Design of a reduced-rule self-organizing fuzzy logic controller for water hydraulic applications

Abstract

The use of self-organizing fuzzy logic controllers (SOFLCs) in high-speed multi-variable systems has been largely limited by the high number of rules generated, and by the application specific nature of the learning process. This paper concerns the development of a more generically applicable form of an SOFLC that uses a limited rule base of predetermined size, resulting in improved generalization properties and a reduction in the processing time. A simulation study on a four-valve water hydraulic actuator for a subsea robotic arm shows how this method can be applied to system modelling, the resulting 48-rule fuzzy model then providing the necessary process model for training the SOFLC. Using this fuzzy model the SOFLC was able to tune a set of 12 rules to control the actuator and then to adapt these rules to compensate for a simulated leak of hydraulic fluid.

Mathematical modelling and fuzzy logic based position control of an electrohydraulic servosystem with internal leakage

“…But, electrohydraulic servosystems exhibit highly nonlinear behaviour to the effect that classical linear controllers, e.g., PD, usually achieve a limited performance. The other techniques such as model based control [7], adaptive control [8], fuzzy logic [9,10] and neural network based control [11], robust control [12], and adaptive wavelet backstepping control [13] techniques are also used for controlling the hydraulic systems. Zeb [14] employed an analytical method in mathematical modelling of an electrohydraulic position control servosystem and discussed various factors affecting the performance of a typical position servo and developed expressions for estimating the more important performance factors.…”

“…Deticek [17] designed a fuzzy PD based selflearning fuzzy controller and used this controller in position control of an electrohydraulic system. Jones et al [9] designed an adaptive self-learning fuzzy logic controller in order to improve tracking performance of an electrohydraulic system. Karpenko and Sepehri [12] developed a fault-tolerant control (FTC) strategy to compensate for the degrading effects of fluid leakage across a faulty actuator piston seal in an electrohydraulic positioning system.…”

Self-organizing fuzzy optimal control for under-actuated systems

“…Since then, the learning algorithms and methods for SOFC have been further modified and improved by many following researchers. [22][23][24][25][26][27][28][29] Significantly, a new type of learning algorithm was developed and widely applied in recent investigations, which used the output error and error change to adjust the SOFC's linguistic fuzzy rule table directly, so that it can be generated without any initial fuzzy rules and significantly eliminate the difficulty of designing the fuzzy rule base. [30][31][32][33][34][35][36][37] The general form of this proposed self-organizing fuzzy algorithm can be formulated as…”

Development of a model and simulation of a water hydraulic manipulator joint

“…In order to carry out a thorough analysis of the water hydraulic system and its interaction with the control algorithms [6 ], a system model can be used.…”