Modelling and Control of Shape Memory Alloy Actuators by using Preisach Model, Genetic Algorithm and Fuzzy Logic

Shapememoryalloy (SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications in aeronautics, surgical tools, robotics and so on. Nonlinearity hysteresis effects existing in SMA actuators present a problem in the motion control of these smart actuators. This paper investigates the control problem of SMA actuators in both simulation and experiment. In the simulation, the numerical Preisachmodel with geometrical interpretation is used for hysteresis modeling of SMA actuators. This model is then incorporated in a closed loop PID control strategy. The optimal values of PID parameters are determined by using geneticalgorithm to minimize the mean squared error between desired output displacement and simulated output. However, the control performance is not good compared with the simulation results when these parameters are applied to the real SMA control since the system is disturbed by unknown factors and changes in the surrounding environment of the system. A further automated readjustment of the PID parameters using fuzzylogic is proposed for compensating the limitation. To demonstrate the effectiveness of the proposed controller, real time control experiment results are presented.

Improved structure optimization for fuzzy-neural networks

Fuzzy-neural-network-based inference systems are well-known universal approximators which can produce linguistically interpretable results. Unfortunately, their dimensionality can be extremely high due to an excessive number of inputs and rules, which raises the need for overall structure optimization. In the literature, various input selection methods are available, but they are applied separately from rule selection, often without considering the fuzzy structure. This paper proposes an integrated framework to optimize the number of inputs and the number of rules simultaneously. First, a method is developed to select the most significant rules, along with a refinement stage to remove unnecessary correlations. An improved information criterion is then proposed to find an appropriate number of inputs and rules to include in the model, leading to a balanced tradeoff between interpretability and accuracy. Simulation results confirm the efficacy of the proposed method.

System oriented neural networks - Problem formulation, methodology and application

A novel methodology is proposed for the development of neural network models for complex engineering systems exhibiting nonlinearity. This method performs neural network modeling by first establishing some fundamental nonlinear functions from a priori engineering knowledge, which are then constructed and coded into appropriate chromosome representations. Given a suitable fitness function, using evolutionary approaches such as genetic algorithms, a population of chromosomes evolves for a certain number of generations to finally produce a neural network model best fitting the system data. The objective is to improve the transparency of the neural networks, i.e. to produce physically meaningful