Pressing demands of productivity and accuracy in today’s robotic applications have highlighted an urge to replace classical control strategies with their modern control counterparts. This recent trend is further justified by the fact that the robotic manipulators have complex nonlinear dynamic structure with uncertain parameters. Highlighting the authors’ research achievements in the domain of manipulator design and control, this paper presents a systematic and comprehensive review of the state-of-the-art control techniques that find enormous potential in controlling manipulators to execute cuttingedge applications. In particular, three kinds of strategies, i.e., intelligent proportional-integral-derivative (PID) scheme, robust control and adaptation based approaches, are reviewed. Future trend in the subject area is commented. Open-source simulators to facilitate controller design are also tabulated. With a comprehensive list of references, it is anticipated that the review will act as a firsthand reference for researchers, engineers and industrialinterns to realize the control laws for multi-degree of freedom (DOF) manipulators.

A systematic review of current and emergent manipulator control approaches

Sliding mode controller (SMC) is a significant 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, because this controller is a robust and stable. Conversely, pure sliding mode controller is used in many applications; it has two important drawbacks namely; chattering phenomenon, and nonlinear equivalent dynamic formulation in uncertain dynamic parameter. The nonlinear equivalent dynamic formulation problem and chattering phenomenon in uncertain system can be solved by using artificial intelligence theorem. However fuzzy logic controller is used to control complicated nonlinear dynamic systems, but it cannot guarantee stability and robustness.  In this research parallel fuzzy logic theory is used to compensate the system dynamic uncertainty.

Design Sliding Mode Controller of with Parallel Fuzzy Inference System Compensator to Control of Robot Manipulator

This paper studies the use of Modified Proportional-Integral-Derivative Sliding Mode Controller (MPIDSMC) control used to control a flexible manipulator. The control gain in the MPIDSMC controller has been determined in an empirical way so far. It is a considerable time-consuming process because the control performance depends not only on the control gain but also on the other parameters such as the payload, references and PID joint servo gains. Hence, the control gain must be tuned considering the other parameters. In order to find the optimal control gain for the MPIDSMC controller, a fuzzy logic approach is proposed in this paper. The proposed fuzzy logic scheme finds an optimum control gain that minimizes the tip vibration for the end effector of the flexible manipulator. Tuned gain response results are compared to results for other types of gains. The effectiveness of using the fuzzy logic appears in the reduction of the computational time and the ability to tune the gain with different loading condition and input parameters.

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Design Sliding Mode Modified Fuzzy Linear Controller with Application to Flexible Robot Manipulator

In this research, an artificial chattering free adaptive fuzzy modified sliding mode control design and application to continuum robotic manipulator has proposed in order to design high performance nonlinear controller in the presence of uncertainties. Regarding to the positive points in sliding mode controller, fuzzy logic controller and online tuning method, the output improves. Each method by adding to the previous controller has covered negative points. The main target in this research is design of model free estimator on-line sliding mode fuzzy algorithm for continuum robot manipulator to reach an acceptable performance. Continuum robot manipulators are highly nonlinear, and a number of parameters are uncertain, therefore design model free controller by both analytical and empirical paradigms are the main goal. Although classical sliding mode methodology has acceptable performance with known dynamic parameters such as stability and robustness but there are two important disadvantages as below: chattering phenomenon and mathematical nonlinear dynamic equivalent controller part. To solve the chattering fuzzy logic inference applied instead of dead zone function. To solve the equivalent problems in classical sliding mode controller this paper focuses on applied on-line tuning method in classical controller. This algorithm works very well in certain and uncertain environment. The system performance in sliding mode controller is sensitive to the sliding function. Therefore, compute the optimum value of sliding function for a system is the next challenge. This problem has solved by adjusting sliding function of the on-line method continuously in real-time. In this way, the overall system performance has improved with respect to the classical sliding mode controller. This controller solved chattering phenomenon as well as mathematical nonlinear equivalent part by applied modified PID supervisory method in modified fuzzy sliding mode controller and tuning the sliding function.

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Artificial Chattering Free on-line Modified Sliding Mode Algorithm: Applied in Continuum Robot Manipulator

Intelligent fuzzy sliding mode control for complex robot system with disturbances

This paper describes the design and implementation of robust nonlinear sliding mode control strategies for robot manipulators whose dynamic or kinematic models are uncertain. Therefore a fuzzy sliding mode tracking controller for robot manipulators with uncertainty in the kinematic and dynamic models is design and analyzes. The controller is developed based on the unit quaternion representation so that singularities associated with the otherwise commonly used three parameter representations are avoided. Simulation results for a planar application of the continuum or hyper-redundant robot manipulator (CRM) are provided to illustrate the performance of the developed adaptive controller. These manipulators do not have rigid joints, hence, they are difficult to model and this leads to significant challenges in developing high-performance control algorithms. In this research, a joint level controller for continuum robots is described which utilizes a fuzzy methodology component to compensate for dynamic uncertainties.

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Design Robust Fuzzy Sliding Mode Control Technique for Robot Manipulator Systems with Modeling Uncertainties

Refer to this research, a position modified parallel error-based fuzzy Proportional Derivative (PD) gravity controller is proposed for continuum robot manipulator. The main problem of the pure conventional nonlinear controller was equivalent dynamic formulation in uncertain systems. The main challenge of linear controllers is linearization techniques and the quality of performance. The nonlinear equivalent dynamic problem in uncertain system is solved by applied fuzzy logic theory to modified PD gravity. To estimate the continuum robot manipulator system's dynamic, proportional plus modified derivative with 7 rules Mamdani inference system is design and applied to modified PD gravity methodology. The proportional coefficient of controller is tuned by new methodology in limitation uncertainties. The results demonstrate that the proposed controller is a partly model-free controllers which works well in certain and partly uncertain system.

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Design Modified Fuzzy PD Gravity Controller with Application to Continuum Robot

In this paper the development, modeling and high precision robust control of an electro-mechanical continuum robot manipulator is presented. In this paper main controller is a Sliding Mode Controller which modified by modified PD methodology based on the boundary derivative methodology. Parallel fuzzy logic theory is used to compensate the system dynamic uncertainty controller based on sliding mode theory. Sliding mode controller (SMC) is a significant nonlinear controller under condition of partly uncertain dynamic parameters of system. This controller is used to control of highly nonlinear systems especially for continuum robot manipulator, because this controller is robust and stable in presence of partly uncertainties. PD partly switching nonlinear SMC by modified PD boundary derivative method is used to achieve a stable tracking, while the parallel fuzzy-logic optimization added intelligence to the control system through an automatic tuning of the PD modified partly switching sliding mode methodology uncertainties. Adaptive methodology is used to on-line tuning the sliding surface slope and gain updating factor of this methodology. Simulation results demonstrated the validity of the Mamdani parallel fuzzy-optimization control with asymptotic and stable tracking at different position inputs. This compensation demonstrated a well synchronized control signal at different excitation conditions.

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MohammadMahdi Ebrahimi

Papers

Design Robust Fuzzy Sliding Mode Control Technique for Robot Manipulator Systems with Modeling Uncertainties

Design Modified Fuzzy PD Gravity Controller with Application to Continuum Robot

Design Intelligent Robust Partly Linear Term SMC for Robot Manipulator Systems