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
Nowadays, high-precision motion controls are needed in modern manufacturing industry. A data-driven nonparametric model adaptive control(NMAC) method is proposed in this paper to control the position of a linear servo...Nowadays, high-precision motion controls are needed in modern manufacturing industry. A data-driven nonparametric model adaptive control(NMAC) method is proposed in this paper to control the position of a linear servo system. The controller design requires no information about the structure of linear servo system, and it is based on the estimation and forecasting of the pseudo-partial derivatives(PPD) which are estimated according to the voltage input and position output of the linear motor. The characteristics and operational mechanism of the permanent magnet synchronous linear motor(PMSLM) are introduced, and the proposed nonparametric model control strategy has been compared with the classic proportional-integral-derivative(PID) control algorithm. Several real-time experiments on the motion control system incorporating a permanent magnet synchronous linear motor showed that the nonparametric model adaptive control method improved the system s response to disturbances and its position-tracking precision, even for a nonlinear system with incompletely known dynamic characteristics.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
In this paper, iterative learning control （ILC） design is studied for an iteration-varying tracking problem in which reference trajectories are generated by high-order internal models （HOLM）. An HOlM formulated as...In this paper, iterative learning control （ILC） design is studied for an iteration-varying tracking problem in which reference trajectories are generated by high-order internal models （HOLM）. An HOlM formulated as a polynomial operator between consecutive iterations describes the changes of desired trajectories in the iteration domain and makes the iterative learning problem become iteration varying. The classical ILC for tracking iteration-invariant reference trajectories, on the other hand, is a special case of HOlM where the polynomial renders to a unity coefficient or a special first-order internal model. By inserting the HOlM into P-type ILC, the tracking performance along the iteration axis is investigated for a class of continuous-time nonlinear systems. Time-weighted norm method is utilized to guarantee validity of proposed algorithm in a sense of data-driven control.展开更多
This work presents an anticipatory terminal iterative learning control scheme for a class of batch proc- esses, where only the final system output is measurable and the control input is constant in each operations. Th...This work presents an anticipatory terminal iterative learning control scheme for a class of batch proc- esses, where only the final system output is measurable and the control input is constant in each operations. The propgsed approach works well with input constraints provided that the desired control input with respect to the desired trajectory is within the samratiorl bound. The tracking error convergence is established with rigorous mathe- matical analysis. Simulation results .are provided to showthe effectiveness, of the proposed approach.展开更多
In this paper,nonlinear observers are incorporated into the adaptive control to synthesize controllers for a class of uncertain nonlinear systems with unknown sinusoidal disturbances which are presented in matched and...In this paper,nonlinear observers are incorporated into the adaptive control to synthesize controllers for a class of uncertain nonlinear systems with unknown sinusoidal disturbances which are presented in matched and unmatched forms.In addition to magnitudes and phases,frequencies of the sinusoidal disturbances need not be known as well,so long as the overall order is known.Nonlinear observers are constructed to eliminate the effect of unknown sinusoidal disturbances to improve the steady-state output tracking performance-asymptotic output tracking is achieved.The adaptation law is used to obtain the estimate of all unknown parameters.The presented disturbance decoupling algorithms can deal with matched and unmatched unknown sinusoidal disturbances.展开更多
ADAPTIVE control is a proven method for learning feedback controllers for systems with unknown dynamic models,exogenous disturbances,nonzero setpoints,and unmodeled nonlinearities.Adaptive control has been applied for...ADAPTIVE control is a proven method for learning feedback controllers for systems with unknown dynamic models,exogenous disturbances,nonzero setpoints,and unmodeled nonlinearities.Adaptive control has been applied for years in process control,industry,aerospace systems。展开更多
Nonlinearity in parallel compliance can be exploited to improve the performance of locomotion systems in terms of(1)energy efficiency,(2)control robustness,and(3)gait optimality;that is,attaining energy efficiency acr...Nonlinearity in parallel compliance can be exploited to improve the performance of locomotion systems in terms of(1)energy efficiency,(2)control robustness,and(3)gait optimality;that is,attaining energy efficiency across a set of motions.Thus far,the literature has investigated and validated only the first two benefits.In this study,we present a new mathematical framework for designing nonlinear compliances in cyclic tasks encompassing all three benefits.We present an optimization-based formulation for each benefit to obtain the desired compliance profile.Furthermore,we analytically prove that,compared to linear compliance,using nonlinear compliance leads to(1)lower energy consumption,(2)better closed-loop performance,specifically in terms of tracking error,and(3)a higher diversity of natural frequencies.To compare the performance of linear and nonlinear compliance,we apply the proposed methods to a diverse set of robotic systems performing cyclic tasks,including a 2-DOF manipulator,a 3-DOF bipedal walker,and a hopper model.Compared to linear compliance,the nonlinear compliance leads to better performance in all aspects;for example,a 70%reduction in energy consumption and tracking error for the manipulator simulation.Regarding gait optimality,for all robotic simulation models,compared to linear compliance,the nonlinear compliance has lower energy consumption and tracking error over the considered set of motions.The proposed analytical studies and simulation results strongly support the idea that using nonlinear compliance significantly improves robotic system performance in terms of energy efficiency,control robustness,and gait optimality.展开更多
Terminal iterative learning control(TILC) is developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations under strictly identical initial con...Terminal iterative learning control(TILC) is developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations under strictly identical initial conditions. In this work, the initial states are not required to be identical further but can be varying from iteration to iteration. In addition, the desired terminal point is not fixed any more but is allowed to change run-to-run. Consequently, a new adaptive TILC is proposed with a neural network initial state learning mechanism to achieve the learning objective over iterations. The neural network is used to approximate the effect of iteration-varying initial states on the terminal output and the neural network weights are identified iteratively along the iteration axis.A dead-zone scheme is developed such that both learning and adaptation are performed only if the terminal tracking error is outside a designated error bound. It is shown that the proposed approach is able to track run-varying terminal desired points fast with a specified tracking accuracy beyond the initial state variance.展开更多
By introducing a deadwzone scheme, a new neural network based adaptive iterative learning control （ILC） （NN-AILC） scheme is presented for nonlinear discrete-time systems, where the NN weights are time-varying. The...By introducing a deadwzone scheme, a new neural network based adaptive iterative learning control （ILC） （NN-AILC） scheme is presented for nonlinear discrete-time systems, where the NN weights are time-varying. The most distinct contribution of the proposed NN-AILC is the relaxation of the identical conditions of initial state and reference trajectory, which are common requirements in traditional ILC problems. Convergence analysis indicates that the tracking error converges to a bounded ball, whose size is determined by the dead-zone nonlinearity. Computer simulations verify the theoretical results.展开更多
Astrocytes have potential to break synchrony between neurons. Authors＇ recent researches reveal that astrocytes vary the synchronization threshold and provide an appropriate feedback control in stabilizing neural act...Astrocytes have potential to break synchrony between neurons. Authors＇ recent researches reveal that astrocytes vary the synchronization threshold and provide an appropriate feedback control in stabilizing neural activities. In this study, we propose an astrocyte-inspired controller for desynchronization of two coupled limit-cycle oscillators as a minimal network model. The design procedure consists of two parts. First, based on the astrocyte model, the structure of the dynamic controller is suggested. Then, to have an emcient controller, parameters of controller are tuned through an optimization algo- rithm. The proposed bio-inspired controller takes advantages of three important proper- ties： （1） the controller desynchronizes the oscillators without any undesirable effects （e.g. stopping, annihilating or starting divergent oscillations）; （2） it consumes little effort to preserve the desirable desynchronized state; and （3） the controller is robust with respect to parameters＇ variations. Simulation results reveal the ability of the proposed controller.展开更多
An optical scrambler using a whispering-gallery-mode[WGM]micro-bottle cavity to scramble a complex optical signal to generate an uncorrelated output is proposed.We experimentally demonstrated this micro-cavity scrambl...An optical scrambler using a whispering-gallery-mode[WGM]micro-bottle cavity to scramble a complex optical signal to generate an uncorrelated output is proposed.We experimentally demonstrated this micro-cavity scrambler by using chaotic laser light as the incident signal and studied the influence of the coupling state.Experiments achieved full scrambling with a low cross correlation of 0.028 between the output and the input.Results indicate that the scrambling effect originates from the interference among numerous WGMs in the bottle cavity.It is believed that the micro-bottle cavity with an efficient scrambling function can become a promising candidate for encryption.展开更多
Synchronization of neurons plays an important role in vision, movement and memory. However, many neurological disorders such as epilepsies, Parkinson disease and essen- tial tremor are related to excessive synchroniza...Synchronization of neurons plays an important role in vision, movement and memory. However, many neurological disorders such as epilepsies, Parkinson disease and essen- tial tremor are related to excessive synchronization of neurons. In the line of therapy, stimulations to these pathologically synchronized neurons should be capable of breaking synchrony. As the first step of designing an effective stimulation, we consider desynchro- nization problem of coupled limit-cycle oscillators ensemble. First, the desynchronization problem is redefined in a nonlinear output regulation framework. Then, we design an output regulator （stimulation） which forces limit-cycle oscillators to track exogenous sinusoidal references with different phases. The proposed stimulation is robust against variations of oscillators＇ frequencies. Mathematical analysis and simulation results reveal the efficiency of the proposed technique.展开更多
基金Supported by National Natural science Foundation-of P.R.Chlna （60474038, 60774022）, Specialized Research Fund for the Doctoral Program of Higher Educatlon（20060004002）
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 Beijing Natural Science Foundation(No.4142017)International Cooperation Project of National Natural Science Foundation of China(No.61120106009)Beijing Science and Technology Commission Precision Machinery Projects(No.Z121100001612007)
文摘Nowadays, high-precision motion controls are needed in modern manufacturing industry. A data-driven nonparametric model adaptive control(NMAC) method is proposed in this paper to control the position of a linear servo system. The controller design requires no information about the structure of linear servo system, and it is based on the estimation and forecasting of the pseudo-partial derivatives(PPD) which are estimated according to the voltage input and position output of the linear motor. The characteristics and operational mechanism of the permanent magnet synchronous linear motor(PMSLM) are introduced, and the proposed nonparametric model control strategy has been compared with the classic proportional-integral-derivative(PID) control algorithm. Several real-time experiments on the motion control system incorporating a permanent magnet synchronous linear motor showed that the nonparametric model adaptive control method improved the system s response to disturbances and its position-tracking precision, even for a nonlinear system with incompletely known dynamic characteristics.
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 by the General Program (No.60774022)the State Key Program of National Natural Science Foundation of China(No.60834001)the State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University (No.RCS2009ZT011)
文摘In this paper, iterative learning control （ILC） design is studied for an iteration-varying tracking problem in which reference trajectories are generated by high-order internal models （HOLM）. An HOlM formulated as a polynomial operator between consecutive iterations describes the changes of desired trajectories in the iteration domain and makes the iterative learning problem become iteration varying. The classical ILC for tracking iteration-invariant reference trajectories, on the other hand, is a special case of HOlM where the polynomial renders to a unity coefficient or a special first-order internal model. By inserting the HOlM into P-type ILC, the tracking performance along the iteration axis is investigated for a class of continuous-time nonlinear systems. Time-weighted norm method is utilized to guarantee validity of proposed algorithm in a sense of data-driven control.
基金Supported by the National Natural Science Foundation of China （60974040, 61120106009）, the Research Award Foundation for the Excellent Youth Scientists of Shandong Province of China （BS2011DX010）, and the High School Science ＆ Technol- ogy Fund Planning Project of Shandong Province of China （J 10LG32）.
文摘This work presents an anticipatory terminal iterative learning control scheme for a class of batch proc- esses, where only the final system output is measurable and the control input is constant in each operations. The propgsed approach works well with input constraints provided that the desired control input with respect to the desired trajectory is within the samratiorl bound. The tracking error convergence is established with rigorous mathe- matical analysis. Simulation results .are provided to showthe effectiveness, of the proposed approach.
基金supported by the National Natural Science Foundation of China(No.60874041,60834001)the Youth Foundation of Henan University of Science and Technology(No.13440018)
文摘In this paper,nonlinear observers are incorporated into the adaptive control to synthesize controllers for a class of uncertain nonlinear systems with unknown sinusoidal disturbances which are presented in matched and unmatched forms.In addition to magnitudes and phases,frequencies of the sinusoidal disturbances need not be known as well,so long as the overall order is known.Nonlinear observers are constructed to eliminate the effect of unknown sinusoidal disturbances to improve the steady-state output tracking performance-asymptotic output tracking is achieved.The adaptation law is used to obtain the estimate of all unknown parameters.The presented disturbance decoupling algorithms can deal with matched and unmatched unknown sinusoidal disturbances.
文摘ADAPTIVE control is a proven method for learning feedback controllers for systems with unknown dynamic models,exogenous disturbances,nonzero setpoints,and unmodeled nonlinearities.Adaptive control has been applied for years in process control,industry,aerospace systems。
文摘Nonlinearity in parallel compliance can be exploited to improve the performance of locomotion systems in terms of(1)energy efficiency,(2)control robustness,and(3)gait optimality;that is,attaining energy efficiency across a set of motions.Thus far,the literature has investigated and validated only the first two benefits.In this study,we present a new mathematical framework for designing nonlinear compliances in cyclic tasks encompassing all three benefits.We present an optimization-based formulation for each benefit to obtain the desired compliance profile.Furthermore,we analytically prove that,compared to linear compliance,using nonlinear compliance leads to(1)lower energy consumption,(2)better closed-loop performance,specifically in terms of tracking error,and(3)a higher diversity of natural frequencies.To compare the performance of linear and nonlinear compliance,we apply the proposed methods to a diverse set of robotic systems performing cyclic tasks,including a 2-DOF manipulator,a 3-DOF bipedal walker,and a hopper model.Compared to linear compliance,the nonlinear compliance leads to better performance in all aspects;for example,a 70%reduction in energy consumption and tracking error for the manipulator simulation.Regarding gait optimality,for all robotic simulation models,compared to linear compliance,the nonlinear compliance has lower energy consumption and tracking error over the considered set of motions.The proposed analytical studies and simulation results strongly support the idea that using nonlinear compliance significantly improves robotic system performance in terms of energy efficiency,control robustness,and gait optimality.
基金Supported by State Key Program of National Natural Science Foundation of China （60834001） and National Natural Science Foundation of China （60774022）.Acknowledgement Authors would like to thank NSFC organizers and participants who shared their ideas and works with us during the NSFC workshop on data-based control, decision making, scheduling, and fault diagnosis. In particular, authors would like to thank Chai Tian-You, Sun You-Xian, Wang Hong, Yan Hong-Sheng, and Gao Fu-Rong for discussing the concept on design model shown in Fig. 12, the concept on temporal multi-scale shown in Fig. 8, the concept on fault diagnosis shown in Fig. 14, the concept on dynamic scheduling shown in Fig. 15, and the concept on interval model shown in Fig. 16, respectively.
基金supported by National Natural Science Foundation of China(Nos.61374102,61433002 and 61120106009)High Education Science&Technology Fund Planning Project of Shandong Province of China(No.J14LN30)
文摘Terminal iterative learning control(TILC) is developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations under strictly identical initial conditions. In this work, the initial states are not required to be identical further but can be varying from iteration to iteration. In addition, the desired terminal point is not fixed any more but is allowed to change run-to-run. Consequently, a new adaptive TILC is proposed with a neural network initial state learning mechanism to achieve the learning objective over iterations. The neural network is used to approximate the effect of iteration-varying initial states on the terminal output and the neural network weights are identified iteratively along the iteration axis.A dead-zone scheme is developed such that both learning and adaptation are performed only if the terminal tracking error is outside a designated error bound. It is shown that the proposed approach is able to track run-varying terminal desired points fast with a specified tracking accuracy beyond the initial state variance.
基金supported by General Program (60774022)State Key Program (60834001) of National Natural Science Foundation of ChinaDoctoral Foundation of Qingdao University of Science & Technology (0022324)
文摘By introducing a deadwzone scheme, a new neural network based adaptive iterative learning control （ILC） （NN-AILC） scheme is presented for nonlinear discrete-time systems, where the NN weights are time-varying. The most distinct contribution of the proposed NN-AILC is the relaxation of the identical conditions of initial state and reference trajectory, which are common requirements in traditional ILC problems. Convergence analysis indicates that the tracking error converges to a bounded ball, whose size is determined by the dead-zone nonlinearity. Computer simulations verify the theoretical results.
文摘Astrocytes have potential to break synchrony between neurons. Authors＇ recent researches reveal that astrocytes vary the synchronization threshold and provide an appropriate feedback control in stabilizing neural activities. In this study, we propose an astrocyte-inspired controller for desynchronization of two coupled limit-cycle oscillators as a minimal network model. The design procedure consists of two parts. First, based on the astrocyte model, the structure of the dynamic controller is suggested. Then, to have an emcient controller, parameters of controller are tuned through an optimization algo- rithm. The proposed bio-inspired controller takes advantages of three important proper- ties： （1） the controller desynchronizes the oscillators without any undesirable effects （e.g. stopping, annihilating or starting divergent oscillations）; （2） it consumes little effort to preserve the desirable desynchronized state; and （3） the controller is robust with respect to parameters＇ variations. Simulation results reveal the ability of the proposed controller.
基金supported by the National Key Research and Development Program of China(No.2019YFB1803500)the National Natural Science Foundation of China(NSFC)(Nos.62105233,62035009,and 61731014)+4 种基金the Natural Science Foundation of Shanxi Province(Nos.20210302124536 and 20210302123183)the Major Key Project of PCL(No.PCL2021A14)the Shanxi“1331 Project”Key Innovative Teamthe Program for Guangdong Introducing Innovative and Entrepreneurial Teamsthe International Cooperation of Key R&D Program of Shanxi Province(No.201903D421012)。
文摘An optical scrambler using a whispering-gallery-mode[WGM]micro-bottle cavity to scramble a complex optical signal to generate an uncorrelated output is proposed.We experimentally demonstrated this micro-cavity scrambler by using chaotic laser light as the incident signal and studied the influence of the coupling state.Experiments achieved full scrambling with a low cross correlation of 0.028 between the output and the input.Results indicate that the scrambling effect originates from the interference among numerous WGMs in the bottle cavity.It is believed that the micro-bottle cavity with an efficient scrambling function can become a promising candidate for encryption.
文摘Synchronization of neurons plays an important role in vision, movement and memory. However, many neurological disorders such as epilepsies, Parkinson disease and essen- tial tremor are related to excessive synchronization of neurons. In the line of therapy, stimulations to these pathologically synchronized neurons should be capable of breaking synchrony. As the first step of designing an effective stimulation, we consider desynchro- nization problem of coupled limit-cycle oscillators ensemble. First, the desynchronization problem is redefined in a nonlinear output regulation framework. Then, we design an output regulator （stimulation） which forces limit-cycle oscillators to track exogenous sinusoidal references with different phases. The proposed stimulation is robust against variations of oscillators＇ frequencies. Mathematical analysis and simulation results reveal the efficiency of the proposed technique.
