The state space representations of fractional order linear time- invariant（LTI） systems are introduced, and their solution formulas are deduced hy means of Laplace transform. The stability condition of fractional or...The state space representations of fractional order linear time- invariant（LTI） systems are introduced, and their solution formulas are deduced hy means of Laplace transform. The stability condition of fractional order LTI systems is given, and its proof is deduced by means of using linear non - singularity transform and the derivative property of Mittag-Leffler function. The controllability condition of fractional m＇der LTI systems is given, and its proof is deduced by means of using its characteristic polynomial and the Cayley-Hamilton theorem. The observability condition of fractional order LTI systems is given, and its proof is deduced by means of their solution formulas. Finally an example is given to prove the correctness of the stability, controllability, and observability conditions mentioned above, s are deduced by means of Laplace transform. Their stability, controllability and observability conditions are given as well as their proofs.展开更多
In this paper, a process modeling and related optimizing control for nonuniformly sampled （NUS） systems are addressed. By using a proposed nonuniform integration filter and subspace method estimation, an identificat...In this paper, a process modeling and related optimizing control for nonuniformly sampled （NUS） systems are addressed. By using a proposed nonuniform integration filter and subspace method estimation, an identification method of NUS systems is developed, based on which either an output soft sensor or a hidden state estimator is developed. The optimizing control is implemented by replacing the sparsely-mea- sured/immeasurable variable with the estimated one. Examples of optimizing control problem are given. The proposed optimizing control strategy in the simulation examples is verified to be very effeetive.展开更多
Design for six sigma （DFSS） is a powerful approach of designing products, processes, and services with the objective of meeting the needs of customers in a cost-effective maimer. DFSS activities are classified into ...Design for six sigma （DFSS） is a powerful approach of designing products, processes, and services with the objective of meeting the needs of customers in a cost-effective maimer. DFSS activities are classified into four major phases viz. identify, design, optimize, and validate （IDOV）. And an adaptive design for six sigma （ADFSS） incorporating the traits of artifidai intelligence and statistical techniques is presented. In the identify phase of the ADFSS, fuzzy relation measures between customer attributes （CAs） and engineering characteristics （ECs） as well as fuzzy correlation measures among ECs are determined with the aid of two fuzzy logic controllers （FLCs）. These two measures are then used to establish the cumulative impact factor for ECs. In the next phase （ i. e. design phase）, a transfer function is developed with the aid of robust multiple nonlinear regression analysis. Furthermore, 1this transfer function is optimized with the simulated annealing （ SA ） algorithm in the optimize phase. In the validate phase, t-test is conducted for the validation of the design resulted in earlier phase. Finally, a case study of a hypothetical writing instrument is simulated to test the efficacy of the proposed ADFSS.展开更多
基金stability, coSponsored by the National High Technology Research and Development Program of China (Grant No.2003AA517020), the National Natural Science Foundation of China (Grant No.50206012), and Developing Fund of Shanghai Science Committee (Grant No.011607033).
文摘The state space representations of fractional order linear time- invariant（LTI） systems are introduced, and their solution formulas are deduced hy means of Laplace transform. The stability condition of fractional order LTI systems is given, and its proof is deduced by means of using linear non - singularity transform and the derivative property of Mittag-Leffler function. The controllability condition of fractional m＇der LTI systems is given, and its proof is deduced by means of using its characteristic polynomial and the Cayley-Hamilton theorem. The observability condition of fractional order LTI systems is given, and its proof is deduced by means of their solution formulas. Finally an example is given to prove the correctness of the stability, controllability, and observability conditions mentioned above, s are deduced by means of Laplace transform. Their stability, controllability and observability conditions are given as well as their proofs.
基金Supported by the China Postdoctoral Science Foundation Funded Project (No. 20080440386)
文摘In this paper, a process modeling and related optimizing control for nonuniformly sampled （NUS） systems are addressed. By using a proposed nonuniform integration filter and subspace method estimation, an identification method of NUS systems is developed, based on which either an output soft sensor or a hidden state estimator is developed. The optimizing control is implemented by replacing the sparsely-mea- sured/immeasurable variable with the estimated one. Examples of optimizing control problem are given. The proposed optimizing control strategy in the simulation examples is verified to be very effeetive.
基金Shanghai Leading Academic Discipline Project,China(No.B602)
文摘Design for six sigma （DFSS） is a powerful approach of designing products, processes, and services with the objective of meeting the needs of customers in a cost-effective maimer. DFSS activities are classified into four major phases viz. identify, design, optimize, and validate （IDOV）. And an adaptive design for six sigma （ADFSS） incorporating the traits of artifidai intelligence and statistical techniques is presented. In the identify phase of the ADFSS, fuzzy relation measures between customer attributes （CAs） and engineering characteristics （ECs） as well as fuzzy correlation measures among ECs are determined with the aid of two fuzzy logic controllers （FLCs）. These two measures are then used to establish the cumulative impact factor for ECs. In the next phase （ i. e. design phase）, a transfer function is developed with the aid of robust multiple nonlinear regression analysis. Furthermore, 1this transfer function is optimized with the simulated annealing （ SA ） algorithm in the optimize phase. In the validate phase, t-test is conducted for the validation of the design resulted in earlier phase. Finally, a case study of a hypothetical writing instrument is simulated to test the efficacy of the proposed ADFSS.
