The output feedback model predictive control(MPC),for a linear parameter varying(LPV) process system including unmeasurable model parameters and disturbance(all lying in known polytopes),is considered.Some previously ...The output feedback model predictive control(MPC),for a linear parameter varying(LPV) process system including unmeasurable model parameters and disturbance(all lying in known polytopes),is considered.Some previously developed tools,including the norm-bounding technique for relaxing the disturbance-related constraint handling,the dynamic output feedback law,the notion of quadratic boundedness for specifying the closed-loop stability,and the ellipsoidal state estimation error bound for guaranteeing the recursive feasibility,are merged in the control design.Some previous approaches are shown to be the special cases.An example of continuous stirred tank reactor(CSTR) is given to show the effectiveness of the proposed approaches.展开更多
The minimization of the profit function with respect to the decision variables is very important for the decision makers in the oil field industry.In this paper,a novel approach of the improved tabu search algorithm h...The minimization of the profit function with respect to the decision variables is very important for the decision makers in the oil field industry.In this paper,a novel approach of the improved tabu search algorithm has been employed to solve a large scale problem in the crude oil refinery industry.This problem involves 44 variables,36 constraints,and four decision variables which represent four types of crude oil types.The decision variables have been modeled in the form of fuzzy linear programming problem.The vagueness factor in the decision variables is captured by the nonlinear modified S-curve membership function.A recursive improved tabu search has been used to solve this fuzzy optimization problem.Tremendously improved results are obtained for the optimal profit function and optimal solution for four crude oil.The accuracy of constraints satisfaction and the quality of the solutions are achieved successfully.展开更多
This paper deals with the adaptive practical output maneuvering control problems for a class of nonlinear systems with uncontrollable unstable linearization. The objective is to design a smooth adaptive maneuvering co...This paper deals with the adaptive practical output maneuvering control problems for a class of nonlinear systems with uncontrollable unstable linearization. The objective is to design a smooth adaptive maneuvering controller to solve the geometric and dynamic tasks with an arbitrary small steady tracking error. The method of adding a power integrator and the robust recursive design technique are employed to force the system output to track a desired path and make the tracking speed to follow a desired speed along the path. An example is considered and simulation results are given. The proposed design procedure can be illustrated by the use of this example.展开更多
基金Supported by the National High Technology Research and Development Program of China(2014AA041802)the National Natural Science Foundation of China(61573269)
文摘The output feedback model predictive control(MPC),for a linear parameter varying(LPV) process system including unmeasurable model parameters and disturbance(all lying in known polytopes),is considered.Some previously developed tools,including the norm-bounding technique for relaxing the disturbance-related constraint handling,the dynamic output feedback law,the notion of quadratic boundedness for specifying the closed-loop stability,and the ellipsoidal state estimation error bound for guaranteeing the recursive feasibility,are merged in the control design.Some previous approaches are shown to be the special cases.An example of continuous stirred tank reactor(CSTR) is given to show the effectiveness of the proposed approaches.
文摘The minimization of the profit function with respect to the decision variables is very important for the decision makers in the oil field industry.In this paper,a novel approach of the improved tabu search algorithm has been employed to solve a large scale problem in the crude oil refinery industry.This problem involves 44 variables,36 constraints,and four decision variables which represent four types of crude oil types.The decision variables have been modeled in the form of fuzzy linear programming problem.The vagueness factor in the decision variables is captured by the nonlinear modified S-curve membership function.A recursive improved tabu search has been used to solve this fuzzy optimization problem.Tremendously improved results are obtained for the optimal profit function and optimal solution for four crude oil.The accuracy of constraints satisfaction and the quality of the solutions are achieved successfully.
基金Supported by the National Natural Science Foundation of China （No. 60304003, 60574007, and 60574080）.
文摘This paper deals with the adaptive practical output maneuvering control problems for a class of nonlinear systems with uncontrollable unstable linearization. The objective is to design a smooth adaptive maneuvering controller to solve the geometric and dynamic tasks with an arbitrary small steady tracking error. The method of adding a power integrator and the robust recursive design technique are employed to force the system output to track a desired path and make the tracking speed to follow a desired speed along the path. An example is considered and simulation results are given. The proposed design procedure can be illustrated by the use of this example.
