Stochastic approximation problem is to find some root or extremum of a nonlinear function for which only noisy measurements of the function are available. The classical algorithm for stochastic approximation problem i...Stochastic approximation problem is to find some root or extremum of a nonlinear function for which only noisy measurements of the function are available. The classical algorithm for stochastic approximation problem is the Robbins-Monro （RM） algorithm, which uses the noisy evaluation of the negative gradient direction as the iterative direction. In order to accelerate the RM algorithm, this paper gives a flame algorithm using adaptive iterative directions. At each iteration, the new algorithm goes towards either the noisy evaluation of the negative gradient direction or some other directions under some switch criterions. Two feasible choices of the criterions are proposed and two corresponding frame algorithms are formed. Different choices of the directions under the same given switch criterion in the frame can also form different algorithms. We also proposed the simultanous perturbation difference forms for the two frame algorithms. The almost surely convergence of the new algorithms are all established. The numerical experiments show that the new algorithms are promising.展开更多
In this paper, with parametric uncertainties such as the mass of vehicle, the inertia of vehicle about vertical axis, and the tire cornering stiffness, we deal with the vehicle lateral control problem in intelligent v...In this paper, with parametric uncertainties such as the mass of vehicle, the inertia of vehicle about vertical axis, and the tire cornering stiffness, we deal with the vehicle lateral control problem in intelligent vehicle systems. Based on the dynamical model of vehicle, by applying Lyapunov function method, the control problem for lane keeping in the presence of parametric uncertainty is studied, the direct adaptive algorithm to compensate for parametric variations is proposed and the terminal sliding mode variable structure control laws are designed with look-ahead references systems. The stability of the system is investigated from the zero dynamics analysis. Simulation results show that convergence rates of the lateral displacement error, yaw angle error and slid angle are fast.展开更多
Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.T...Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development.展开更多
An improved least mean square/fourth direct adaptive equalizer(LMS/F-DAE)is proposed in this paper for underwater acoustic communication in the Arctic.It is able to process complex-valued baseband signals and has bett...An improved least mean square/fourth direct adaptive equalizer(LMS/F-DAE)is proposed in this paper for underwater acoustic communication in the Arctic.It is able to process complex-valued baseband signals and has better equalization performance than LMS.Considering the sparsity feature of equalizer tap coefficients,an adaptive norm(AN)is incorporated into the cost function which is utilized as a sparse regularization.The norm constraint changes adaptively according to the amplitude of each coefficient.For small-scale coefficients,the sparse constraint exists to accelerate the convergence speed.For large-scale coefficients,it disappears to ensure smaller equalization error.The performance of the proposed AN-LMS/F-DAE is verified by the experimental data from the 9th Chinese National Arctic Research Expedition.The results show that compared with the standard LMS/F-DAE,AN-LMS/F-DAE can promote the sparse level of the equalizer and achieve better performance.展开更多
The conventional direct position determination（DPD） algorithm processes all received signals on a single sensor.When sensors have limited computational capabilities or energy storage,it is desirable to distribute th...The conventional direct position determination（DPD） algorithm processes all received signals on a single sensor.When sensors have limited computational capabilities or energy storage,it is desirable to distribute the computation among other sensors.A distributed adaptive DPD（DADPD）algorithm based on diffusion framework is proposed for emitter localization.Unlike the corresponding centralized adaptive DPD（CADPD） algorithm,all but one sensor in the proposed algorithm participate in processing the received signals and estimating the common emitter position,respectively.The computational load and energy consumption on a single sensor in the CADPD algorithm is distributed among other computing sensors in a balanced manner.Exactly the same iterative localization algorithm is carried out in each computing sensor,respectively,and the algorithm in each computing sensor exhibits quite similar convergence behavior.The difference of the localization and tracking performance between the proposed distributed algorithm and the corresponding CADPD algorithm is negligible through simulation evaluations.展开更多
A control method of direct adaptive control based on gradient estimation is proposed in this article. The dynamic system is embedded in a linear model set. Based on the embedding property of the dynamic system, an ada...A control method of direct adaptive control based on gradient estimation is proposed in this article. The dynamic system is embedded in a linear model set. Based on the embedding property of the dynamic system, an adaptive optimal control algorithm is proposed. The robust convergence of the proposed control algorithm has been proved and the static control error with the proposed method is also analyzed. The application results of the proposed method to the industrial polypropylene process have verified its feasibility and effectiveness.展开更多
For a stochastic non-minimum phase multivariable system,a multiple models direct adaptive controller is presented.It is composed of multiple fixed models with two adaptive models.The fixed models are used to cover the...For a stochastic non-minimum phase multivariable system,a multiple models direct adaptive controller is presented.It is composed of multiple fixed models with two adaptive models.The fixed models are used to cover the region where the system parameters jump and improve the transient response,while another two adaptive models are used to guarantee the stability.Utilizing generalized minimum variance design method,it adopts the stochastic system estimation algorithm with optimal controller design method to identify the controller parameter directly.Finally,the global convergence is given.The simulation proves the effectives of the controller proposed.展开更多
In this paper, a direct adaptive fuzzy tracking control is proposed for a class of uncertain single-input single-output nonlinear semi-strict feedback systems. Based on Takagi-Sugeno type fuzzy systems, a direct adapt...In this paper, a direct adaptive fuzzy tracking control is proposed for a class of uncertain single-input single-output nonlinear semi-strict feedback systems. Based on Takagi-Sugeno type fuzzy systems, a direct adaptive fuzzy tracking controller is developed by using the backstepping approach. The main advantage of the developed method is that for an n-th order system, only one parameter is needed to be adjusted online. It is proven that, under the appropriate assumptions, the developed scheme can achieve that the output system converges to a small neighborhood of the reference signal and all the signals in the closed-loop system remain bounded. The efficacy of the proposed algorithm is investigated by an illustrative simulation example of one link robot.展开更多
The problem of two-dimensional direction finding is approached by using a multi-layer Lshaped array. The proposed method is based on two sequential sparse representations,fulfilling respectively the estimation of elev...The problem of two-dimensional direction finding is approached by using a multi-layer Lshaped array. The proposed method is based on two sequential sparse representations,fulfilling respectively the estimation of elevation angles,and azimuth angles. For the estimation of elevation angles,the weighted sub-array smoothing technique for perfect data decorrelation is used to produce a covariance vector suitable for exact sparse representation,related only to the elevation angles. The estimates of elevation angles are then obtained by sparse restoration associated with this elevation angle dependent covariance vector. The estimates of elevation angles are further incorporated with weighted sub-array smoothing to yield a second covariance vector for precise sparse representation related to both elevation angles,and azimuth angles. The estimates of azimuth angles,automatically paired with the estimates of elevation angles,are finally obtained by sparse restoration associated with this latter elevation-azimuth angle related covariance vector. Simulation results are included to illustrate the performance of the proposed method.展开更多
We consider a wide range of non-convex regularized minimization problems, where the non-convex regularization term is composite with a linear function engaged in sparse learning. Recent theoretical investigations have...We consider a wide range of non-convex regularized minimization problems, where the non-convex regularization term is composite with a linear function engaged in sparse learning. Recent theoretical investigations have demonstrated their superiority over their convex counterparts. The computational challenge lies in the fact that the proximal mapping associated with non-convex regularization is not easily obtained due to the imposed linear composition. Fortunately, the problem structure allows one to introduce an auxiliary variable and reformulate it as an optimization problem with linear constraints, which can be solved using the Linearized Alternating Direction Method of Multipliers （LADMM）. Despite the success of LADMM in practice, it remains unknown whether LADMM is convergent in solving such non-convex compositely regularized optimizations. In this research, we first present a detailed convergence analysis of the LADMM algorithm for solving a non-convex compositely regularized optimization problem with a large class of non-convex penalties. Furthermore, we propose an Adaptive LADMM （AdaLADMM） algorithm with a line-search criterion. Experimental results on different genres of datasets validate the efficacy of the proposed algorithm.展开更多
Target detection by a noncooperative illuminator is a topic of general interest in the electronic warfare field. First of all, direct-path interference （DPI） suppression which is the technique of bottleneck of movin...Target detection by a noncooperative illuminator is a topic of general interest in the electronic warfare field. First of all, direct-path interference （DPI） suppression which is the technique of bottleneck of moving target detection by a noncooperative frequency modulation（FM） broadcast transmitter is analyzed in this article; Secondly, a space-time-frequency domain synthetic solution to this problem is introduced： Adaptive nulling array processing is considered in the space domain, DPI cancellation based on adaptive fractional delay interpolation （AFDI） technique is used in planned time domain, and long-time coherent integration is utilized in the frequency domain; Finaily, an experimental system is planned by considering FM broadcast transmitter as a noncooperative illuminator, Simulation results by real collected data show that the proposed method has a better performance of moving target detection.展开更多
An adaptive beamforming algorithm named robust joint iterative optimizationdirection adaptive （RJIO-DA） is proposed for large-array scenarios. Based on the framework of minimum variance distortionless response （MVD...An adaptive beamforming algorithm named robust joint iterative optimizationdirection adaptive （RJIO-DA） is proposed for large-array scenarios. Based on the framework of minimum variance distortionless response （MVDR）, the proposed algorithm jointly updates a transforming matrix and a reduced-rank filter. Each column of the transforming matrix is treated as an independent direction vector and updates the weight values of each dimension within a subspace. In addition, the direction vector rotation improves the performance of the algorithm by reducing the uncertainties due to the direction error. Simulation results show that the RJIO-DA algorithm has lower complexity and faster convergence than other conventional reduced-rank algorithms.展开更多
Direct adaptive fuzzy sliding mode control design for discrete non-affine nonlinear systems is presented for trajectory tracking problems with disturbance. To obtain adaptiveness and eliminate chattering of sliding mo...Direct adaptive fuzzy sliding mode control design for discrete non-affine nonlinear systems is presented for trajectory tracking problems with disturbance. To obtain adaptiveness and eliminate chattering of sliding mode control, a dynamic fuzzy logical system is used to implement an equivalent control, in which the parameters are self-tuned online. Stability of the sliding mode control is validated using the Lyapunov analysis theory. The overall system is adaptive, asymptotically stable, and chattering-free. A numerical simulation and an application to a robotic arm with two degrees of freedom further verify the good performance of the control design.展开更多
In this paper, a multivariable direct adaptive controller using multiple models without minimum phase assumption is presented to improve the transient response when the parameters of the system jump abruptly. The cont...In this paper, a multivariable direct adaptive controller using multiple models without minimum phase assumption is presented to improve the transient response when the parameters of the system jump abruptly. The controller is composed of multiple fixed controller models, a free-running adaptive con- troller model and a re-initialized adaptive controller model. The fixed controller models are derived from the corresponding fixed system models directly. The adaptive controller models adopt the direct adaptive algorithm to reduce the design calculation. At every instant, the optimal controller is chosen out according to the switching index. The interaction of the system is viewed as the measured distur- bance which is eliminated by the choice of the weighing polynomial matrix. The global convergence is obtained. Finally, several simulation examples in a wind tunnel experiment are given to show both effectiveness and practicality of the proposed method. The significance of the proposed method is that it is applicable to a non-minimum phase system, adopting direct adaptive algorithm to overcome the singularity problem during the matrix calculation and realizing decoupling control for a multivariable system.展开更多
In this paper we present a robust adaptive control for a class of uncertain continuous time multiple input multiple output （MIMO） nonlinear systems. Multiple multi-layer neural networks are employed to approximate t...In this paper we present a robust adaptive control for a class of uncertain continuous time multiple input multiple output （MIMO） nonlinear systems. Multiple multi-layer neural networks are employed to approximate the uncertainty of the nonlinear functions, and robustifying control terms are used to compensate for approximation errors. All parameter adaptive laws and robustifying control terms are derived based on Lyapunov stability analysis so that, under appropriate assumptions, semi-global stability of the closed-loop system is guaranteed, and the tracking error asymptotically converges to zero. Simulations performed on a two-link robot manipulator illustrate the approach and its performance.展开更多
The authors propose a data-driven direct adaptive control law based on the adaptive dynamic programming(ADP) algorithm for continuous-time stochastic linear systems with partially unknown system dynamics and infinite ...The authors propose a data-driven direct adaptive control law based on the adaptive dynamic programming(ADP) algorithm for continuous-time stochastic linear systems with partially unknown system dynamics and infinite horizon quadratic risk-sensitive indices.The authors use online data of the system to iteratively solve the generalized algebraic Riccati equation(GARE) and to learn the optimal control law directly.For the case with measurable system noises,the authors show that the adaptive control law approximates the optimal control law as time goes on.For the case with unmeasurable system noises,the authors use the least-square solution calculated only from the measurable data instead of the real solution of the regression equation to iteratively solve the GARE.The authors also study the influences of the intensity of the system noises,the intensity of the exploration noises,the initial iterative matrix,and the sampling period on the convergence of the ADP algorithm.Finally,the authors present two numerical simulation examples to demonstrate the effectiveness of the proposed algorithms.展开更多
Piezo-actuated stage is a core component in micro-nano manufacturing field.However,the inherent nonlinearity,such as rate-dependent hysteresis,in the piezo-actuated stage severely impacts its tracking accuracy.This st...Piezo-actuated stage is a core component in micro-nano manufacturing field.However,the inherent nonlinearity,such as rate-dependent hysteresis,in the piezo-actuated stage severely impacts its tracking accuracy.This study proposes a direct adaptive control(DAC)method to realize high precision tracking.The proposed controller is designed by a time delay recursive neural network.Compared with those existing DAC methods designed under the general Lipschitz condition,the proposed control method can be easily generalized to the actual systems,which have hysteresis behavior.Then,a hopfield neural network(HNN)estimator is proposed to adjust the parameters of the proposed controller online.Meanwhile,a modular model consisting of linear submodel,hysteresis submodel,and lumped uncertainties is established based on the HNN estimator to describe the piezoactuated stage in this study.Thus,the performance of the HNN estimator can be exhibited visually through the modeling results.The proposed control method eradicates the adverse effects on the control performance arising from the inaccuracy in establishing the offline model and improves the capability to suppress the influence of hysteresis on the tracking accuracy of piezo-actuated stage in comparison with the conventional DAC methods.The stability of the control system is studied.Finally,a series of comparison experiments with a dual neural networks-based data driven adaptive controller are carried out to demonstrate the superiority of the proposed controller.展开更多
This work studies the tracking issue of uncertain nonlinear systems.The existence of odd rational powers,multiple unknown parameters and the dead-zone input add many difficulties for control design.During procedures o...This work studies the tracking issue of uncertain nonlinear systems.The existence of odd rational powers,multiple unknown parameters and the dead-zone input add many difficulties for control design.During procedures of the control design,by introducing an appropriate Lyapunov function,utilizing recursive control method and the inequality technique,some appropriate intermediate auxiliary control laws are designed under the hypothesis that nonlinear terms in the system are known.When those nonlinear terms are unknown,by employing the powerful approximation ability of fuzzy systems,the intermediate auxiliary control laws are approximated recursively and used to construct the virtual control.Finally,a new fuzzy adaptive tracking controller is constructed to ensure a small tracking error and the boundedness of all states.In this paper,the overparameterization problem is significantly avoided since only two adaptive laws are adopted.Numerical and practical examples are used to verify the raised theory.展开更多
The basic principles of target detection by forward acoustic scattering are presented.A direct blast suppression approach based on adaptive filtering(DBS-AF) is proposed to suppress the direct blast.The DBS-AF techniq...The basic principles of target detection by forward acoustic scattering are presented.A direct blast suppression approach based on adaptive filtering(DBS-AF) is proposed to suppress the direct blast.The DBS-AF technique is extended to the linear frequency modulation(LFM) signal,where the envelope of the signal is regarded as a 'general waveform' and imported into the adaptive filter.Application of the DBS-AF method to the data collected from a lake trial yields an output detection curve,in which the direct blast is mapped to the background while the acoustic field aberration is represented by the peak value fluctuation.The inhibitory effect in single hydrophone is approximately- 5 dB,and is then enhanced by exploiting the mean value removal approach as a preprocessing technique.The direct blast is further suppressed to a level of-10 dB by making full use of multichannel receptions.The main factors affecting the algorithm performance are as follows:the fluctuation degree of the receptions during the weighting vector training period and the power ratio of the forward scattered wave to the direct blast when the target is present.展开更多
A new method for the direct adaptive regulation of unknown nonlinear dynamical systems is proposed in this paper,paying special attention to the analysis of the model order problem.The method uses a neurofuzzy (NF) mo...A new method for the direct adaptive regulation of unknown nonlinear dynamical systems is proposed in this paper,paying special attention to the analysis of the model order problem.The method uses a neurofuzzy (NF) modeling of the unknown system,which combines fuzzy systems (FSs) with high order neural networks (HONNs).We propose the approximation of the unknown system by a special form of an NF-dynamical system (NFDS),which,however,may assume a smaller number of states than the original unknown model.The omission of states,referred to as a model order problem,is modeled by introducing a disturbance term in the approximating equations.The development is combined with a sensitivity analysis of the closed loop and provides a comprehensive and rigorous analysis of the stability properties.An adaptive modification method,termed ‘parameter hopping’,is incorporated into the weight estimation algorithm so that the existence and boundedness of the control signal are always assured.The applicability and potency of the method are tested by simulations on well known benchmarks such as ‘DC motor’ and ‘Lorenz system’,where it is shown that it performs quite well under a reduced model order assumption.Moreover,the proposed NF approach is shown to outperform simple recurrent high order neural networks (RHONNs).展开更多
基金supported by the Chinese NSF grants 10571171 and 40233029.
文摘Stochastic approximation problem is to find some root or extremum of a nonlinear function for which only noisy measurements of the function are available. The classical algorithm for stochastic approximation problem is the Robbins-Monro （RM） algorithm, which uses the noisy evaluation of the negative gradient direction as the iterative direction. In order to accelerate the RM algorithm, this paper gives a flame algorithm using adaptive iterative directions. At each iteration, the new algorithm goes towards either the noisy evaluation of the negative gradient direction or some other directions under some switch criterions. Two feasible choices of the criterions are proposed and two corresponding frame algorithms are formed. Different choices of the directions under the same given switch criterion in the frame can also form different algorithms. We also proposed the simultanous perturbation difference forms for the two frame algorithms. The almost surely convergence of the new algorithms are all established. The numerical experiments show that the new algorithms are promising.
基金Sponsored by the National Natural Science Foundation of China(Grant No.10772152)
文摘In this paper, with parametric uncertainties such as the mass of vehicle, the inertia of vehicle about vertical axis, and the tire cornering stiffness, we deal with the vehicle lateral control problem in intelligent vehicle systems. Based on the dynamical model of vehicle, by applying Lyapunov function method, the control problem for lane keeping in the presence of parametric uncertainty is studied, the direct adaptive algorithm to compensate for parametric variations is proposed and the terminal sliding mode variable structure control laws are designed with look-ahead references systems. The stability of the system is investigated from the zero dynamics analysis. Simulation results show that convergence rates of the lateral displacement error, yaw angle error and slid angle are fast.
基金supported partly by the National Science and Technology Major Project of China(Grant No.2016ZX05025-001006)Major Science and Technology Project of CNPC(Grant No.ZD2019-183-007)
文摘Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development.
基金The National Natural Science Foundation of China under contract Nos 61631008 and 61901136the National Key Research and Development Program of China under contract No.2018YFC1405904+3 种基金the Fok Ying-Tong Education Foundation under contract No.151007the Heilongjiang Province Outstanding Youth Science Fund under contract No.JC2017017the Opening Funding of Science and Technology on Sonar Laboratory under contract No.6142109KF201802the Innovation Special Zone of National Defense Science and Technology.
文摘An improved least mean square/fourth direct adaptive equalizer(LMS/F-DAE)is proposed in this paper for underwater acoustic communication in the Arctic.It is able to process complex-valued baseband signals and has better equalization performance than LMS.Considering the sparsity feature of equalizer tap coefficients,an adaptive norm(AN)is incorporated into the cost function which is utilized as a sparse regularization.The norm constraint changes adaptively according to the amplitude of each coefficient.For small-scale coefficients,the sparse constraint exists to accelerate the convergence speed.For large-scale coefficients,it disappears to ensure smaller equalization error.The performance of the proposed AN-LMS/F-DAE is verified by the experimental data from the 9th Chinese National Arctic Research Expedition.The results show that compared with the standard LMS/F-DAE,AN-LMS/F-DAE can promote the sparse level of the equalizer and achieve better performance.
基金supported by the National Natural Science Foundation of China(61101173)
文摘The conventional direct position determination（DPD） algorithm processes all received signals on a single sensor.When sensors have limited computational capabilities or energy storage,it is desirable to distribute the computation among other sensors.A distributed adaptive DPD（DADPD）algorithm based on diffusion framework is proposed for emitter localization.Unlike the corresponding centralized adaptive DPD（CADPD） algorithm,all but one sensor in the proposed algorithm participate in processing the received signals and estimating the common emitter position,respectively.The computational load and energy consumption on a single sensor in the CADPD algorithm is distributed among other computing sensors in a balanced manner.Exactly the same iterative localization algorithm is carried out in each computing sensor,respectively,and the algorithm in each computing sensor exhibits quite similar convergence behavior.The difference of the localization and tracking performance between the proposed distributed algorithm and the corresponding CADPD algorithm is negligible through simulation evaluations.
基金Supported by the National Natural Science Foundation of China （60774080） and BJNOVA 2005B 15.
文摘A control method of direct adaptive control based on gradient estimation is proposed in this article. The dynamic system is embedded in a linear model set. Based on the embedding property of the dynamic system, an adaptive optimal control algorithm is proposed. The robust convergence of the proposed control algorithm has been proved and the static control error with the proposed method is also analyzed. The application results of the proposed method to the industrial polypropylene process have verified its feasibility and effectiveness.
基金the National Natural Science Foundation of China (Nos.60504010 and 60774015)the National High Technology Research and Development Program (863) of China (No.2008AA04Z129)+1 种基金the Disbursal Budget Program of Shanghai Municipal Education Commission of China (No.2008093) the Innovation Program of Shanghai Municipal Education Commission of China (No.09YZ241)
文摘For a stochastic non-minimum phase multivariable system,a multiple models direct adaptive controller is presented.It is composed of multiple fixed models with two adaptive models.The fixed models are used to cover the region where the system parameters jump and improve the transient response,while another two adaptive models are used to guarantee the stability.Utilizing generalized minimum variance design method,it adopts the stochastic system estimation algorithm with optimal controller design method to identify the controller parameter directly.Finally,the global convergence is given.The simulation proves the effectives of the controller proposed.
文摘In this paper, a direct adaptive fuzzy tracking control is proposed for a class of uncertain single-input single-output nonlinear semi-strict feedback systems. Based on Takagi-Sugeno type fuzzy systems, a direct adaptive fuzzy tracking controller is developed by using the backstepping approach. The main advantage of the developed method is that for an n-th order system, only one parameter is needed to be adjusted online. It is proven that, under the appropriate assumptions, the developed scheme can achieve that the output system converges to a small neighborhood of the reference signal and all the signals in the closed-loop system remain bounded. The efficacy of the proposed algorithm is investigated by an illustrative simulation example of one link robot.
基金Supported by the National Natural Science Foundation of China(61331019,61490691)
文摘The problem of two-dimensional direction finding is approached by using a multi-layer Lshaped array. The proposed method is based on two sequential sparse representations,fulfilling respectively the estimation of elevation angles,and azimuth angles. For the estimation of elevation angles,the weighted sub-array smoothing technique for perfect data decorrelation is used to produce a covariance vector suitable for exact sparse representation,related only to the elevation angles. The estimates of elevation angles are then obtained by sparse restoration associated with this elevation angle dependent covariance vector. The estimates of elevation angles are further incorporated with weighted sub-array smoothing to yield a second covariance vector for precise sparse representation related to both elevation angles,and azimuth angles. The estimates of azimuth angles,automatically paired with the estimates of elevation angles,are finally obtained by sparse restoration associated with this latter elevation-azimuth angle related covariance vector. Simulation results are included to illustrate the performance of the proposed method.
基金supported by the National Natural Science Foundation of China(Nos.61303264,61202482,and 61202488)Guangxi Cooperative Innovation Center of Cloud Computing and Big Data(No.YD16505)Distinguished Young Scientist Promotion of National University of Defense Technology
文摘We consider a wide range of non-convex regularized minimization problems, where the non-convex regularization term is composite with a linear function engaged in sparse learning. Recent theoretical investigations have demonstrated their superiority over their convex counterparts. The computational challenge lies in the fact that the proximal mapping associated with non-convex regularization is not easily obtained due to the imposed linear composition. Fortunately, the problem structure allows one to introduce an auxiliary variable and reformulate it as an optimization problem with linear constraints, which can be solved using the Linearized Alternating Direction Method of Multipliers （LADMM）. Despite the success of LADMM in practice, it remains unknown whether LADMM is convergent in solving such non-convex compositely regularized optimizations. In this research, we first present a detailed convergence analysis of the LADMM algorithm for solving a non-convex compositely regularized optimization problem with a large class of non-convex penalties. Furthermore, we propose an Adaptive LADMM （AdaLADMM） algorithm with a line-search criterion. Experimental results on different genres of datasets validate the efficacy of the proposed algorithm.
文摘Target detection by a noncooperative illuminator is a topic of general interest in the electronic warfare field. First of all, direct-path interference （DPI） suppression which is the technique of bottleneck of moving target detection by a noncooperative frequency modulation（FM） broadcast transmitter is analyzed in this article; Secondly, a space-time-frequency domain synthetic solution to this problem is introduced： Adaptive nulling array processing is considered in the space domain, DPI cancellation based on adaptive fractional delay interpolation （AFDI） technique is used in planned time domain, and long-time coherent integration is utilized in the frequency domain; Finaily, an experimental system is planned by considering FM broadcast transmitter as a noncooperative illuminator, Simulation results by real collected data show that the proposed method has a better performance of moving target detection.
基金supported by the National Science&Technology Pillar Program(2013BAF07B03)Zhejiang Provincial Natural Science Foundation of China(LY13F010009)
文摘An adaptive beamforming algorithm named robust joint iterative optimizationdirection adaptive （RJIO-DA） is proposed for large-array scenarios. Based on the framework of minimum variance distortionless response （MVDR）, the proposed algorithm jointly updates a transforming matrix and a reduced-rank filter. Each column of the transforming matrix is treated as an independent direction vector and updates the weight values of each dimension within a subspace. In addition, the direction vector rotation improves the performance of the algorithm by reducing the uncertainties due to the direction error. Simulation results show that the RJIO-DA algorithm has lower complexity and faster convergence than other conventional reduced-rank algorithms.
基金Project supported by the National Natural Science Foundation of China （No. 61304024）, the Science and Technology Project of Hebei Province, China （No. 15272118）, and the Fundamental Research Funds for the Central Universities, China （No. 3142015101）
文摘Direct adaptive fuzzy sliding mode control design for discrete non-affine nonlinear systems is presented for trajectory tracking problems with disturbance. To obtain adaptiveness and eliminate chattering of sliding mode control, a dynamic fuzzy logical system is used to implement an equivalent control, in which the parameters are self-tuned online. Stability of the sliding mode control is validated using the Lyapunov analysis theory. The overall system is adaptive, asymptotically stable, and chattering-free. A numerical simulation and an application to a robotic arm with two degrees of freedom further verify the good performance of the control design.
基金Supported by the National Natural Science Foundation of China (Grant Nos. 60504010, 60864004)the National High-Tech Research and Development Program of China (Grant No. 2008AA04Z129)+1 种基金the Key Project of Chinese Ministry of Education (Grant No. 208071)the Natural Science Foundation of Jiangxi Province (Grant No. 0611006)
文摘In this paper, a multivariable direct adaptive controller using multiple models without minimum phase assumption is presented to improve the transient response when the parameters of the system jump abruptly. The controller is composed of multiple fixed controller models, a free-running adaptive con- troller model and a re-initialized adaptive controller model. The fixed controller models are derived from the corresponding fixed system models directly. The adaptive controller models adopt the direct adaptive algorithm to reduce the design calculation. At every instant, the optimal controller is chosen out according to the switching index. The interaction of the system is viewed as the measured distur- bance which is eliminated by the choice of the weighing polynomial matrix. The global convergence is obtained. Finally, several simulation examples in a wind tunnel experiment are given to show both effectiveness and practicality of the proposed method. The significance of the proposed method is that it is applicable to a non-minimum phase system, adopting direct adaptive algorithm to overcome the singularity problem during the matrix calculation and realizing decoupling control for a multivariable system.
基金the National Aviation Cooperation Research Foun-dation of China (No. 10577012)
文摘In this paper we present a robust adaptive control for a class of uncertain continuous time multiple input multiple output （MIMO） nonlinear systems. Multiple multi-layer neural networks are employed to approximate the uncertainty of the nonlinear functions, and robustifying control terms are used to compensate for approximation errors. All parameter adaptive laws and robustifying control terms are derived based on Lyapunov stability analysis so that, under appropriate assumptions, semi-global stability of the closed-loop system is guaranteed, and the tracking error asymptotically converges to zero. Simulations performed on a two-link robot manipulator illustrate the approach and its performance.
基金supported in part by the National Natural Science Foundation of China under Grant No.62261136550in part by the Basic Research Project of Shanghai Science and Technology Commission under Grant No.20JC1414000。
文摘The authors propose a data-driven direct adaptive control law based on the adaptive dynamic programming(ADP) algorithm for continuous-time stochastic linear systems with partially unknown system dynamics and infinite horizon quadratic risk-sensitive indices.The authors use online data of the system to iteratively solve the generalized algebraic Riccati equation(GARE) and to learn the optimal control law directly.For the case with measurable system noises,the authors show that the adaptive control law approximates the optimal control law as time goes on.For the case with unmeasurable system noises,the authors use the least-square solution calculated only from the measurable data instead of the real solution of the regression equation to iteratively solve the GARE.The authors also study the influences of the intensity of the system noises,the intensity of the exploration noises,the initial iterative matrix,and the sampling period on the convergence of the ADP algorithm.Finally,the authors present two numerical simulation examples to demonstrate the effectiveness of the proposed algorithms.
基金supported by the National Natural Science Foundation of China(Grant Nos.51675228 and 51875237)the Key Project of Science and Technology Development Plan of Jilin Province,China(Grant No.20190303020SF)。
文摘Piezo-actuated stage is a core component in micro-nano manufacturing field.However,the inherent nonlinearity,such as rate-dependent hysteresis,in the piezo-actuated stage severely impacts its tracking accuracy.This study proposes a direct adaptive control(DAC)method to realize high precision tracking.The proposed controller is designed by a time delay recursive neural network.Compared with those existing DAC methods designed under the general Lipschitz condition,the proposed control method can be easily generalized to the actual systems,which have hysteresis behavior.Then,a hopfield neural network(HNN)estimator is proposed to adjust the parameters of the proposed controller online.Meanwhile,a modular model consisting of linear submodel,hysteresis submodel,and lumped uncertainties is established based on the HNN estimator to describe the piezoactuated stage in this study.Thus,the performance of the HNN estimator can be exhibited visually through the modeling results.The proposed control method eradicates the adverse effects on the control performance arising from the inaccuracy in establishing the offline model and improves the capability to suppress the influence of hysteresis on the tracking accuracy of piezo-actuated stage in comparison with the conventional DAC methods.The stability of the control system is studied.Finally,a series of comparison experiments with a dual neural networks-based data driven adaptive controller are carried out to demonstrate the superiority of the proposed controller.
基金supported by Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi(STIP)under Grant No.2019L0011the Major Scientific and Technological Innovation Project in Shandong Province under Grant No.2019JZZY011111。
文摘This work studies the tracking issue of uncertain nonlinear systems.The existence of odd rational powers,multiple unknown parameters and the dead-zone input add many difficulties for control design.During procedures of the control design,by introducing an appropriate Lyapunov function,utilizing recursive control method and the inequality technique,some appropriate intermediate auxiliary control laws are designed under the hypothesis that nonlinear terms in the system are known.When those nonlinear terms are unknown,by employing the powerful approximation ability of fuzzy systems,the intermediate auxiliary control laws are approximated recursively and used to construct the virtual control.Finally,a new fuzzy adaptive tracking controller is constructed to ensure a small tracking error and the boundedness of all states.In this paper,the overparameterization problem is significantly avoided since only two adaptive laws are adopted.Numerical and practical examples are used to verify the raised theory.
基金supported by the National Natural Science Foundation of China(11174235,61571366)
文摘The basic principles of target detection by forward acoustic scattering are presented.A direct blast suppression approach based on adaptive filtering(DBS-AF) is proposed to suppress the direct blast.The DBS-AF technique is extended to the linear frequency modulation(LFM) signal,where the envelope of the signal is regarded as a 'general waveform' and imported into the adaptive filter.Application of the DBS-AF method to the data collected from a lake trial yields an output detection curve,in which the direct blast is mapped to the background while the acoustic field aberration is represented by the peak value fluctuation.The inhibitory effect in single hydrophone is approximately- 5 dB,and is then enhanced by exploiting the mean value removal approach as a preprocessing technique.The direct blast is further suppressed to a level of-10 dB by making full use of multichannel receptions.The main factors affecting the algorithm performance are as follows:the fluctuation degree of the receptions during the weighting vector training period and the power ratio of the forward scattered wave to the direct blast when the target is present.
文摘A new method for the direct adaptive regulation of unknown nonlinear dynamical systems is proposed in this paper,paying special attention to the analysis of the model order problem.The method uses a neurofuzzy (NF) modeling of the unknown system,which combines fuzzy systems (FSs) with high order neural networks (HONNs).We propose the approximation of the unknown system by a special form of an NF-dynamical system (NFDS),which,however,may assume a smaller number of states than the original unknown model.The omission of states,referred to as a model order problem,is modeled by introducing a disturbance term in the approximating equations.The development is combined with a sensitivity analysis of the closed loop and provides a comprehensive and rigorous analysis of the stability properties.An adaptive modification method,termed ‘parameter hopping’,is incorporated into the weight estimation algorithm so that the existence and boundedness of the control signal are always assured.The applicability and potency of the method are tested by simulations on well known benchmarks such as ‘DC motor’ and ‘Lorenz system’,where it is shown that it performs quite well under a reduced model order assumption.Moreover,the proposed NF approach is shown to outperform simple recurrent high order neural networks (RHONNs).
