This paper describes the MATLAB/SIMULINK realization of the PUMA 560 robot manipulator position control methodology. This paper focuses on two main areas, namely robot manipulator analysis and implementation, and design, analyzed and implement nonlinear sliding mode control (SMC) methods. These simulation models are developed as a part of a software laboratory to support and enhance graduate/undergraduate robotics courses, nonlinear control courses and MATLAB/SIMULINK courses at research and development company (SSP Co.) research center, Shiraz, Iran.

PUMA-560 Robot Manipulator Position Sliding Mode Control Methods Using MATLAB/SIMULINK and Their Integration into Graduate/Undergraduate Nonlinear Control, Robotics and MATLAB Courses

PUMA-560 Robot Manipulator Position Computed Torque Control Methods Using MATLAB/SIMULINK and Their Integration into Graduate Nonlinear Control and MATLAB Courses

This paper expands a sliding mode fuzzy controller which sliding surface gain is on-line tuned by minimum fuzzy inference algorithm. The main goal is to guarantee acceptable trajectories tracking between the second order nonlinear system (robot manipulator) actual and the desired trajectory. The fuzzy controller in proposed sliding mode fuzzy controller is based on Mamdani ’s fuzzy inference system (FIS) and it has one input and one output. The input represents the function between sliding function, error and the rate of error. The outputs represent torque, respectively. The fuzzy inference system methodology is on-line tune the sliding surface gain based on error-based fuzzy tuning methodology. Pure sliding mode fuzzy controller has difficulty in handling unstructured model uncertainties. To solve this problem applied fuzzy-based tuning method to sliding mode fuzzy controller for adjusting the sliding surface gain ( λ ). Since the sliding surface gain ( λ) is adjusted by fuzzy-based tuning method, it is nonlinear and continuous. Fuzzybased tuning sliding mode fuzzy controller is stable model-free controller which eliminates the chattering phenomenon without to use the boundary layer saturation function. Lyapunov stability is proved in fuzzy-based tuning sliding mode fuzzy controller based on switching (sign) function.

Online Tuning Chattering Free Sliding Mode Fuzzy Control Design: Lyapunov Approach

The application of design nonlinear controller such as computed torque controller in control of 6 degrees of freedom (DOF) robot arm will be investigated in this research. One of the significant challenges in control algorithms is a linear behavior controller design for nonlinear systems (e.g., robot manipulator). Some of robot manipulators which work in industrial processes are controlled by linear PID controllers, but the design of linear controller for robot manipulators is extremely difficult because they are hardly nonlinear and uncertain. To reduce the above challenges, the nonlinear robust controller is used to control of robot manipulator. Computed torque controller is a powerful nonlinear controller under condition of partly uncertain dynamic parameters of system. This controller is used to control of highly nonlinear systems especially for robot manipulators. To adjust this controller’s coefficient baseline methodology is used and applied to CTC.

Design Baseline Computed Torque Controller

Adaptive neural network force tracking impedance control for uncertain robotic manipulator based on nonlinear velocity observer

One of the most active research areas in field of robotics is control of robot manipulator because this system has highly nonlinear dynamic parameters and most of dynamic parameters are unknown so design an acceptable controller is the main goal in this work. To solve this challenge position new estimation sliding mode fuzzy controller is introduced and applied to robot manipulator. This controller can solve to most important challenge in classical sliding mode controller in presence of highly uncertainty, namely; chattering phenomenon based on fuzzy estimator and online tuning and equivalent nonlinear dynamic based on estimation. Proposed method has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.8 s, steady state error = 1e-9 and RMS error=0.0001632).

A New Estimate Sliding Mode Fuzzy Controller for Robotic Manipulator

Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This research focuses on the design, implementation and analysis of a model-free linear error-based tuning computed torque controller for highly nonlinear dynamic second order system, in presence of uncertainties. In order to provide high performance nonlinear methodology, computed torque controller is selected. Pure computed torque controller can be used to control of partly known nonlinear dynamic parameters of nonlinear systems. Conversely, pure computed torque controller is used in many applications; it has an important drawback namely; nonlinear equivalent dynamic formulation in uncertain dynamic parameter. In order to solve the uncertain nonlinear dynamic parameters, implement easily and avoid mathematical model base controller, model-free performance/error-based linear methodology with three inputs and one output is applied to pure computed torque controller. The results demonstrate that the error-based linear tuning computed torque controller is a model-based controllers which works well in certain and uncertain system. Pure computed torque controller has difficulty in handling unstructured model uncertainties. To solve this problem applied linear modelfree error -based tuning method to computed torque controller for adjusting the linear inner loop gain (� ). Since the linear inner loop gain (�) is adjusted by linear error-based tuning method, it is

Design Error-based Linear Model-free Evaluation Performance Computed Torque Controller

In this research, a model free sliding mode fuzzy adaptive inverse dynamic fuzzy controller (SMFIDFC) is designed for a robot manipulator to rich the best performance. Inverse dynamic controller is considered because of its high performance in certain system. Fuzzy methodology has been included in inverse dynamic to keep away from design nonlinear controller based on dynamic model. Sliding mode fuzzy adaptive methodology is applied to model free controller to have better result in presence of structure and unstructured uncertainties. Besides, this control method can be applied to non-linear systems easily. Today, strong mathematical tools are used in new control methodologies to design adaptive nonlinear controller with satisfactory output results (e.g., minimum error, good trajectory, disturbance rejection).

Design Adaptive Artificial Inverse Dynamic Controller: Design Sliding Mode Fuzzy Adaptive New Inverse Dynamic Fuzzy Controller

Both fuzzy logic and sliding mode can compensate the steady-state error of proportional- derivative (PD) control. This paper presents parallel sliding mode compensations for fuzzy PD controllers. The asymptotic stability of fuzzy PD control with first- order sliding mode compensation in the parallel structure is proven. For the parallel structure, the finite time convergence with a super-twisting second-order sliding-mode is guaranteed.

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Farid Aghayari

Papers

A New Estimate Sliding Mode Fuzzy Controller for Robotic Manipulator

Design Error-based Linear Model-free Evaluation Performance Computed Torque Controller

Design Adaptive Artificial Inverse Dynamic Controller: Design Sliding Mode Fuzzy Adaptive New Inverse Dynamic Fuzzy Controller

Stable Fuzzy PD Control with Parallel Sliding Mode Compensation with Application to Rigid Manipulator