The controller of an image‐based visual servoing (IBVS) system is based on the design of the kinematic velocity controller which guarantees exponentially decreasing feature errors. In fact, this controller is using the sliding surface approach of classical Sliding Mode Control (SMC). In SMC, the system dynamics are taken into consideration and the sliding surface is designed according to the physical limitations and desired convergence time. Different design methods are proposed in the literature using adaptive gain, time variations, nonlinear functions, and intelligent methods like fuzzy logic (FL) and genetic algorithms (GA). In this study, five different sliding surface designs with analytical and intelligent methods are modified and applied to an IBVS system to expand these designs to visually guided robot manipulators. The design methods are selected by their convenience and applicability to these types of manipulator systems. To show the performance of the design methods, an IBVS system with six‐DOF manipulator is simulated using MATLAB Simulink, Robotics Toolbox, Machine Vision Toolbox, and Fuzzy Logic Toolbox. A comparison of these design methods according to convergence time, error cost function, defined parameters, and motion characteristics is given.
Part of the book: Applications from Engineering with MATLAB Concepts