Force control in a pneumatic system using hybrid adaptive neuro-fuzzy model reference control

Robust loop shaping–fuzzy gain scheduling control of a servo-pneumatic system using particle swarm optimization approach

“…Conventional fixedstructure robust loop shaping control [6,14] was also designed to verify the effectiveness of the proposed controller. This controller Step Response Proposed controller ISE controller is better than the conventional H infinity loop shaping controller in terms of lower order and simple structure [3,14], and is better than the reduced order controller by Hankel Norm Model Reduction in terms of its higher stability margin [14]. The identified model at the middle of a pneumatic cylinder (x = 0.6) is adopted as the plant of this conventional fixed structure controller.…”

“…However, the design of a high performance controller for this actuator is not easy due to its high nonlinearity and parameter variations in the system. In addition, for a long stroke pneumatic cylinder, higher nonlinear dynamic due to wide operation range is occurred, making it difficult to design an effective controller to control the plant [1][2][3][4]. To overcome this problem, many methods such as sliding mode control [5], fixed-structure robust loop shaping (RLS) control [6], and fuzzy gain scheduling control [7] have been proposed.…”

How to optimize the TS‐fuzzy knowledge base to achieve desired performances: Accuracy and robustness

“…The learning procedure by the steepest descent technique is described by flowchart [24][25][26] given in Figure 5. The parameters ( w…”

“…Only two rules remain at the end of HAFSL execution, i.e. the weightings of rules c (19) F and c (25) F are equal to the unit, as indicated in Table VI with…”

“…with { w (19) F , c (19) F } = {363.7765, 1} O (25) ut = (0.1559 · x 1 + 0.3166 · x 2 ) with { w (25) F , c (25) F } = {198.5222, 1}…”