tailieunhanh - Model Predictive Control Part 10

Tham khảo tài liệu 'model predictive control part 10', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Multi-objective Nonlinear Model Predictive Control Lexicographic Method 173 the output will reach the set point along it. Suppose the future reference trajectories of output yi k are wi k i 1 2 in most MPC NMPC these trajectories often can be set as exponential curves as 7 and Fig. 3. Zheng et al. 2008 wi k j ai wi k j -1 1 y. .1 j p i 1 2 7 where wi k yi k and 0 ai 1 . Then the objective function of a setpoint goal would be J k ị ỹi k j k - w k j 2 i 1 2 8 j 1 Fig. 3. Description of exponential reference trajectory The stair-like control strategy To enhance the control quality and lighten the computational load of dynamic optimization of NMPC especially the computational load of GA in this chapter stair-like control strategy Wu et al. 2000 could be used here. Suppose the first unknown increment of instant control input is Au k u k - u k -1 and the stair constant p is a positive real number in stairlike control strategy the future control inputs could be decided as follow Wu et al. 2000 Zheng et al. 2008 Au k j p Au k j -1 pj Au k 1 j p-1 9 18 ---- datal data2 data3 16 14 12 10 p 8 Ọ 6 4 2 0 . - 0 1 2 3 4 5 Time Fig. 4. Description of stair-like control strategy 174 Model Predictive Control With this disposal the elements in the future sequence of control input u k u k 1 u k p-1 are not independent as before and the only unknown variable here in NMPC is the increment of instant control input Au k which can determine all the later control input. The dimension of unknown variable in NMPC now decreases from i p to i remarkably where i is only the dimension of control input thus the computational load is no longer depend on the length of the predictive horizon like many other MPC NMPC . So it is very convenient to use long predictive horizon to obtain better control quality without additional computational load under this strategy. Because MPC NMPC will repeat the dynamic optimization at every sample time and only u k Au k u k -1 will be carried out .

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