This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hype...This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.展开更多
Feedforward control is one of the most effective control techniques to increase the robot’s tracking accuracy.However,most of the dynamic models used in the feedforward controllers are linearly simplified such that t...Feedforward control is one of the most effective control techniques to increase the robot’s tracking accuracy.However,most of the dynamic models used in the feedforward controllers are linearly simplified such that the nonlinear and time-varying characteristics of dynamics in the workspace are ignored.In this paper,an iterative tuning method for feedforward control of parallel manipulators by taking nonlinear dynamics into account is proposed.Based on the robot rigid-body dynamic model,a feedforward controller considering the dynamic nonlinearity is presented.An iterative tuning method is given to iteratively update the feedforward controller by minimizing the root mean square(RMS)of the joint errors at each cycle.The effectiveness and extrapolation capability of the proposed method are validated through the experiments on a 2-DOF parallel manipulator.This research proposes an iterative tuning method for feedforward control of parallel manipulators considering nonlinear dynamics,which has better extrapolation capability in the whole workspace of manipulators.展开更多
Aiming at the pulse response sequence of a kind of repetitive linear discrete-time singular systems unavailable,the paper explores a data-driven adaptive iterative learning control(DDAILC)strategy that interacts with ...Aiming at the pulse response sequence of a kind of repetitive linear discrete-time singular systems unavailable,the paper explores a data-driven adaptive iterative learning control(DDAILC)strategy that interacts with the pulse response iterative correction(PRIC).The mechanism is to formulate the correction performance index as a linear summation of the quadratic correction error of the pulse response and the quadratic tracking error.The correction algorithm of the pulse response arrives and the correction error goes down in a monotonic way.It also discusses the conditional relationship between the declining rate of the correction error and the correction ratio.A DDAILC algorithm is designed by means of substituting the exact pulse response of the gain-optimized iterative learning control(GOILC)with its approximated one updated in the correction algorithm.The convergences regarding tracking error and correction error are obtained monotonically.Finally,numerical simulation verifies the validity and effectiveness.展开更多
Iterative control structures allow the repeated execution of tasks,activities or sub-processes according to the given conditions in a process model.Iterative control structures can significantly increase the risk of t...Iterative control structures allow the repeated execution of tasks,activities or sub-processes according to the given conditions in a process model.Iterative control structures can significantly increase the risk of triggering temporal exceptions since activities within the scope of these control structures could be repeatedly executed until a predefined condition is met.In this paper,we propose two approaches to unravel iterative control structures from process models.The first approach unravels loops based on zero-one principle.The second approach unravels loops based on branching probabilities assigned at split gateways.The proposed methods can be used to unfold structured loops,nested loops and crossing loops.Since the unfolded model does not contain any iterative control structures,it can be used for further analysis by process designers during the modeling phase.The proposed methods are implemented based on workflow graphs,and therefore they are compatible with modeling languages such as Business Process Modelling Notation(BPMN).In the experiments,the execution behavior of unfolded process models is compared against the original models based on the concept of runs.Experimental results reveal that runs generated from the original models can be correctly executed in the unfolded BPMN models that do not contain any loops.展开更多
A new kind of volume control hydraulic press that combines the advantages of both hydraulic and SRM(switched reluctance motor) driving technology is developed.Considering that the serious dead zone and time-variant no...A new kind of volume control hydraulic press that combines the advantages of both hydraulic and SRM(switched reluctance motor) driving technology is developed.Considering that the serious dead zone and time-variant nonlinearity exist in the volume control electro-hydraulic servo system,the ILC(iterative learning control) method is applied to tracking the displacement curve of the hydraulic press slider.In order to improve the convergence speed and precision of ILC,a fuzzy ILC algorithm that utilizes the fuzzy strategy to adaptively adjust the iterative learning gains is put forward.The simulation and experimental researches are carried out to investigate the convergence speed and precision of the fuzzy ILC for hydraulic press slider position tracking.The results show that the fuzzy ILC can raise the iterative learning speed enormously,and realize the tracking control of slider displacement curve with rapid response speed and high control precision.In experiment,the maximum tracking error 0.02 V is achieved through 12 iterations only.展开更多
The PD-type iterative learning control design of a class of affine nonlinear time-delay systems with external disturbances is considered. Sufficient conditions guaranteeing the convergence of the n-norm of the trackin...The PD-type iterative learning control design of a class of affine nonlinear time-delay systems with external disturbances is considered. Sufficient conditions guaranteeing the convergence of the n-norm of the tracking error are derived. It is shown that the system outputs can be guaranteed to converge to desired trajectories in the absence of external disturbances and output measurement noises. And in the presence of state disturbances and measurement noises, the tracking error will be bounded uniformly. A numerical simulation example is presented to validate the effectiveness of the proposed scheme.展开更多
This paper conducts a survey on iterative learn-ing control(ILC)with incomplete information and associated control system design,which is a frontier of the ILC field.The incomplete information,including passive and ac...This paper conducts a survey on iterative learn-ing control(ILC)with incomplete information and associated control system design,which is a frontier of the ILC field.The incomplete information,including passive and active types,can cause data loss or fragment due to various factors.Passive incomplete information refers to incomplete data and information caused by practical system limitations during data collection,storage,transmission,and processing,such as data dropouts,delays,disordering,and limited transmission bandwidth.Active incomplete information refers to incomplete data and information caused by man-made reduction of data quantity and quality on the premise that the given objective is satisfied,such as sampling and quantization.This survey emphasizes two aspects:the first one is how to guarantee good learning performance and tracking performance with passive incomplete data,and the second is how to balance the control performance index and data demand by active means.The promising research directions along this topic are also addressed,where data robustness is highly emphasized.This survey is expected to improve understanding of the restrictive relationship and trade-off between incomplete data and tracking performance,quantitatively,and promote further developments of ILC theory.展开更多
Designing a controller for the docking maneuver in Probe-Drogue Refueling(PDR) is an important but challenging task, due to the complex system model and the high precision requirement.In order to overcome the disadvan...Designing a controller for the docking maneuver in Probe-Drogue Refueling(PDR) is an important but challenging task, due to the complex system model and the high precision requirement.In order to overcome the disadvantage of only feedback control, a feedforward control scheme known as Iterative Learning Control(ILC) is adopted in this paper.First, Additive State Decomposition(ASD) is used to address the tight coupling of input saturation, nonlinearity and the property of Non Minimum Phase(NMP) by separating these features into two subsystems(a primary system and a secondary system).After system decomposition, an adjoint-type ILC is applied to the Linear Time-Invariant(LTI) primary system with NMP to achieve entire output trajectory tracking, whereas state feedback is used to stabilize the secondary system with input saturation.The two controllers designed for the two subsystems can be combined to achieve the original control goal of the PDR system.Furthermore, to compensate for the receiverindependent uncertainties, a correction action is proposed by using the terminal docking error,which can lead to a smaller docking error at the docking moment.Simulation tests have been carried out to demonstrate the performance of the proposed control method, which has some advantages over the traditional derivative-type ILC and adjoint-type ILC in the docking control of PDR.展开更多
In this paper, the stability of iterative learning control with data dropouts is discussed. By the super vector formulation, an iterative learning control （ILC） system with data dropouts can be modeled as an asynchr...In this paper, the stability of iterative learning control with data dropouts is discussed. By the super vector formulation, an iterative learning control （ILC） system with data dropouts can be modeled as an asynchronous dynamical system with rate constraints on events in the iteration domain. The stability condition is provided in the form of linear matrix inequalities （LMIS） depending on the stability of asynchronous dynamical systems. The analysis is supported by simulations.展开更多
Based on an equivalent two-dimensional Fornasini-Marchsini model for a batch process in industry, a closed-loop robust iterative learning fault-tolerant guaranteed cost control scheme is proposed for batch processes w...Based on an equivalent two-dimensional Fornasini-Marchsini model for a batch process in industry, a closed-loop robust iterative learning fault-tolerant guaranteed cost control scheme is proposed for batch processes with actuator failures. This paper introduces relevant concepts of the fault-tolerant guaranteed cost control and formulates the robust iterative learning reliable guaranteed cost controller (ILRGCC). A significant advantage is that the proposed ILRGCC design method can be used for on-line optimization against batch-to-batch process uncertainties to realize robust tracking of set-point trajectory in time and batch-to-batch sequences. For the convenience of implementation, only measured output errors of current and previous cycles are used to design a synthetic controller for iterative learning control, consisting of dynamic output feedback plus feed-forward control. The proposed controller can not only guarantee the closed-loop convergency along time and cycle sequences but also satisfy the H∞performance level and a cost function with upper bounds for all admissible uncertainties and any actuator failures. Sufficient conditions for the controller solution are derived in terms of linear matrix inequalities (LMIs), and design procedures, which formulate a convex optimization problem with LMI constraints, are presented. An example of injection molding is given to illustrate the effectiveness and advantages of the ILRGCC design approach.展开更多
This paper proposes a new adaptive iterative learning control approach for a class of nonlinearly parameterized systems with unknown time-varying delay and unknown control direction.By employing the parameter separati...This paper proposes a new adaptive iterative learning control approach for a class of nonlinearly parameterized systems with unknown time-varying delay and unknown control direction.By employing the parameter separation technique and signal replacement mechanism,the approach can overcome unknown time-varying parameters and unknown time-varying delay of the nonlinear systems.By incorporating a Nussbaum-type function,the proposed approach can deal with the unknown control direction of the nonlinear systems.Based on a Lyapunov-Krasovskii-like composite energy function,the convergence of tracking error sequence is achieved in the iteration domain.Finally,two simulation examples are provided to illustrate the feasibility of the proposed control method.展开更多
Under the condition of large inertia load,the stability of the servo system is more sensitive to the response speed and more likely to produce overshoot oscillations.In order to realize the requirements of high-precis...Under the condition of large inertia load,the stability of the servo system is more sensitive to the response speed and more likely to produce overshoot oscillations.In order to realize the requirements of high-precision and fast-response control of permanent magnet synchronous motor(PMSM)under large inertia load,an improved feedforward control strategy based on position impulse compensation and PD iterative algorithm is proposed to improve the response speed of the PMSM servo system and reduce the overshoot oscillation.This paper analyzes the mathematical models of the speed servo system and position servo system of the PMSM,calculates position overshoot impulse of the PMSM servo system,and improves the traditional feedforward control strategy to reversely compensate when the position is about to overshoot.Moreover,in order to further reduce the position overshoot,the PD iterative control algorithm is superimposed without increasing the complexity of the algorithm.The input signal is continuously corrected through multiple runs to achieve a smoother response control.The effectiveness of the proposed feedforward control strategy is verified by simulation and experiment.展开更多
A batch-to-batch optimal iterative learning control （ILC） strategy for the tracking control of product quality in batch processes is presented. The linear time-varying perturbation （LTVP） model is built for produc...A batch-to-batch optimal iterative learning control （ILC） strategy for the tracking control of product quality in batch processes is presented. The linear time-varying perturbation （LTVP） model is built for product quality around the nominal trajectories. To address problems of model-plant mismatches, model prediction errors in the previous batch run are added to the model predictions for the current batch run. Then tracking error transition models can be built, and the ILC law with direct error feedback is explicitly obtained, A rigorous theorem is proposed, to prove the convergence of tracking error under ILC, The proposed methodology is illustrated on a typical batch reactor and the results show that the performance of trajectory tracking is gradually improved by the ILC.展开更多
An observer-based adaptive iterative learning control(AILC)scheme is developed for a class of nonlinear systems with unknown time-varying parameters and unknown time-varying delays.The linear matrix inequality(LMI)met...An observer-based adaptive iterative learning control(AILC)scheme is developed for a class of nonlinear systems with unknown time-varying parameters and unknown time-varying delays.The linear matrix inequality(LMI)method is employed to design the nonlinear observer.The designed controller contains a proportional-integral-derivative(PID)feedback term in time domain.The learning law of unknown constant parameter is differential-difference-type,and the learning law of unknown time-varying parameter is difference-type.It is assumed that the unknown delay-dependent uncertainty is nonlinearly parameterized.By constructing a Lyapunov-Krasovskii-like composite energy function(CEF),we prove the boundedness of all closed-loop signals and the convergence of tracking error.A simulation example is provided to illustrate the effectiveness of the control algorithm proposed in this paper.展开更多
The iterative learning control （ILC） has been demon-strated to be capable of considerably improving the tracking perfor-mance of systems which are affected by the iteration-independent disturbance. However, the achi...The iterative learning control （ILC） has been demon-strated to be capable of considerably improving the tracking perfor-mance of systems which are affected by the iteration-independent disturbance. However, the achievable performance is greatly degraded when iteration-dependent, stochastic disturbances are pre-sented. This paper considers the robustness of the ILC algorithm for the nonlinear system in presence of stochastic measurement disturbances. The robust convergence of the P-type ILC algorithm is firstly addressed, and then an improved ILC algorithm with a decreasing gain is proposed. Theoretical analyses show that the proposed algorithm can guarantee that the tracking error of the nonlinear system tends to zero in presence of measurement dis-turbances. The analysis is also supported by a numerical example.展开更多
In this paper, an iterative learning control algorithm is proposed for discrete linear time-varying systems to track iterationvarying desired trajectories. A high-order internal model(HOIM) is utilized to describe the...In this paper, an iterative learning control algorithm is proposed for discrete linear time-varying systems to track iterationvarying desired trajectories. A high-order internal model(HOIM) is utilized to describe the variation of desired trajectories in the iteration domain. In the sequel, the HOIM is incorporated into the design of learning gains. The learning convergence in the iteration axis can be guaranteed with rigorous proof. The simulation results with permanent magnet linear motors(PMLM) demonstrate that the proposed HOIM based approach yields good performance and achieves perfect tracking.展开更多
Iterative Learning Control （ILC） captures interests of many scholars because of its capability of high precision control implement without identifying plant mathematical models, and it is widely applied in control e...Iterative Learning Control （ILC） captures interests of many scholars because of its capability of high precision control implement without identifying plant mathematical models, and it is widely applied in control engineering. Presently, most ILC algorithms still follow the original ideas of ARIMOTO, in which the iterative-learning-rate is composed by the control error with its derivative and integral values. This kind of algorithms will result in inevitable problems such as huge computation, big storage capacity for algorithm data, and also weak robust. In order to resolve these problems, an improved iterative learning control algorithm with fixed step is proposed here which breaks the primary thought of ARIMOTO. In this algorithm, the control step is set only according to the value of the control error, which could enormously reduce the computation and storage size demanded, also improve the robust of the algorithm by not using the differential coefficient of the iterative learning error. In this paper, the convergence conditions of this proposed fixed step iterative learning algorithm is theoretically analyzed and testified. Then the algorithm is tested through simulation researches on a time-variant object with randomly set disturbance through calculation of step threshold value, algorithm robustness testing,and evaluation of the relation between convergence speed and step size. Finally the algorithm is validated on a valve-serving-cylinder system of a joint robot with time-variant parameters. Experiment results demonstrate the stability of the algorithm and also the relationship between step value and convergence rate. Both simulation and experiment testify the feasibility and validity of the new algorithm proposed here. And it is worth to noticing that this algorithm is simple but with strong robust after improvements, which provides new ideas to the research of iterative learning control algorithms.展开更多
This paper aims to solve the robust iterative learning control(ILC)problems for nonlinear time-varying systems in the presence of nonrepetitive uncertainties.A new optimization-based method is proposed to design and a...This paper aims to solve the robust iterative learning control(ILC)problems for nonlinear time-varying systems in the presence of nonrepetitive uncertainties.A new optimization-based method is proposed to design and analyze adaptive ILC,for which robust convergence analysis via a contraction mapping approach is realized by leveraging properties of substochastic matrices.It is shown that robust tracking tasks can be realized for optimization-based adaptive ILC,where the boundedness of system trajectories and estimated parameters can be ensured,regardless of unknown time-varying nonlinearities and nonrepetitive uncertainties.Two simulation tests,especially implemented for an injection molding process,demonstrate the effectiveness of our robust optimization-based ILC results.展开更多
In this paper, both output-feedback iterative learning control(ILC) and repetitive learning control(RLC) schemes are proposed for trajectory tracking of nonlinear systems with state-dependent time-varying uncertaintie...In this paper, both output-feedback iterative learning control(ILC) and repetitive learning control(RLC) schemes are proposed for trajectory tracking of nonlinear systems with state-dependent time-varying uncertainties. An iterative learning controller, together with a state observer and a fully-saturated learning mechanism, through Lyapunov-like synthesis, is designed to deal with time-varying parametric uncertainties. The estimations for outputs, instead of system outputs themselves, are applied to form the error equation, which helps to establish convergence of the system outputs to the desired ones. This method is then extended to repetitive learning controller design. The boundedness of all the signals in the closed-loop is guaranteed and asymptotic convergence of both the state estimation error and the tracking error is established in both cases of ILC and RLC. Numerical results are presented to verify the effectiveness of the proposed methods.展开更多
In the procedure of the steady-state hierarchical optimization with feedback for large-scale industrial processes, a sequence of set-point changes with different magnitudes is carried out on the optimization layer. To...In the procedure of the steady-state hierarchical optimization with feedback for large-scale industrial processes, a sequence of set-point changes with different magnitudes is carried out on the optimization layer. To improve the dynamic performance of transient response driven by the set-point changes, a filter-based iterative learning control strategy is proposed. In the proposed updating law, a local-symmetric-integral operator is adopted for eliminating the measurement noise of output information,a set of desired trajectories are specified according to the set-point changes sequence, the current control input is iteratively achieved by utilizing smoothed output error to modify its control input at previous iteration, to which the amplified coefficients related to the different magnitudes of set-point changes are introduced. The convergence of the algorithm is conducted by incorporating frequency-domain technique into time-domain analysis. Numerical simulation demonstrates the effectiveness of the proposed strategy,展开更多
基金supported by the National Natural Science Foundation of China(12072090).
文摘This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.
基金Supported by National Natural Science Foundation of China(Grant No.52375502)EU H2020 MSCA R&I Programme(Grant No.101022696).
文摘Feedforward control is one of the most effective control techniques to increase the robot’s tracking accuracy.However,most of the dynamic models used in the feedforward controllers are linearly simplified such that the nonlinear and time-varying characteristics of dynamics in the workspace are ignored.In this paper,an iterative tuning method for feedforward control of parallel manipulators by taking nonlinear dynamics into account is proposed.Based on the robot rigid-body dynamic model,a feedforward controller considering the dynamic nonlinearity is presented.An iterative tuning method is given to iteratively update the feedforward controller by minimizing the root mean square(RMS)of the joint errors at each cycle.The effectiveness and extrapolation capability of the proposed method are validated through the experiments on a 2-DOF parallel manipulator.This research proposes an iterative tuning method for feedforward control of parallel manipulators considering nonlinear dynamics,which has better extrapolation capability in the whole workspace of manipulators.
基金supported by the National Natural Science Foundation of China(619733380).
文摘Aiming at the pulse response sequence of a kind of repetitive linear discrete-time singular systems unavailable,the paper explores a data-driven adaptive iterative learning control(DDAILC)strategy that interacts with the pulse response iterative correction(PRIC).The mechanism is to formulate the correction performance index as a linear summation of the quadratic correction error of the pulse response and the quadratic tracking error.The correction algorithm of the pulse response arrives and the correction error goes down in a monotonic way.It also discusses the conditional relationship between the declining rate of the correction error and the correction ratio.A DDAILC algorithm is designed by means of substituting the exact pulse response of the gain-optimized iterative learning control(GOILC)with its approximated one updated in the correction algorithm.The convergences regarding tracking error and correction error are obtained monotonically.Finally,numerical simulation verifies the validity and effectiveness.
基金The work was supported by University of Macao under Grant No. MYRG2019-00136-FST。
文摘Iterative control structures allow the repeated execution of tasks,activities or sub-processes according to the given conditions in a process model.Iterative control structures can significantly increase the risk of triggering temporal exceptions since activities within the scope of these control structures could be repeatedly executed until a predefined condition is met.In this paper,we propose two approaches to unravel iterative control structures from process models.The first approach unravels loops based on zero-one principle.The second approach unravels loops based on branching probabilities assigned at split gateways.The proposed methods can be used to unfold structured loops,nested loops and crossing loops.Since the unfolded model does not contain any iterative control structures,it can be used for further analysis by process designers during the modeling phase.The proposed methods are implemented based on workflow graphs,and therefore they are compatible with modeling languages such as Business Process Modelling Notation(BPMN).In the experiments,the execution behavior of unfolded process models is compared against the original models based on the concept of runs.Experimental results reveal that runs generated from the original models can be correctly executed in the unfolded BPMN models that do not contain any loops.
基金Project(2007AA04Z144) supported by the National High-Tech Research and Development Program of ChinaProject(2007421119) supported by the China Postdoctoral Science Foundation
文摘A new kind of volume control hydraulic press that combines the advantages of both hydraulic and SRM(switched reluctance motor) driving technology is developed.Considering that the serious dead zone and time-variant nonlinearity exist in the volume control electro-hydraulic servo system,the ILC(iterative learning control) method is applied to tracking the displacement curve of the hydraulic press slider.In order to improve the convergence speed and precision of ILC,a fuzzy ILC algorithm that utilizes the fuzzy strategy to adaptively adjust the iterative learning gains is put forward.The simulation and experimental researches are carried out to investigate the convergence speed and precision of the fuzzy ILC for hydraulic press slider position tracking.The results show that the fuzzy ILC can raise the iterative learning speed enormously,and realize the tracking control of slider displacement curve with rapid response speed and high control precision.In experiment,the maximum tracking error 0.02 V is achieved through 12 iterations only.
基金This project was supported by the National Natural Science Foundation of China (60074001) and the Natural ScienceFoundation of Shandong Province (Y2000G02)
文摘The PD-type iterative learning control design of a class of affine nonlinear time-delay systems with external disturbances is considered. Sufficient conditions guaranteeing the convergence of the n-norm of the tracking error are derived. It is shown that the system outputs can be guaranteed to converge to desired trajectories in the absence of external disturbances and output measurement noises. And in the presence of state disturbances and measurement noises, the tracking error will be bounded uniformly. A numerical simulation example is presented to validate the effectiveness of the proposed scheme.
基金supported by the National Natural Science Foundation of China(61673045)Beijing Natural Science Foundation(4152040)
文摘This paper conducts a survey on iterative learn-ing control(ILC)with incomplete information and associated control system design,which is a frontier of the ILC field.The incomplete information,including passive and active types,can cause data loss or fragment due to various factors.Passive incomplete information refers to incomplete data and information caused by practical system limitations during data collection,storage,transmission,and processing,such as data dropouts,delays,disordering,and limited transmission bandwidth.Active incomplete information refers to incomplete data and information caused by man-made reduction of data quantity and quality on the premise that the given objective is satisfied,such as sampling and quantization.This survey emphasizes two aspects:the first one is how to guarantee good learning performance and tracking performance with passive incomplete data,and the second is how to balance the control performance index and data demand by active means.The promising research directions along this topic are also addressed,where data robustness is highly emphasized.This survey is expected to improve understanding of the restrictive relationship and trade-off between incomplete data and tracking performance,quantitatively,and promote further developments of ILC theory.
基金supported by the National Natural Science Foundation of China(No.61473012)。
文摘Designing a controller for the docking maneuver in Probe-Drogue Refueling(PDR) is an important but challenging task, due to the complex system model and the high precision requirement.In order to overcome the disadvantage of only feedback control, a feedforward control scheme known as Iterative Learning Control(ILC) is adopted in this paper.First, Additive State Decomposition(ASD) is used to address the tight coupling of input saturation, nonlinearity and the property of Non Minimum Phase(NMP) by separating these features into two subsystems(a primary system and a secondary system).After system decomposition, an adjoint-type ILC is applied to the Linear Time-Invariant(LTI) primary system with NMP to achieve entire output trajectory tracking, whereas state feedback is used to stabilize the secondary system with input saturation.The two controllers designed for the two subsystems can be combined to achieve the original control goal of the PDR system.Furthermore, to compensate for the receiverindependent uncertainties, a correction action is proposed by using the terminal docking error,which can lead to a smaller docking error at the docking moment.Simulation tests have been carried out to demonstrate the performance of the proposed control method, which has some advantages over the traditional derivative-type ILC and adjoint-type ILC in the docking control of PDR.
基金supported by General Program (No. 60774022)State Key Program (No. 60834001) of National Natural Science Foundation of China
文摘In this paper, the stability of iterative learning control with data dropouts is discussed. By the super vector formulation, an iterative learning control （ILC） system with data dropouts can be modeled as an asynchronous dynamical system with rate constraints on events in the iteration domain. The stability condition is provided in the form of linear matrix inequalities （LMIS） depending on the stability of asynchronous dynamical systems. The analysis is supported by simulations.
基金Supported in part by NSFC/RGC joint Research Scheme （N-HKUST639/09）, the National Natural Science Foundation of China （61104058, 61273101）, Guangzhou Scientific and Technological Project （2012J5100032）, Nansha district independent innovation project （201103003）, China Postdoctoral Science Foundation （2012M511367, 2012M511368）, and Doctor Scientific Research Foundation of Liaoning Province （20121046）.
文摘Based on an equivalent two-dimensional Fornasini-Marchsini model for a batch process in industry, a closed-loop robust iterative learning fault-tolerant guaranteed cost control scheme is proposed for batch processes with actuator failures. This paper introduces relevant concepts of the fault-tolerant guaranteed cost control and formulates the robust iterative learning reliable guaranteed cost controller (ILRGCC). A significant advantage is that the proposed ILRGCC design method can be used for on-line optimization against batch-to-batch process uncertainties to realize robust tracking of set-point trajectory in time and batch-to-batch sequences. For the convenience of implementation, only measured output errors of current and previous cycles are used to design a synthetic controller for iterative learning control, consisting of dynamic output feedback plus feed-forward control. The proposed controller can not only guarantee the closed-loop convergency along time and cycle sequences but also satisfy the H∞performance level and a cost function with upper bounds for all admissible uncertainties and any actuator failures. Sufficient conditions for the controller solution are derived in terms of linear matrix inequalities (LMIs), and design procedures, which formulate a convex optimization problem with LMI constraints, are presented. An example of injection molding is given to illustrate the effectiveness and advantages of the ILRGCC design approach.
基金supported by National Natural Science Foundation of China (No. 60974139)Fundamental Research Funds for the Central Universities (No. 72103676)
文摘This paper proposes a new adaptive iterative learning control approach for a class of nonlinearly parameterized systems with unknown time-varying delay and unknown control direction.By employing the parameter separation technique and signal replacement mechanism,the approach can overcome unknown time-varying parameters and unknown time-varying delay of the nonlinear systems.By incorporating a Nussbaum-type function,the proposed approach can deal with the unknown control direction of the nonlinear systems.Based on a Lyapunov-Krasovskii-like composite energy function,the convergence of tracking error sequence is achieved in the iteration domain.Finally,two simulation examples are provided to illustrate the feasibility of the proposed control method.
基金supported in part by the National Natural Science Foundation of China under Project No.52207043。
文摘Under the condition of large inertia load,the stability of the servo system is more sensitive to the response speed and more likely to produce overshoot oscillations.In order to realize the requirements of high-precision and fast-response control of permanent magnet synchronous motor(PMSM)under large inertia load,an improved feedforward control strategy based on position impulse compensation and PD iterative algorithm is proposed to improve the response speed of the PMSM servo system and reduce the overshoot oscillation.This paper analyzes the mathematical models of the speed servo system and position servo system of the PMSM,calculates position overshoot impulse of the PMSM servo system,and improves the traditional feedforward control strategy to reversely compensate when the position is about to overshoot.Moreover,in order to further reduce the position overshoot,the PD iterative control algorithm is superimposed without increasing the complexity of the algorithm.The input signal is continuously corrected through multiple runs to achieve a smoother response control.The effectiveness of the proposed feedforward control strategy is verified by simulation and experiment.
基金Supported by the National Natural Science Foundation of China （60404012, 60674064）, UK EPSRC （GR/N13319 and GR/R10875）, the National High Technology Research and Development Program of China （2007AA04Z193）, New Star of Science and Technology of Beijing City （2006A62）, and IBM China Research Lab 2007 UR-Program.
文摘A batch-to-batch optimal iterative learning control （ILC） strategy for the tracking control of product quality in batch processes is presented. The linear time-varying perturbation （LTVP） model is built for product quality around the nominal trajectories. To address problems of model-plant mismatches, model prediction errors in the previous batch run are added to the model predictions for the current batch run. Then tracking error transition models can be built, and the ILC law with direct error feedback is explicitly obtained, A rigorous theorem is proposed, to prove the convergence of tracking error under ILC, The proposed methodology is illustrated on a typical batch reactor and the results show that the performance of trajectory tracking is gradually improved by the ILC.
基金supported by National Natural Science Foundation of China(No.60804021,No.60702063)
文摘An observer-based adaptive iterative learning control(AILC)scheme is developed for a class of nonlinear systems with unknown time-varying parameters and unknown time-varying delays.The linear matrix inequality(LMI)method is employed to design the nonlinear observer.The designed controller contains a proportional-integral-derivative(PID)feedback term in time domain.The learning law of unknown constant parameter is differential-difference-type,and the learning law of unknown time-varying parameter is difference-type.It is assumed that the unknown delay-dependent uncertainty is nonlinearly parameterized.By constructing a Lyapunov-Krasovskii-like composite energy function(CEF),we prove the boundedness of all closed-loop signals and the convergence of tracking error.A simulation example is provided to illustrate the effectiveness of the control algorithm proposed in this paper.
基金supported by the National Natural Science Foundation of China (61203065 60834001)the Program of Open Laboratory Foundation of Control Engineering Key Discipline of Henan Provincial High Education (KG 2011-10)
文摘The iterative learning control （ILC） has been demon-strated to be capable of considerably improving the tracking perfor-mance of systems which are affected by the iteration-independent disturbance. However, the achievable performance is greatly degraded when iteration-dependent, stochastic disturbances are pre-sented. This paper considers the robustness of the ILC algorithm for the nonlinear system in presence of stochastic measurement disturbances. The robust convergence of the P-type ILC algorithm is firstly addressed, and then an improved ILC algorithm with a decreasing gain is proposed. Theoretical analyses show that the proposed algorithm can guarantee that the tracking error of the nonlinear system tends to zero in presence of measurement dis-turbances. The analysis is also supported by a numerical example.
基金supported by National Basic Research Program of China(973 Program)(No.2012CB316400)National Natural Science Foundation of China(Nos.61171034 and 61273134)
文摘In this paper, an iterative learning control algorithm is proposed for discrete linear time-varying systems to track iterationvarying desired trajectories. A high-order internal model(HOIM) is utilized to describe the variation of desired trajectories in the iteration domain. In the sequel, the HOIM is incorporated into the design of learning gains. The learning convergence in the iteration axis can be guaranteed with rigorous proof. The simulation results with permanent magnet linear motors(PMLM) demonstrate that the proposed HOIM based approach yields good performance and achieves perfect tracking.
基金supported by Specialized Research Fund for Doctoral Program of Higher Education of China (Grant No. 20091102120038)
文摘Iterative Learning Control （ILC） captures interests of many scholars because of its capability of high precision control implement without identifying plant mathematical models, and it is widely applied in control engineering. Presently, most ILC algorithms still follow the original ideas of ARIMOTO, in which the iterative-learning-rate is composed by the control error with its derivative and integral values. This kind of algorithms will result in inevitable problems such as huge computation, big storage capacity for algorithm data, and also weak robust. In order to resolve these problems, an improved iterative learning control algorithm with fixed step is proposed here which breaks the primary thought of ARIMOTO. In this algorithm, the control step is set only according to the value of the control error, which could enormously reduce the computation and storage size demanded, also improve the robust of the algorithm by not using the differential coefficient of the iterative learning error. In this paper, the convergence conditions of this proposed fixed step iterative learning algorithm is theoretically analyzed and testified. Then the algorithm is tested through simulation researches on a time-variant object with randomly set disturbance through calculation of step threshold value, algorithm robustness testing,and evaluation of the relation between convergence speed and step size. Finally the algorithm is validated on a valve-serving-cylinder system of a joint robot with time-variant parameters. Experiment results demonstrate the stability of the algorithm and also the relationship between step value and convergence rate. Both simulation and experiment testify the feasibility and validity of the new algorithm proposed here. And it is worth to noticing that this algorithm is simple but with strong robust after improvements, which provides new ideas to the research of iterative learning control algorithms.
基金supported by the National Natural Science Foundation of China(61873013,61922007)。
文摘This paper aims to solve the robust iterative learning control(ILC)problems for nonlinear time-varying systems in the presence of nonrepetitive uncertainties.A new optimization-based method is proposed to design and analyze adaptive ILC,for which robust convergence analysis via a contraction mapping approach is realized by leveraging properties of substochastic matrices.It is shown that robust tracking tasks can be realized for optimization-based adaptive ILC,where the boundedness of system trajectories and estimated parameters can be ensured,regardless of unknown time-varying nonlinearities and nonrepetitive uncertainties.Two simulation tests,especially implemented for an injection molding process,demonstrate the effectiveness of our robust optimization-based ILC results.
基金supported by the Third Level of Hangzhou 131 Young Talent Cultivation Plan Funding2018 Soft Science Research Project of Zhejiang Provincial Science and Technology Department Zhejiang Province Construction and participate in the“The Belt and Road”Technology Innovation Community Path Research(2018C35029)
文摘In this paper, both output-feedback iterative learning control(ILC) and repetitive learning control(RLC) schemes are proposed for trajectory tracking of nonlinear systems with state-dependent time-varying uncertainties. An iterative learning controller, together with a state observer and a fully-saturated learning mechanism, through Lyapunov-like synthesis, is designed to deal with time-varying parametric uncertainties. The estimations for outputs, instead of system outputs themselves, are applied to form the error equation, which helps to establish convergence of the system outputs to the desired ones. This method is then extended to repetitive learning controller design. The boundedness of all the signals in the closed-loop is guaranteed and asymptotic convergence of both the state estimation error and the tracking error is established in both cases of ILC and RLC. Numerical results are presented to verify the effectiveness of the proposed methods.
基金This work was supported by the National Natural Science Foundation of China (No. 60274055)
文摘In the procedure of the steady-state hierarchical optimization with feedback for large-scale industrial processes, a sequence of set-point changes with different magnitudes is carried out on the optimization layer. To improve the dynamic performance of transient response driven by the set-point changes, a filter-based iterative learning control strategy is proposed. In the proposed updating law, a local-symmetric-integral operator is adopted for eliminating the measurement noise of output information,a set of desired trajectories are specified according to the set-point changes sequence, the current control input is iteratively achieved by utilizing smoothed output error to modify its control input at previous iteration, to which the amplified coefficients related to the different magnitudes of set-point changes are introduced. The convergence of the algorithm is conducted by incorporating frequency-domain technique into time-domain analysis. Numerical simulation demonstrates the effectiveness of the proposed strategy,
