Robot interaction control with variable impedance parameters may conform to task requirements during continuous interaction with dynamic environments.Iterative learning(IL)is effective to learn desired impedance param...Robot interaction control with variable impedance parameters may conform to task requirements during continuous interaction with dynamic environments.Iterative learning(IL)is effective to learn desired impedance parameters for robots under unknown environments,and Gaussian process(GP)is a nonparametric Bayesian approach that models complicated functions with provable confidence using limited data.In this paper,we propose an impedance IL method enhanced by a sparse online Gaussian process(SOGP)to speed up learning convergence and improve generalization.The SOGP for variable impedance modeling is updated in the same iteration by removing similar data points from previous iterations while learning impedance parameters in multiple iterations.The proposed IL-SOGP method is verified by high-fidelity simulations of a collaborative robot with 7 degrees of freedom based on the admittance control framework.It is shown that the proposed method accelerates iterative convergence and improves generalization compared to the classical IL-based impedance learning method.展开更多
The composite implicit iteration process introduced by Su and Li [J. Math. Anal. Appl. 320 (2006) 882-891] is modified. A strong convergence theorem for approximation of common fixed points of finite family of k-stric...The composite implicit iteration process introduced by Su and Li [J. Math. Anal. Appl. 320 (2006) 882-891] is modified. A strong convergence theorem for approximation of common fixed points of finite family of k-strictly asymptotically pseudo-contractive mappings is proved in Banach spaces using the modified iteration process.展开更多
A model for both stochastic jumps and volatility for equity returns in the area of option pricing is the stochastic volatility process with jumps (SVPJ). A major advantage of this model lies in the area of mean revers...A model for both stochastic jumps and volatility for equity returns in the area of option pricing is the stochastic volatility process with jumps (SVPJ). A major advantage of this model lies in the area of mean reversion and volatility clustering between returns and volatility with uphill movements in price asserts. Thus, in this article, we propose to solve the SVPJ model numerically through a discretized variational iteration method (DVIM) to obtain sample paths for the state variable and variance process at various timesteps and replications in order to estimate the expected jump times at various iterates resulting from executing the DVIM as n increases. These jumps help in estimating the degree of randomness in the financial market. It was observed that the average computed expected jump times for the state variable and variance process is moderated by the parameters (variance process through mean reversion), Θ (long-run mean of the variance process), σ (volatility variance process) and λ (constant intensity of the Poisson process) at each iterate. For instance, when = 0.0, Θ = 0.0, σ = 0.0 and λ = 1.0, the state variable cluttered maximally compared to the variance process with less volatility cluttering with an average computed expected jump times of 52.40607869 as n increases in the DVIM scheme. Similarly, when = 3.99, Θ = 0.014, σ = 0.27 and λ = 0.11, the stochastic jumps for the state variable are less cluttered compared to the variance process with maximum volatility cluttering as n increases in the DVIM scheme. In terms of option pricing, the value 52.40607869 suggest a better bargain compared to the value 20.40344029 due to the fact that it yields less volatility rate. MAPLE 18 software was used for all computations in this research.展开更多
In this paper,we introduce a three-step composite implicit iteration process for approximating the common fixed point of three uniformly continuous and asymptotically generalizedΦ-hemicontractive mappings in the inte...In this paper,we introduce a three-step composite implicit iteration process for approximating the common fixed point of three uniformly continuous and asymptotically generalizedΦ-hemicontractive mappings in the intermediate sense.We prove that our proposed iteration process converges to the common fixed point of three finite family of asymptotically generalizedΦ-hemicontractive mappings in the intermediate sense.Our results extends,improves and complements several known results in literature.展开更多
There is a contradiction between high processing complexity and limited processing resources when turbo codes are used on the on-board processing(OBP)satellite platform.To solve this problem,this paper proposes a part...There is a contradiction between high processing complexity and limited processing resources when turbo codes are used on the on-board processing(OBP)satellite platform.To solve this problem,this paper proposes a partial iterative decode method for on-board application,in which satellite only carries out limited number of iteration according to the on-board processing resource limitation and the throughput capacity requirements.In this method,the soft information of parity bits,which is not obtained individually in conventional turbo decoder,is encoded and forwarded along with those of information bits.To save downlink transmit power,the soft information is limited and normalized before forwarding.The iteration number and limiter parameters are optimized with the help of EXIT chart and numerical analysis,respectively.Simulation results show that the proposed method can effectively decrease the complexity of onboard processing while achieve most of the decoding gain..展开更多
The oxide-zeolite process provides a promising way for one-step production of aromatics from syngas,whereas the reasons for the dramatic effect of intimacy between oxide and zeolite in the composite catalyst on the pr...The oxide-zeolite process provides a promising way for one-step production of aromatics from syngas,whereas the reasons for the dramatic effect of intimacy between oxide and zeolite in the composite catalyst on the product selectivity are still unclear. In order to explore the optimal intimacy and the essential influence factors, ZnCrOxcombined with ZSM-5 are employed to prepare the composite catalysts with different distances between the two components by changing the mixing methods. An aromatic selectivity of 74%(with CO conversion to be 16%) is achieved by the composite catalyst when the intimacy is in the range of nanometer. A so-called ‘iterative reactions’ mechanism of intermediates over oxides is then proposed and studied: the intermediate chemical can undergo a hydrogenation reaction on oxide.So the shorter the intermediates stay on oxide, the more of chance for C-C coupling takes place on zeolite to form aromatics. Moreover, the aero-environments of reaction is found to impact on the extent of iterative reaction as well. Therefore, when the intimacy between the two components changes, the extent of iterative reactions vary, resulting in alteration of product distribution. This work provides new insight in understanding the mechanisms during the complex process of OX-ZEO composite catalysis and sheds light to the design of a high-yield catalyst for synthetization of aromatics from syngas.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
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 iterative learning model predictive control （ILMPC） technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong ...An iterative learning model predictive control （ILMPC） technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control （ILC） handling repetitive signal and the flexibility of model predictive control （MPC）. By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial application show that the proposed ILMPC method is effective for a class of continuous/batch processes.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 addresses the problem of robust iterative learning control design for a class of uncertain multiple-input multipleoutput discrete linear systems with actuator faults. The stability theory for linear repetit...This paper addresses the problem of robust iterative learning control design for a class of uncertain multiple-input multipleoutput discrete linear systems with actuator faults. The stability theory for linear repetitive processes is used to develop formulas for gain matrices design, together with convergent conditions in terms of linear matrix inequalities. An extension to deal with model uncertainty of the polytopic or norm bounded form is also developed and an illustrative example is given.展开更多
Two modified BP algorithms related to vertical and horizontal processes are proposed to accelerate iterative low-density parity- check （LDPC） decoding over an additive white Gaussian noise （AWGN） channel, where th...Two modified BP algorithms related to vertical and horizontal processes are proposed to accelerate iterative low-density parity- check （LDPC） decoding over an additive white Gaussian noise （AWGN） channel, where the newly updated extrinsic information is immediately used in the current decoding round. Theoretical analysis and simulation results demonstrate that both the modified approaches provide significant performance improvements over the traditional BP algorithm with almost no additional decoding complexity. The proposed algorithm with modified horizontal process offers even better performance than another algorithm with the modified horizontal process. The two modified BP algorithms are very promising in practical communications since both can achieve an excellent trade-off between the performance and decoding complexity.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
The purpose of this paper is to introduce the concept of Φ_pseudo contractive type mapping and to study the convergence problem of Ishikawa and Mann iterative processes with error for this kind of mappings. The resul...The purpose of this paper is to introduce the concept of Φ_pseudo contractive type mapping and to study the convergence problem of Ishikawa and Mann iterative processes with error for this kind of mappings. The results presented in this paper improve and extend many authors'recent results.展开更多
In this paper, based on the implicit Runge-Kutta(IRK) methods, we derive a class of parallel scheme that can be implemented on the parallel computers with Ns(N is a positive even number) processors efficiently, and di...In this paper, based on the implicit Runge-Kutta(IRK) methods, we derive a class of parallel scheme that can be implemented on the parallel computers with Ns(N is a positive even number) processors efficiently, and discuss the iteratively B-convergence of the Newton iterative process for solving the algebraic equations of the scheme, secondly we present a strategy providing initial values parallelly for the iterative process. Finally, some numerical results show that our parallel scheme is higher efficient as N is not so large.展开更多
Batch process is a typical multi-phase process. Due to the interaction between the phases of the batch process, high precision control in a single phase cannot guarantee high precision control of the whole batch proce...Batch process is a typical multi-phase process. Due to the interaction between the phases of the batch process, high precision control in a single phase cannot guarantee high precision control of the whole batch process. In order to solve this problem, the guaranteed cost iterative learning control(ILC) of multi-phase batch processes is studied in this paper. Firstly, through introducing the output error, the state error and the extended information, the multi-phase batch process is transformed into an equivalent 2D switched system which has different dimensions. In addition, under the measurable condition, the guaranteed cost iterative learning control law with extended information is designed. The proposed control law ensures not only the stability of the system but also the optimal control performance. Next, in order to study the stability of the system and the minimum running time under the condition of stable running, the multi-Lyapunov function method is used. By means of the average dwell time method, the sufficient conditions ensuring system to be exponentially stable are given in the form of linear matrix inequality(LMI). Finally, the injection molding process is taken as an example to make simulation, which shows the feasibility and effectiveness of the proposed method.展开更多
Let E be a uniformly smooth Banach space, K be a nonempty closed convex subset of E, and suppose: T: K --> K is a continuous Phi-strongly pseudocontractive operator with a bounded range. Using a new analytical meth...Let E be a uniformly smooth Banach space, K be a nonempty closed convex subset of E, and suppose: T: K --> K is a continuous Phi-strongly pseudocontractive operator with a bounded range. Using a new analytical method, under general cases, the Ishikawa iterative process {x(n)} converges strongly to the unique fixed point x* of the operator T were proved. The paper generalizes and extends a lot of recent corresponding results.展开更多
Considering the same initial state error in each repetitive operation in the iterative learning system,a method of arranging the transient process is given.During the current iteration,the system will track the transi...Considering the same initial state error in each repetitive operation in the iterative learning system,a method of arranging the transient process is given.During the current iteration,the system will track the transient function firstly,and then the expected trajectory.After several iterations,the learning system output will trend to the arranged curve,which has avoided the effect of the initial error on the controller.Also the transient time can be changed as you need,which makes the designing simple and the operation easy.Then the detailed designing steps are given via the robot system.At last the simulation of the robot system is given,which shows the validity of the method.展开更多
AI researchers typically formulated probabilistic planning under uncertainty problems using Markov Decision Processes (MDPs).Value Iteration is an inef?cient algorithm for MDPs, because it puts the majority of its eff...AI researchers typically formulated probabilistic planning under uncertainty problems using Markov Decision Processes (MDPs).Value Iteration is an inef?cient algorithm for MDPs, because it puts the majority of its effort into backing up the entire state space, which turns out to be unnecessary in many cases. In order to overcome this problem, many approaches have been proposed. Among them, LAO*, LRTDP and HDP are state-of-the-art ones. All of these use reach ability analysis and heuristics to avoid some unnecessary backups. However, none of these approaches fully exploit the graphical features of the MDPs or use these features to yield the best backup sequence of the state space. We introduce an improved algorithm named Topological Order Value Iteration (TOVI) that can circumvent the problem of unnecessary backups by detecting the structure of MDPs and backing up states based on topological sequences. The experimental results demonstrate the effectiveness and excellent performance of our algorithm.展开更多
Contrary to the opinion about approximation nature of a simple-iteration method, the exact solution of a system of linear algebraic equations (SLAE) in a finite number of iterations with a stationary matrix is demonst...Contrary to the opinion about approximation nature of a simple-iteration method, the exact solution of a system of linear algebraic equations (SLAE) in a finite number of iterations with a stationary matrix is demonstrated. We present a theorem and its proof that confirms the possibility to obtain the finite process and imposes the requirement for the matrix of SLAE. This matrix must be unipotent, i.e. all its eigenvalues to be equal to 1. An example of transformation of SLAE given analytically to the form with a unipotent matrix is presented. It is shown that splitting the unipotent matrix into identity and nilpotent ones results in Cramer’s analytical formulas in a finite number of iterations.展开更多
基金supported in part by the National Research Foundation of Korea(NRF)Grant Funded by the Korea Government(MSIT)(RS-2025-00555064).Recommended by Associate Editor Zengguang Hou.
文摘Robot interaction control with variable impedance parameters may conform to task requirements during continuous interaction with dynamic environments.Iterative learning(IL)is effective to learn desired impedance parameters for robots under unknown environments,and Gaussian process(GP)is a nonparametric Bayesian approach that models complicated functions with provable confidence using limited data.In this paper,we propose an impedance IL method enhanced by a sparse online Gaussian process(SOGP)to speed up learning convergence and improve generalization.The SOGP for variable impedance modeling is updated in the same iteration by removing similar data points from previous iterations while learning impedance parameters in multiple iterations.The proposed IL-SOGP method is verified by high-fidelity simulations of a collaborative robot with 7 degrees of freedom based on the admittance control framework.It is shown that the proposed method accelerates iterative convergence and improves generalization compared to the classical IL-based impedance learning method.
文摘The composite implicit iteration process introduced by Su and Li [J. Math. Anal. Appl. 320 (2006) 882-891] is modified. A strong convergence theorem for approximation of common fixed points of finite family of k-strictly asymptotically pseudo-contractive mappings is proved in Banach spaces using the modified iteration process.
文摘A model for both stochastic jumps and volatility for equity returns in the area of option pricing is the stochastic volatility process with jumps (SVPJ). A major advantage of this model lies in the area of mean reversion and volatility clustering between returns and volatility with uphill movements in price asserts. Thus, in this article, we propose to solve the SVPJ model numerically through a discretized variational iteration method (DVIM) to obtain sample paths for the state variable and variance process at various timesteps and replications in order to estimate the expected jump times at various iterates resulting from executing the DVIM as n increases. These jumps help in estimating the degree of randomness in the financial market. It was observed that the average computed expected jump times for the state variable and variance process is moderated by the parameters (variance process through mean reversion), Θ (long-run mean of the variance process), σ (volatility variance process) and λ (constant intensity of the Poisson process) at each iterate. For instance, when = 0.0, Θ = 0.0, σ = 0.0 and λ = 1.0, the state variable cluttered maximally compared to the variance process with less volatility cluttering with an average computed expected jump times of 52.40607869 as n increases in the DVIM scheme. Similarly, when = 3.99, Θ = 0.014, σ = 0.27 and λ = 0.11, the stochastic jumps for the state variable are less cluttered compared to the variance process with maximum volatility cluttering as n increases in the DVIM scheme. In terms of option pricing, the value 52.40607869 suggest a better bargain compared to the value 20.40344029 due to the fact that it yields less volatility rate. MAPLE 18 software was used for all computations in this research.
文摘In this paper,we introduce a three-step composite implicit iteration process for approximating the common fixed point of three uniformly continuous and asymptotically generalizedΦ-hemicontractive mappings in the intermediate sense.We prove that our proposed iteration process converges to the common fixed point of three finite family of asymptotically generalizedΦ-hemicontractive mappings in the intermediate sense.Our results extends,improves and complements several known results in literature.
基金supported by National High Technology Research and Development Program(863 Program,2012AA01A502)National Natural Science Foundation of China (41206031)National Basic Research Program(2012CB316000)
文摘There is a contradiction between high processing complexity and limited processing resources when turbo codes are used on the on-board processing(OBP)satellite platform.To solve this problem,this paper proposes a partial iterative decode method for on-board application,in which satellite only carries out limited number of iteration according to the on-board processing resource limitation and the throughput capacity requirements.In this method,the soft information of parity bits,which is not obtained individually in conventional turbo decoder,is encoded and forwarded along with those of information bits.To save downlink transmit power,the soft information is limited and normalized before forwarding.The iteration number and limiter parameters are optimized with the help of EXIT chart and numerical analysis,respectively.Simulation results show that the proposed method can effectively decrease the complexity of onboard processing while achieve most of the decoding gain..
基金the National Key R&D Program of China(2016YFA0202804)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB17020400)+2 种基金the National Natural Science Foundation of China(Nos.21506204,21476226)Dalian Science Foundation for Distinguished Young Scholars(2016RJ04)the Youth Innovation Promotion Association CAS for financial support
文摘The oxide-zeolite process provides a promising way for one-step production of aromatics from syngas,whereas the reasons for the dramatic effect of intimacy between oxide and zeolite in the composite catalyst on the product selectivity are still unclear. In order to explore the optimal intimacy and the essential influence factors, ZnCrOxcombined with ZSM-5 are employed to prepare the composite catalysts with different distances between the two components by changing the mixing methods. An aromatic selectivity of 74%(with CO conversion to be 16%) is achieved by the composite catalyst when the intimacy is in the range of nanometer. A so-called ‘iterative reactions’ mechanism of intermediates over oxides is then proposed and studied: the intermediate chemical can undergo a hydrogenation reaction on oxide.So the shorter the intermediates stay on oxide, the more of chance for C-C coupling takes place on zeolite to form aromatics. Moreover, the aero-environments of reaction is found to impact on the extent of iterative reaction as well. Therefore, when the intimacy between the two components changes, the extent of iterative reactions vary, resulting in alteration of product distribution. This work provides new insight in understanding the mechanisms during the complex process of OX-ZEO composite catalysis and sheds light to the design of a high-yield catalyst for synthetization of aromatics from syngas.
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,
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 the National Creative Research Groups Science Foundation of China （60721062） and the National High Technology Research and Development Program of China （2007AA04Z162）.
文摘An iterative learning model predictive control （ILMPC） technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control （ILC） handling repetitive signal and the flexibility of model predictive control （MPC）. By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial application show that the proposed ILMPC method is effective for a class of continuous/batch processes.
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(Nos.61273070 and 61203092)111 project(No.B12018)
文摘This paper addresses the problem of robust iterative learning control design for a class of uncertain multiple-input multipleoutput discrete linear systems with actuator faults. The stability theory for linear repetitive processes is used to develop formulas for gain matrices design, together with convergent conditions in terms of linear matrix inequalities. An extension to deal with model uncertainty of the polytopic or norm bounded form is also developed and an illustrative example is given.
基金National Mobile Communication Research Laboratory,Southeast University(No.W200704),ChinaNatural Science foundation of Jiangsu Province (No.BK2006188),ChinaQuebec-China Joint Research Foundation by McGill University,Montreal,Quebec,Canada
文摘Two modified BP algorithms related to vertical and horizontal processes are proposed to accelerate iterative low-density parity- check （LDPC） decoding over an additive white Gaussian noise （AWGN） channel, where the newly updated extrinsic information is immediately used in the current decoding round. Theoretical analysis and simulation results demonstrate that both the modified approaches provide significant performance improvements over the traditional BP algorithm with almost no additional decoding complexity. The proposed algorithm with modified horizontal process offers even better performance than another algorithm with the modified horizontal process. The two modified BP algorithms are very promising in practical communications since both can achieve an excellent trade-off between the performance and decoding complexity.
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.
文摘The purpose of this paper is to introduce the concept of Φ_pseudo contractive type mapping and to study the convergence problem of Ishikawa and Mann iterative processes with error for this kind of mappings. The results presented in this paper improve and extend many authors'recent results.
基金national natural science foundation natural science foundation of Gansu province.
文摘In this paper, based on the implicit Runge-Kutta(IRK) methods, we derive a class of parallel scheme that can be implemented on the parallel computers with Ns(N is a positive even number) processors efficiently, and discuss the iteratively B-convergence of the Newton iterative process for solving the algebraic equations of the scheme, secondly we present a strategy providing initial values parallelly for the iterative process. Finally, some numerical results show that our parallel scheme is higher efficient as N is not so large.
基金the National Natural Science Foundation of China(Nos.61773190 and 61433005)the Guangdong Innovative and Entrepreneurial Research Team Program(No.2013G076)
文摘Batch process is a typical multi-phase process. Due to the interaction between the phases of the batch process, high precision control in a single phase cannot guarantee high precision control of the whole batch process. In order to solve this problem, the guaranteed cost iterative learning control(ILC) of multi-phase batch processes is studied in this paper. Firstly, through introducing the output error, the state error and the extended information, the multi-phase batch process is transformed into an equivalent 2D switched system which has different dimensions. In addition, under the measurable condition, the guaranteed cost iterative learning control law with extended information is designed. The proposed control law ensures not only the stability of the system but also the optimal control performance. Next, in order to study the stability of the system and the minimum running time under the condition of stable running, the multi-Lyapunov function method is used. By means of the average dwell time method, the sufficient conditions ensuring system to be exponentially stable are given in the form of linear matrix inequality(LMI). Finally, the injection molding process is taken as an example to make simulation, which shows the feasibility and effectiveness of the proposed method.
文摘Let E be a uniformly smooth Banach space, K be a nonempty closed convex subset of E, and suppose: T: K --> K is a continuous Phi-strongly pseudocontractive operator with a bounded range. Using a new analytical method, under general cases, the Ishikawa iterative process {x(n)} converges strongly to the unique fixed point x* of the operator T were proved. The paper generalizes and extends a lot of recent corresponding results.
文摘Considering the same initial state error in each repetitive operation in the iterative learning system,a method of arranging the transient process is given.During the current iteration,the system will track the transient function firstly,and then the expected trajectory.After several iterations,the learning system output will trend to the arranged curve,which has avoided the effect of the initial error on the controller.Also the transient time can be changed as you need,which makes the designing simple and the operation easy.Then the detailed designing steps are given via the robot system.At last the simulation of the robot system is given,which shows the validity of the method.
文摘AI researchers typically formulated probabilistic planning under uncertainty problems using Markov Decision Processes (MDPs).Value Iteration is an inef?cient algorithm for MDPs, because it puts the majority of its effort into backing up the entire state space, which turns out to be unnecessary in many cases. In order to overcome this problem, many approaches have been proposed. Among them, LAO*, LRTDP and HDP are state-of-the-art ones. All of these use reach ability analysis and heuristics to avoid some unnecessary backups. However, none of these approaches fully exploit the graphical features of the MDPs or use these features to yield the best backup sequence of the state space. We introduce an improved algorithm named Topological Order Value Iteration (TOVI) that can circumvent the problem of unnecessary backups by detecting the structure of MDPs and backing up states based on topological sequences. The experimental results demonstrate the effectiveness and excellent performance of our algorithm.
文摘Contrary to the opinion about approximation nature of a simple-iteration method, the exact solution of a system of linear algebraic equations (SLAE) in a finite number of iterations with a stationary matrix is demonstrated. We present a theorem and its proof that confirms the possibility to obtain the finite process and imposes the requirement for the matrix of SLAE. This matrix must be unipotent, i.e. all its eigenvalues to be equal to 1. An example of transformation of SLAE given analytically to the form with a unipotent matrix is presented. It is shown that splitting the unipotent matrix into identity and nilpotent ones results in Cramer’s analytical formulas in a finite number of iterations.
