Artificial neural network for solving the inverse kinematic model of a spatial and planar variable curvature continuum robot

In this paper, neural networks are presented to solve the inverse kinematic models of continuum robots. Firstly, the forward kinematic models are calculated for variable curvature continuum robots. Then, the forward kinematic models are implemented in the neural networks which present the position of the continuum robot’s end effector. After that, the inverse kinematic models are solved through neural networks without setting up any constraints. In the same context, to validate the utility of the developed neural networks, various types of trajectories are proposed to be followed by continuum robots. It is found that the developed neural networks are powerful tool to deal with the high complexity of the non-linear equations, in particular when it comes to solving the inverse kinematics model of variable curvature continuum robots. To have a closer look at the efficiency of the developed neural network models during the follow up of the proposed trajectories, 3D simulation examples through Matlab have been carried out with different configurations. It is noteworthy to say that the developed models are a needed tool for real time application since it does not depend on the complexity of the continuum robots' inverse kinematic models

Fuzzy logic in nonlinear modeling and control

Fuzzy logic is a logic of approximate reasoning, established by Lotfi Zadeh since 1965 in his work [115] which is considered as the starting point of this new field of mathematics. Since the beginning of 70’s, this approach has been used in engineering [62, 63, 116], both for the approximation of nonlinear behavior and for the control of dynamical systems. The first studies have confirmed that fuzzy logic is appealing because it is similar to human language. It is also an efficient tool to map an input space to an output space using rules expressed in terms of variables that are linguistic expressions. The use of these linguistic variables was not possible using classical mathematical modeling. In addition it has been found that fuzzy logic theory gives a convenient way to describe imprecise information or partially unknown behaviors. These researches led to the establishment of a new branch of control engineering referred to as fuzzy modeling and fuzzy control.Doctorat en sciences appliquées (FSA 3)--UCL, 200

Fuzzy logic in nonlinear modeling and control

Fuzzy logic is a logic of approximate reasoning, established by Lotfi Zadeh since 1965 in his work [115] which is considered as the starting point of this new field of mathematics. Since the beginning of 70’s, this approach has been used in engineering [62, 63, 116], both for the approximation of nonlinear behavior and for the control of dynamical systems. The first studies have confirmed that fuzzy logic is appealing because it is similar to human language. It is also an efficient tool to map an input space to an output space using rules expressed in terms of variables that are linguistic expressions. The use of these linguistic variables was not possible using classical mathematical modeling. In addition it has been found that fuzzy logic theory gives a convenient way to describe imprecise information or partially unknown behaviors. These researches led to the establishment of a new branch of control engineering referred to as fuzzy modeling and fuzzy control.Doctorat en sciences appliquées (FSA 3)--UCL, 200

Constrained Fuzzy Predictive Control Using Particle Swarm Optimization

A fuzzy predictive controller using particle swarm optimization (PSO) approach is proposed. The aim is to develop an efficient algorithm that is able to handle the relatively complex optimization problem with minimal computational time. This can be achieved using reduced population size and small number of iterations. In this algorithm, instead of using the uniform distribution as in the conventional PSO algorithm, the initial particles positions are distributed according to the normal distribution law, within the area around the best position. The radius limiting this area is adaptively changed according to the tracking error values. Moreover, the choice of the initial best position is based on prior knowledge about the search space landscape and the fact that in most practical applications the dynamic optimization problem changes are gradual. The efficiency of the proposed control algorithm is evaluated by considering the control of the model of a 4 × 4 Multi-Input Multi-Output industrial boiler. This model is characterized by being nonlinear with high interactions between its inputs and outputs, having a nonminimum phase behaviour, and containing instabilities and time delays. The obtained results are compared to those of the control algorithms based on the conventional PSO and the linear approach