This paper is concerned with the control performance assessment based on the multivariable generalized minimum variance benchmark.An explicit expression for the feedback controller-invariant(the generalized minimum va...This paper is concerned with the control performance assessment based on the multivariable generalized minimum variance benchmark.An explicit expression for the feedback controller-invariant(the generalized minimum variance)term of the multivariable control system is obtained,which is used as a standard benchmark for the assessment of the control performance for multi input multi output(MIMO)process.The proposed approach is based on the multivariable minimum variance benchmark.In comparison with the minimum variance benchmark, the developed method is more reasonable and practical for the control performance assessment of multivariable systems.The approach is illustrated by a simulation example and an industrial application.展开更多
In order to improve the robustness and noise resistance of generalized minimum valance cothrol systems, several generalizedminimum variance control schemes are synthetically analyzed. The output variance caused by st...In order to improve the robustness and noise resistance of generalized minimum valance cothrol systems, several generalizedminimum variance control schemes are synthetically analyzed. The output variance caused by stochastic noise is decomposed to two parts. One part accords with the output variance of minboum vedance control and the other is the additional term of output variance causedby the control weighting factors. At the same time, the sensitivity function of modeling error is also deduced. A new robast design method that can minimize the sensitivity and the additional part of output variance is Presented by regulating variable parameters of contollers. The simulation results of self-tuning control show the effect of this method.展开更多
In an extraction turbine, pressure of the extracted steam and rotate speed of the rotor are two important controlled quantities. The traditional linear state feedback control method is not perfect enough to control th...In an extraction turbine, pressure of the extracted steam and rotate speed of the rotor are two important controlled quantities. The traditional linear state feedback control method is not perfect enough to control the two quantities accurately because of existence of nonlinearity and coupling. A generalized minimum variance control method is studied for an extraction turbine. Firstly, a nonlinear mathematical model of the control system about the two quantities is transformed into a linear system with two white noises. Secondly, a generalized minimum variance control law is applied to the system. A comparative simulation is done. The simulation results indicate that precision and dynamic quality of the regulating system under the new control law are both better than those under the state feedback control law.展开更多
To reduce the vibration in the suspension, semi active suspension system was employed. And its CARMA model was built. Two adaptive control schemes, the minimum variance self tuning control algorithm and the pole con...To reduce the vibration in the suspension, semi active suspension system was employed. And its CARMA model was built. Two adaptive control schemes, the minimum variance self tuning control algorithm and the pole configuration self tuning control algorithm, were proposed. The former can make the variance of the output minimum while the latter can make dynamic behavior satisfying. The stability of the two schemes was analyzed. Simulations of them show that the acceleration in the vertical direction has been reduced greatly. The purpose of reducing vibration is realized. The two schemes can reduce the vibration in the suspension and have some practicability.展开更多
For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential （PID） controller is proposed, which consists of a tra...For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential （PID） controller is proposed, which consists of a traditional PID controller, a decoupling compensator and a feedforward compensator for the unmodeled dynamics. The parameters of such controller is selected based on the generalized minimum variance control law. The unmodeled dynamics is estimated and compensated by neural networks, a switching mechanism is introduced to improve tracking performance, then a nonlinear decoupling PID control algorithm is proposed. All signals in such switching system are globally bounded and the tracking error is convergent. Simulations show effectiveness of the algorithm.展开更多
Performance assessment of a proportional-integral-derivative （PID） controller is condueted using the PIDachievable minimum variance as a benchmark. When the process model is unknown, we can estimate the PID-achievab...Performance assessment of a proportional-integral-derivative （PID） controller is condueted using the PIDachievable minimum variance as a benchmark. When the process model is unknown, we can estimate the PID-achievable minimum variance and the corresponding parameters by routine closed-loop operation data. Simulation results show that the process output variance is reduced by retuning controller parameters.展开更多
The current highly competitive environment has driven industries to operate with increasingly restricted profit margins. Thus, it is imperative to optimize production processes. Faced with this scenario, multivariable...The current highly competitive environment has driven industries to operate with increasingly restricted profit margins. Thus, it is imperative to optimize production processes. Faced with this scenario, multivariable predictive control of processes has been presented as a powerful alternative to achieve these goals. Moreover, the rationale for implementation of advanced control and subsequent analysis of its post-match performance also focus on the benefits that this tool brings to the plant. It is therefore essential to establish a methodology for analysis, based on clear and measurable criteria. Currently, there are different methodologies available in the market to assist with such analysis. These tools can have a quantitative or qualitative focus. The aim of this study is to evaluate three of the best current main performance assessment technologies： Minimum Variance Control-Harris Index; Statistical Process Control （Cp and Cpk）; and the Qin and Yu Index. These indexes were studied for an alumina plant controlled by three MPC （model predictive control） algorithms （GPC （generalized predictive control）, RMPCT （robust multivariable predictive control technology） and ESSMPC （extended state space model predictive controller）） with different results.展开更多
A novel dual control method is proposed for the stochastic systems with unknown parameters, which converts the unsolvable dynamic programming problem into a tractable twostep ahead minimum variance control problem in ...A novel dual control method is proposed for the stochastic systems with unknown parameters, which converts the unsolvable dynamic programming problem into a tractable twostep ahead minimum variance control problem in a stochastic suboptimal view. Innovation variance is used to improve the learning effect, and the instant weight is introduced to reduce the influence of the future output estimation error on the system. Simulation results show the satisfactory performance of the new controller.展开更多
A scheme of adaptive control based on a recurrent neural network with a neural network compensation is presented for a class of nonlinear systems with a nonlinear prefix. The recurrent neural network is used to identi...A scheme of adaptive control based on a recurrent neural network with a neural network compensation is presented for a class of nonlinear systems with a nonlinear prefix. The recurrent neural network is used to identify the unknown nonlinear part and compensate the difference between the real output and the identified model output. The identified model of the controlled object consists of a linear model and the neural network. The generalized minimum variance control method is used to identify parameters, which can deal with the problem of adaptive control of systems with unknown nonlinear part, which can not be controlled by traditional methods. Simulation results show that this algorithm has higher precision, faster convergent speed.展开更多
The control problem for single-input single-output（SISO） systems in the presence of mixed uncertainties, both stochastic and deterministic uncertainties, is considered. The stochastic uncertainties are modeled as ex...The control problem for single-input single-output（SISO） systems in the presence of mixed uncertainties, both stochastic and deterministic uncertainties, is considered. The stochastic uncertainties are modeled as exogenous noises, while the deterministic uncertainties are time invariant and appear as the unknown parameters which lie in a bounded interval. Based on a subdivision for the continuous interval, a robust adaptive controller is designed. The controller can not only realize the system output to track the desired output, but also learn a more accurate interval which contains the true value of the unknown parameter with a learning error given in advance. An example is given finally to demonstrate the effectiveness of the proposed method.展开更多
In this paper, the application of modified genetic algorithms (MGA) in the optimization of the ARX Model-based observer of the Pneumatic Artificial Muscle (PAM) manipulator is investigated. The new MGA algorithm is pr...In this paper, the application of modified genetic algorithms (MGA) in the optimization of the ARX Model-based observer of the Pneumatic Artificial Muscle (PAM) manipulator is investigated. The new MGA algorithm is proposed from the genetic algorithm with important additional strategies, and consequently yields a faster convergence and a more accurate search. Firstly, MGA-based identification method is used to identify the parameters of the nonlinear PAM manipulator described by an ARX model in the presence of white noise and this result will be validated by MGA and compared with the simple genetic algorithm (GA) and LMS (Least mean-squares) method. Secondly, the intrinsic features of the hysteresis as well as other nonlinear disturbances existing intuitively in the PAM system are estimated online by a Modified Recursive Least Square (MRLS) method in identification experiment. Finally, a highly efficient self-tuning control algorithm Minimum Variance Control (MVC) is taken for tracking the joint angle position trajectory of this PAM manipulator. Experiment results are included to demonstrate the excellent performance of the MGA algorithm in the NARX model-based MVC control system of the PAM system. These results can be applied to model, identify and control other highly nonlinear systems as well.展开更多
This paper discusses a design method for the control system of a weigh feeder that supplies powder and granular material at a constant rate. Most weigh feeders employed in industry are controlled by proportional and i...This paper discusses a design method for the control system of a weigh feeder that supplies powder and granular material at a constant rate. Most weigh feeders employed in industry are controlled by proportional and integral （PI） compensation, and the control performance is decided by the selection of parameters. To attain advanced control performance by PI control, the PI parameters are designed on the basis of generalized minimum variance control （GMVC）. In this study, to achieve user-specified control performance by GMVC-based PI control, the design parameters of GMVC are automatically adjusted using a performance-adaptive method. The control performance discussed in this study consists of the variance of the control error and that of the difference in the control input. In a conventional performance-adaptive method, the variance of the control error is reduced. In this study, to reduce energy consumption and to achieve user-specified control performance, the variance of the difference in the control input is specified and the design parameter is determined. To demonstrate its effectiveness, the proposed method is applied to an actual weigh feeder.展开更多
对同步发电机励磁系统性能评价一般通过阶跃响应方法,但该方法无法在线进行,为此提出了最小方差控制基准的性能评估方法。对系统设计最小方差控制器并作为系统控制性能上限,与系统实际性能进行比较而得到性能指标,并对该方法进行系统滤...对同步发电机励磁系统性能评价一般通过阶跃响应方法,但该方法无法在线进行,为此提出了最小方差控制基准的性能评估方法。对系统设计最小方差控制器并作为系统控制性能上限,与系统实际性能进行比较而得到性能指标,并对该方法进行系统滤波和相关性分析FCOR(Filtering and Correlation Analysis)算法的改进,避免了Diophantine方程的展开运算。分析表明该评估方法只需利用同步发电机输出端电压数据,结合系统时滞d就可以得到励磁系统的性能指标。仿真结果表明该方法简化了计算过程,能够及时准确地在线评估励磁系统的控制性能。展开更多
基金Supported by the National High Technology Research and Development Program of China(2008AA042902)the National Basic Research Program of China(2007CB714006)the Graduate Creative Research Program of Zhejiang Province (YK2008024)
文摘This paper is concerned with the control performance assessment based on the multivariable generalized minimum variance benchmark.An explicit expression for the feedback controller-invariant(the generalized minimum variance)term of the multivariable control system is obtained,which is used as a standard benchmark for the assessment of the control performance for multi input multi output(MIMO)process.The proposed approach is based on the multivariable minimum variance benchmark.In comparison with the minimum variance benchmark, the developed method is more reasonable and practical for the control performance assessment of multivariable systems.The approach is illustrated by a simulation example and an industrial application.
文摘In order to improve the robustness and noise resistance of generalized minimum valance cothrol systems, several generalizedminimum variance control schemes are synthetically analyzed. The output variance caused by stochastic noise is decomposed to two parts. One part accords with the output variance of minboum vedance control and the other is the additional term of output variance causedby the control weighting factors. At the same time, the sensitivity function of modeling error is also deduced. A new robast design method that can minimize the sensitivity and the additional part of output variance is Presented by regulating variable parameters of contollers. The simulation results of self-tuning control show the effect of this method.
文摘In an extraction turbine, pressure of the extracted steam and rotate speed of the rotor are two important controlled quantities. The traditional linear state feedback control method is not perfect enough to control the two quantities accurately because of existence of nonlinearity and coupling. A generalized minimum variance control method is studied for an extraction turbine. Firstly, a nonlinear mathematical model of the control system about the two quantities is transformed into a linear system with two white noises. Secondly, a generalized minimum variance control law is applied to the system. A comparative simulation is done. The simulation results indicate that precision and dynamic quality of the regulating system under the new control law are both better than those under the state feedback control law.
文摘To reduce the vibration in the suspension, semi active suspension system was employed. And its CARMA model was built. Two adaptive control schemes, the minimum variance self tuning control algorithm and the pole configuration self tuning control algorithm, were proposed. The former can make the variance of the output minimum while the latter can make dynamic behavior satisfying. The stability of the two schemes was analyzed. Simulations of them show that the acceleration in the vertical direction has been reduced greatly. The purpose of reducing vibration is realized. The two schemes can reduce the vibration in the suspension and have some practicability.
基金This paper is supported by the National Foundamental Research Program of China (No. 2002CB312201), the State Key Program of NationalNatural Science of China (No. 60534010), the Funds for Creative Research Groups of China (No. 60521003), and Program for Changjiang Scholarsand Innovative Research Team in University (No. IRT0421).
文摘For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential （PID） controller is proposed, which consists of a traditional PID controller, a decoupling compensator and a feedforward compensator for the unmodeled dynamics. The parameters of such controller is selected based on the generalized minimum variance control law. The unmodeled dynamics is estimated and compensated by neural networks, a switching mechanism is introduced to improve tracking performance, then a nonlinear decoupling PID control algorithm is proposed. All signals in such switching system are globally bounded and the tracking error is convergent. Simulations show effectiveness of the algorithm.
文摘Performance assessment of a proportional-integral-derivative （PID） controller is condueted using the PIDachievable minimum variance as a benchmark. When the process model is unknown, we can estimate the PID-achievable minimum variance and the corresponding parameters by routine closed-loop operation data. Simulation results show that the process output variance is reduced by retuning controller parameters.
文摘The current highly competitive environment has driven industries to operate with increasingly restricted profit margins. Thus, it is imperative to optimize production processes. Faced with this scenario, multivariable predictive control of processes has been presented as a powerful alternative to achieve these goals. Moreover, the rationale for implementation of advanced control and subsequent analysis of its post-match performance also focus on the benefits that this tool brings to the plant. It is therefore essential to establish a methodology for analysis, based on clear and measurable criteria. Currently, there are different methodologies available in the market to assist with such analysis. These tools can have a quantitative or qualitative focus. The aim of this study is to evaluate three of the best current main performance assessment technologies： Minimum Variance Control-Harris Index; Statistical Process Control （Cp and Cpk）; and the Qin and Yu Index. These indexes were studied for an alumina plant controlled by three MPC （model predictive control） algorithms （GPC （generalized predictive control）, RMPCT （robust multivariable predictive control technology） and ESSMPC （extended state space model predictive controller）） with different results.
文摘A novel dual control method is proposed for the stochastic systems with unknown parameters, which converts the unsolvable dynamic programming problem into a tractable twostep ahead minimum variance control problem in a stochastic suboptimal view. Innovation variance is used to improve the learning effect, and the instant weight is introduced to reduce the influence of the future output estimation error on the system. Simulation results show the satisfactory performance of the new controller.
文摘A scheme of adaptive control based on a recurrent neural network with a neural network compensation is presented for a class of nonlinear systems with a nonlinear prefix. The recurrent neural network is used to identify the unknown nonlinear part and compensate the difference between the real output and the identified model output. The identified model of the controlled object consists of a linear model and the neural network. The generalized minimum variance control method is used to identify parameters, which can deal with the problem of adaptive control of systems with unknown nonlinear part, which can not be controlled by traditional methods. Simulation results show that this algorithm has higher precision, faster convergent speed.
基金supported by the National Natural Science Foundation of China(61273127U1534208)+2 种基金the Key Program of National Natural Science Foundation of China(61533014)the Key Laboratory for Fault Diagnosis and Maintenance of Spacecraft in Orbit(SDML-OF2015004)the Science and Technology Preject of Shaanxi Province(2016GY-108)
文摘The control problem for single-input single-output（SISO） systems in the presence of mixed uncertainties, both stochastic and deterministic uncertainties, is considered. The stochastic uncertainties are modeled as exogenous noises, while the deterministic uncertainties are time invariant and appear as the unknown parameters which lie in a bounded interval. Based on a subdivision for the continuous interval, a robust adaptive controller is designed. The controller can not only realize the system output to track the desired output, but also learn a more accurate interval which contains the true value of the unknown parameter with a learning error given in advance. An example is given finally to demonstrate the effectiveness of the proposed method.
文摘In this paper, the application of modified genetic algorithms (MGA) in the optimization of the ARX Model-based observer of the Pneumatic Artificial Muscle (PAM) manipulator is investigated. The new MGA algorithm is proposed from the genetic algorithm with important additional strategies, and consequently yields a faster convergence and a more accurate search. Firstly, MGA-based identification method is used to identify the parameters of the nonlinear PAM manipulator described by an ARX model in the presence of white noise and this result will be validated by MGA and compared with the simple genetic algorithm (GA) and LMS (Least mean-squares) method. Secondly, the intrinsic features of the hysteresis as well as other nonlinear disturbances existing intuitively in the PAM system are estimated online by a Modified Recursive Least Square (MRLS) method in identification experiment. Finally, a highly efficient self-tuning control algorithm Minimum Variance Control (MVC) is taken for tracking the joint angle position trajectory of this PAM manipulator. Experiment results are included to demonstrate the excellent performance of the MGA algorithm in the NARX model-based MVC control system of the PAM system. These results can be applied to model, identify and control other highly nonlinear systems as well.
文摘This paper discusses a design method for the control system of a weigh feeder that supplies powder and granular material at a constant rate. Most weigh feeders employed in industry are controlled by proportional and integral （PI） compensation, and the control performance is decided by the selection of parameters. To attain advanced control performance by PI control, the PI parameters are designed on the basis of generalized minimum variance control （GMVC）. In this study, to achieve user-specified control performance by GMVC-based PI control, the design parameters of GMVC are automatically adjusted using a performance-adaptive method. The control performance discussed in this study consists of the variance of the control error and that of the difference in the control input. In a conventional performance-adaptive method, the variance of the control error is reduced. In this study, to reduce energy consumption and to achieve user-specified control performance, the variance of the difference in the control input is specified and the design parameter is determined. To demonstrate its effectiveness, the proposed method is applied to an actual weigh feeder.
文摘对同步发电机励磁系统性能评价一般通过阶跃响应方法,但该方法无法在线进行,为此提出了最小方差控制基准的性能评估方法。对系统设计最小方差控制器并作为系统控制性能上限,与系统实际性能进行比较而得到性能指标,并对该方法进行系统滤波和相关性分析FCOR(Filtering and Correlation Analysis)算法的改进,避免了Diophantine方程的展开运算。分析表明该评估方法只需利用同步发电机输出端电压数据,结合系统时滞d就可以得到励磁系统的性能指标。仿真结果表明该方法简化了计算过程,能够及时准确地在线评估励磁系统的控制性能。
