This paper presents a genetic algorithm for the optimal design of model output following control in which there are nonlinear disturbance and uncertian parameters, where the output is regulated to follow the output of...This paper presents a genetic algorithm for the optimal design of model output following control in which there are nonlinear disturbance and uncertian parameters, where the output is regulated to follow the output of reference model. The effectiveness of the proposed algorithm is illustrated by some numerical examples.展开更多
Greenhouse system (GHS) is the worldwide fastest growing phenomenon in agricultural sector. Greenhouse models are essential for improving control efficiencies. The Relative Gain Analysis (RGA) reveals that the GHS con...Greenhouse system (GHS) is the worldwide fastest growing phenomenon in agricultural sector. Greenhouse models are essential for improving control efficiencies. The Relative Gain Analysis (RGA) reveals that the GHS control is complex due to 1) high nonlinear interactions between the biological subsystem and the physical subsystem and 2) strong coupling between the process variables such as temperature and humidity. In this paper, a decoupled linear cooling model has been developed using a feedback-feed forward linearization technique. Further, based on the model developed Internal Model Control (IMC) based Proportional Integrator (PI) controller parameters are optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to achieve minimum Integral Square Error (ISE). The closed loop control is carried out using the above control schemes for set-point change and disturbance rejection. Finally, closed loop servo and servo-regulatory responses of GHS are compared quantitatively as well as qualitatively. The results implicate that IMC based PI controller using PSO provides better performance than the IMC based PI controller using GA. Also, it is observed that the disturbance introduced in one loop will not affect the other loop due to feedback-feed forward linearization and decoupling. Such a control scheme used for GHS would result in better yield in production of crops such as tomato, lettuce and broccoli.展开更多
Actuator and hydraulic system parameters have great influence on the performance,safety and reliability of the stepless capacity control system of reciprocating compressor.Due to the diversity and complex relationship...Actuator and hydraulic system parameters have great influence on the performance,safety and reliability of the stepless capacity control system of reciprocating compressor.Due to the diversity and complex relationship of parameters,traditional parameters selected and calculated based on feasibility can’t make the system run efficiently,have limitations,and may have adverse effects on the system or compressor.Therefore,taking the spring stiffness of the actuator and the impact velocity of ejection,the inlet oil pressure of the hydraulic system,and the indicated power deviation of the compressor as objective functions,the multi-parameter and multi-objective optimization research of the actuator and hydraulic system with the stepless capacity control system based on non-dominated sorting genetic algorithm II(NSGA-II)is carried out.Based on fuzzy analytic hierarchy process(FAHP),the optimal solution is selected from the Pareto front,and compared with the traditional design value,the result is better than that obtained by the traditional design method.展开更多
文摘This paper presents a genetic algorithm for the optimal design of model output following control in which there are nonlinear disturbance and uncertian parameters, where the output is regulated to follow the output of reference model. The effectiveness of the proposed algorithm is illustrated by some numerical examples.
文摘Greenhouse system (GHS) is the worldwide fastest growing phenomenon in agricultural sector. Greenhouse models are essential for improving control efficiencies. The Relative Gain Analysis (RGA) reveals that the GHS control is complex due to 1) high nonlinear interactions between the biological subsystem and the physical subsystem and 2) strong coupling between the process variables such as temperature and humidity. In this paper, a decoupled linear cooling model has been developed using a feedback-feed forward linearization technique. Further, based on the model developed Internal Model Control (IMC) based Proportional Integrator (PI) controller parameters are optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to achieve minimum Integral Square Error (ISE). The closed loop control is carried out using the above control schemes for set-point change and disturbance rejection. Finally, closed loop servo and servo-regulatory responses of GHS are compared quantitatively as well as qualitatively. The results implicate that IMC based PI controller using PSO provides better performance than the IMC based PI controller using GA. Also, it is observed that the disturbance introduced in one loop will not affect the other loop due to feedback-feed forward linearization and decoupling. Such a control scheme used for GHS would result in better yield in production of crops such as tomato, lettuce and broccoli.
基金the State Key Laboratory of Compressor Technology Open Fund Project(No.SKL-YSJ201808)the National Key Research and Development Plan(No.2016YFF0203305)the Special Fund Support for Basic Scientific Research Business Expenses of Central Universities(No.JD1912).
文摘Actuator and hydraulic system parameters have great influence on the performance,safety and reliability of the stepless capacity control system of reciprocating compressor.Due to the diversity and complex relationship of parameters,traditional parameters selected and calculated based on feasibility can’t make the system run efficiently,have limitations,and may have adverse effects on the system or compressor.Therefore,taking the spring stiffness of the actuator and the impact velocity of ejection,the inlet oil pressure of the hydraulic system,and the indicated power deviation of the compressor as objective functions,the multi-parameter and multi-objective optimization research of the actuator and hydraulic system with the stepless capacity control system based on non-dominated sorting genetic algorithm II(NSGA-II)is carried out.Based on fuzzy analytic hierarchy process(FAHP),the optimal solution is selected from the Pareto front,and compared with the traditional design value,the result is better than that obtained by the traditional design method.
