separates the learning of the optimal output feedback and the feedforward terms of the local control law for each agent. The local feedback parameters are learned using the proposed off-policy Q-learning algorithm, whereas a gradient adaptive law is presented to learn the local feedforward control parameters to achieve asymptotic tracking of each agent. This learning scheme and the resulting distributed control laws neither require access to the local internal state of the agents nor do they need an additional distributed leader state observer. The proposed approach has the advantage over the previous state augmentation approaches as it circumvents the need of introducing a discounting factor in the local performance functions. It is shown that the proposed algorithm converges to the optimal solution of the algebraic Riccati equation and the output regulator equations without explicitly solving them as long as the leader agent is reachable directly or indirectly from all the follower agents. Simulation results validate the proposed scheme.

Experience replay–based output feedback Q‐learning scheme for optimal output tracking control of discrete‐time linear systems

SummaryThis paper focuses on solving the adaptive optimal tracking control problem for discrete‐time linear systems with unknown system dynamics using output feedback. A Q‐learning‐based optimal adaptive control scheme is presented to learn the feedback and feedforward control parameters of the optimal tracking control law. The optimal feedback parameters are learned using the proposed output feedback Q‐learning Bellman equation, whereas the estimation of the optimal feedforward control parameters is achieved using an adaptive algorithm that guarantees convergence to zero of the tracking error. The proposed method has the advantage that it is not affected by the exploration noise bias problem and does not require a discounting factor, relieving the two bottlenecks in the past works in achieving stability guarantee and optimal asymptotic tracking. Furthermore, the proposed scheme employs the experience replay technique for data‐driven learning, which is data efficient and relaxes the persistence of excitation requirement in learning the feedback control parameters. It is shown that the learned feedback control parameters converge to the optimal solution of the Riccati equation and the feedforward control parameters converge to the solution of the Sylvester equation. Simulation studies on two practical systems have been carried out to show the effectiveness of the proposed scheme.

Event-Based Control for Differentially Flat Systems: Application to Autonomous Underwater Vehicle

The problem of trajectory tracking for nonlinear differentially flat systems is addressed in the paper. A novel event-based control methodology is developed for systems depending on unmeasurable disturbances. A standard flatness-based feedforward control is applied when the flat outputs are close to their desired values. If the error becomes too large, the feedforward controller parameters are updated based on the measurements of the flat outputs and on the estimates of their time-derivatives. The developed control strategy is applied to an autonomous underwater vehicle. Simulations show that the vehicle tracks the pre-defined reference trajectory both in healthy situation as well as in the case of certain mechanical faults.

Reduced Capacitance Battery Storage DC-Link Voltage Regulation and Dynamic Improvement Using a Feedforward Control Strategy

This paper proposes a feedback/feedforward control strategy to attenuate the dc-link voltage variations in a reduced-capacitor battery energy storage system. It also identifies the impact of dc-link voltage variations on a battery energy storage system's response dynamics and power capabilities. It shows that addition of a feedforward controller to the existing feedback controller improves the dc-link voltage regulation and consequently the response dynamics of the reduced-capacitor battery energy storage system. It formulates a power setpoint change as a disturbance to the battery energy storage control system. The battery energy storage system is analyzed using an averaged-value modeling technique. Small-signal analysis is then used to tune feedforward controller parameters. The performance of the proposed controller is evaluated via an electromagnetic transient software (PSCAD/EMTDC), and the findings are experimentally validated using a laboratory-sized test bench.

Design of a Data-Oriented PID controller for a Two Degree of Freedom Control System

Virtual Reference Feedback Tuning (VRFT) and Fictitious Reference Iterative Tuning (FRIT) are the data-oriented tuning schemes for directly designing feedback controllers. In these schemes, controller parameters are tuned to achieve the desired response without any system identifications. Furthermore, these schemes have been extended to the two-degree of freedom (2DOF) control systems in recent years. The conventional design schemes of 2DOF controllers are a two-step tuning method. The first step is to design a feedback controller to satisfy the desired disturbance response. The second step is to tune the feed-forward controller parameters to achieve the desired reference response. Consider these methods are complicated, this paper provides a one-step tuning scheme for a 2DOF control system based on data-oriented method. According to the proposed scheme, the reference of the complementary sensitivity function is introduced, and the least squares method can be applied to calculate the optimal controller parameters. Finally, the effectiveness of the proposed scheme is verified by using a numerical example.

Servo performance improvement through iterative tuning feedforward controller with disturbance compensator

Servo system is widely used in NC machines and its performance directly determines the precision of the machines. In most situations, the control structure for the servo system usually contains a cascaded P-PI feedback controller and a feedforward controller. This paper focuses on the feedforward controller parameters tuning to improve the servo performance. The feedforward controller consists of a model inversion and a parameterized disturbance model. Its parameters are tuned iteratively using the last cycle motion results. This method has the good extrapolation capability to the references and the performance improvement capacity. Moreover, it is easy to implement in real machines due to the simplicity and thus is of interest to control engineers. Experiments are carried out on an industrial prototype system. The results show that the proposed tuning method can improve the servo performance rapidly and the references are not required to keep the same during the tuning process.

Cascaded proportional–integral–derivative controller parameters tuning for contour following improvement

Most commercial servo systems utilize cascaded proportional–integral–derivative controller due to its simplicity and robustness. Under this control structure, motion performances such as single-axial tracking and multi-axial contour following are completely determined by the controller parameters. In real applications, current loop controller parameters usually remain unchanged while velocity loop and position loop ones should be tuned as the mechanisms including inertia and resonant frequency vary. This article focuses on such cascaded proportional–integral–derivative controller parameters tuning for contour following performance improvement. It first describes the servo system dynamics under the cascaded proportional–integral–derivative control structure and identifies the model parameters via relay feedback technology. Then, the effects of the closed-loop dynamics on contour following are analyzed to gain that the matched axial dynamics is preferable. In order to match the axial dynamics, the controller parameters are tuned loop by loop through setting all the axes to have the same bandwidths. Based on the identified results, feedforward controller parameters including nonlinear coulomb friction compensation are also tuned to further improve the contour following performance. At last, the proposed tuning method is verified through the ellipse and diamond following experiments carried out on an X-Y motion stage. The results show that it can match the axial dynamics effectively and thus reduce the contour error greatly.

Coriolis mass-flow meter with integrated multi-DOF active vibration isolation

Vibration isolation of more than 40 dB is achieved for a Coriolis Mass-Flow Meter (CMFM) with integrated Active Vibration Isolation. A CMFM is an active device based on the Coriolis force principle for direct mass-flow measurements independent of fluid properties. The mass-flow measurement is derived from tube displacement measurements. Support excitations can introduce motions that cannot be distinguished from the Coriolis force induced motion, thus introducing a measurement error. Therefore, the measurement stage is passively suspended at 30 Hz in the 3 out-of-plane directions. Active vibration isolation is added to increase the attenuation. In this paper the system model and controller design are presented. Based on the model an on-scale proof of principle is built and the model and controller are validated in multi-DOF. Acceleration feedback and a novel adaptive feedforward control strategy are compared A filtered-reference least-mean-square (FxLMS) adaptive scheme is used to determine the optimal feedforward controller parameters to minimise a squared error signal; the motion of the measurement stage. Both strategies result in an attenuation of 10 – 20 dB at 175 Hz in addition to the 30 dB attenuation obtained by the 30 Hz passive vibration isolation stage. The performance of the feedback strategy is limited by robust stability and the the feedforward performance is limited by sensor noise.

AUTOMATIC CONTROL OF THE CONTAMINATED SOIL TREATMENT TECHNOLOGICAL PROCESS

The extension of a recently developed method for gain scheduling of linear controllers is applied for self-tuning of feed-forward/feed-back control system that is applied for automatic control of contaminated soil washing technological process. Adaptation of the feed-back and the feed-forward controller parameters to the time-varying state of controlled process refers to the process state model based transfer functions, which are up-dated on-line with the measured values of the state and disturbance variables.

Building Air Conditioning Model using the Room-specific Thermal Inertia and its Implementation as a Controller

Various exogenous elements are involved in the thermal environment of the residential indoor spaces of building. Most of these elements behave differently and inter-relatedly to affect each room. It is difficult for the building air conditioning system to control the thermal environment of the rooms. Making use of the stability brought by the inherent equilibrium of the heat inertia system itself and regarding the changes by the result of various extrinsic factors as the change of the heat inertia of the room, the model of the differential air conditional system (DACS) is proposed. In order to verify the effectiveness of the system, the differential air-conditioning controller (DAC) is installed. It employs the rhyming control method. It is a method of air conditioning that causes gentle oscillatory movement to the process value of the room. In the controller, heuristic learning method is employed to determine the feed forward control parameters to capture the thermal behaviors (Differential Heat-effecting Dynamics), which is observed in the thermal environment of the room, defined as the thermal inertia. DAC keeps the appropriate temperature without heat shock to the residents in accordance with the trajectory of the temperature set point.

Implementation of Feedback Error Learning for Position Control System of Linear Slider

This paper considers implementation of feedback error learning (FEL) for linear slider position control. The effect of modelling error of the slider is compensated by FEL. In algorithm of FEL, parameters of feedforward controller are updated so that output signals of a feedback controller can become small. It leads to improvement of tracking performance. On the other hand, when conventional FEL algorithm is implemented for control of a linear slider, parameters of the feedforward controller tend to converge slowly. In industrial scenes, it is desirable to shorten learning time in order to reduce cost of setting up manufacturing devices. Furthermore, when the learning time is long, guide rails of the slider become worn. It is shown that convergence speed is improved by tuning free parameters of the feedforward controller. Due to further improvement of the convergence, by using prefilter and free parameters of feedforward controller, some parameters of an inverse system are obtained a priori and utilized for learning. Command with mixed frequency is selected in order to identify accurate inverse system of plant. Simulation results are presented to show the effectiveness of proposed method.

Transparency in time-delayed systems and the effect of local force feedback for transparent teleoperation

This paper first investigates the issue of transparency in time-delayed teleoperation. It then studies the advantages of employing local force feedback for enhanced stability and performance. In addition, two classes of three-channel control architectures, that are perfectly transparent under ideal conditions are introduced. The stability robustness of the proposed architectures to delays is rigorously analyzed, leading to certain bounds on force feedforward control parameters. Experimental results are included in support of the theoretical work.