Fuzzy-logic control of dynamic systems: from modeling to design (original) (raw)
Related papers
Fuzzy sliding mode control for a robot manipulator
Artificial Life and Robotics, 2008
Fuzzy sliding mode control for a robot manipulator multi-input-multi-output systems. The most signifi cant property of an SMC is its robustness. SMC nowadays enjoys a wide variety of application areas, such as in robotics, 1 in process control, 2 in dc motor control 3 and so on.
I.J. Information Technology and Computer Science, 2013
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.
Adaptive fuzzy sliding mode motion control of robot manipulator
Proceedings of the 15th IFAC World Congress, 2002, 2002
This paper describes development and implementation of a decentralized continuous sliding mode motion controller for the robot manipulators. Adaptive fuzzy logic systems (FLSs), one for each robot axis, are employed to approximate almost a whole system dynamics. The structural properties of the robot dynamics are used for division of the each FLS to three simpler subsystems. This reduces the FLS's complexity, emphasizes their transparency and enables systematized inclusion of the linguistic knowledge. The validity of the controller scheme was tested by experiments on a three-degree of freedom direct drive robot.
Applied Soft Computing, 2011
Control of an industrial robot includes nonlinearities, uncertainties and external perturbations that should be considered in the design of control laws. This paper presents a control strategy for robot manipulators, based on the coupling of the fuzzy logic control with the so-called sliding mode control, SMC, approach. The motivation for using SMC in robotics mainly relies on its appreciable features, such as design simplicity and robustness. Yet, the chattering effect, typical of the conventional SMC, can be destructive. In this paper, this problem is suitably circumvented by adopting an adaptive fuzzy sliding mode control, AFSMC, approach with a proportional-integral-derivative, PID sliding surface. For this proposed approach, we have used a fuzzy logic control to generate the hitting control signal. Moreover, the output gain of the fuzzy sliding mode control, FSMC, is tuned on-line by a supervisory fuzzy system, so the chattering is avoided. The stability of the system is guaranteed in the sense of the Lyapunov stability theorem. Numerical simulations using the dynamic model of a 3 DOF planar rigid robot manipulator with uncertainties show the effectiveness of the approach in trajectory tracking problems. The simulation results that are compared with the results of conventional SMC with PID sliding surface indicate that the control performance of the robot system is satisfactory and the proposed AFSMC can achieve favorable tracking performance, and it is robust with regard to uncertainties and disturbances.
An Adaptive Fuzzy Sliding Mode Control Scheme for Robotic Systems
Intelligent Control and Automation, 2011
In this article, an adaptive fuzzy sliding mode control (AFSMC) scheme is derived for robotic systems. In the AFSMC design, the sliding mode control (SMC) concept is combined with fuzzy control strategy to obtain a model-free fuzzy sliding mode control. The equivalent controller has been substituted for by a fuzzy system and the uncertainties are estimated on-line. The approach of the AFSMC has the learning ability to generate the fuzzy control actions and adaptively compensates for the uncertainties. Despite the high nonlinearity and coupling effects, the control input of the proposed control algorithm has been decoupled leading to a simplified control mechanism for robotic systems. Simulations have been carried out on a two link planar robot. Results show the effectiveness of the proposed control system.
A Robust Fuzzy Tracking Control Scheme for Robotic Manipulators with Experimental Verification
Intelligent Control and Automation, 2011
The performance of any fuzzy logic controller (FLC) is greatly dependent on its inference rules. In most cases, the closed-loop control performance and stability are enhanced if more rules are added to the rule base of the FLC. However, a large set of rules requires more on-line computational time and more parameters need to be adjusted. In this paper, a robust PD-type FLC is driven for a class of MIMO second order nonlinear systems with application to robotic manipulators. The rule base consists of only four rules per each degree of freedom (DOF). The approach implements fuzzy partition to the state variables based on Lyapunov synthesis. The resulting control law is stable and able to exploit the dynamic variables of the system in a linguistic manner. The presented methodology enables the designer to systematically derive the rule base of the control. Furthermore, the controller is decoupled and the procedure is simplified leading to a computationally efficient FLC. The methodology is model free approach and does not require any information about the system nonlinearities, uncertainties, time varying parameters, etc. Here, we present experimental results for the following controllers: the conventional PD controller, computed torque controller (CTC), sliding mode controller (SMC) and the proposed FLC. The four controllers are tested and compared with respect to ease of design, implementation, and performance of the closed-loop system. Results show that the proposed FLC has outperformed the other controllers.
A Simple Robust Sliding-Mode Fuzzy-Logic Controller of the Diagonal Type
Journal of Intelligent and Robotic Systems, 1999
This paper derives and analyzes a new robust fuzzy-logic sliding-mode controller of the diagonal type, which does not need the prior design of the rule base. The basic objective of the controller is to keep the system on the sliding surface so as to ensure the asympotic stability of the closed-loop system. The control law consists of two rules: (i) IF sign(e(t)ė(t)) 0 THEN change the control action, where e(t) = x(t) − xd(t) is the system state error, and the control action can be either an increase or decrease of the control signal, which is realized through the use of fuzzy rules. The proposed controller, which does not need the prior knowledge of the system model and the prior design of the membership functions" shape, was tested, by simulation, on linear and nonlinear systems. The performance was in all cases excellent (very fast trajectory tracking, no chattering) . Of course, as in traditional control, there was a trade-off between the rise-time and the overshoot of the system response.
International Journal of Intelligent Systems and Applications, vol.6, no.4, pp.63-75, 2014. DOI: 10.5815/ijisa, 2014
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.
Comparison of Conventional & Fuzzy based Sliding Mode PID Controller for Robot Manipulator
IEEE
High accuracy trajectory tracking is challenging topic in robotic manipulator control. This is due to nonlinearities and input coupling present in robotic arm. In this paper, a chattering free sliding mode control (SMC) for a robot manipulator including PID part with a fuzzy tunable gain is designed. The main idea is that the robustness property of SMC and good response characteristics of PID are combined with fuzzy tuning gain approach to achieve more acceptable performance. A PID sliding surface is considered such that the robot dynamic equation can be rewritten in terms of sliding surface. Then in order to decrease the reaching time to the sliding surface and deleting the oscillation of the response, a fuzzy tuning system is used for adjusting both controller gains including sliding controller gain parameter and PID coefficient. Controller is applied to two link robot manipulator including model uncertainty and external disturbance as a case study. Simulation study has been done in MATLAB/Simulink environment shows the improvements of the results compare to conventional SMC.
Comparison of conventional & fuzzy based sliding mode PID controller for robot manipulator
2013 International Conference on Individual and Collective Behaviors in Robotics (ICBR), 2013
High accuracy trajectory tracking is challenging topic in robotic manipulator control. This is due to nonlinearities and input coupling present in robotic arm. In this paper, a chattering free sliding mode control (SMC) for a robot manipulator including PID part with a fuzzy tunable gain is designed. The main idea is that the robustness property of SMC and good response characteristics of PID are combined with fuzzy tuning gain approach to achieve more acceptable performance. A PID sliding surface is considered such that the robot dynamic equation can be rewritten in terms of sliding surface. Then in order to decrease the reaching time to the sliding surface and deleting the oscillation of the response, a fuzzy tuning system is used for adjusting both controller gains including sliding controller gain parameter and PID coefficient. Controller is applied to two link robot manipulator including model uncertainty and external disturbance as a case study. Simulation study has been done in MATLAB/Simulink environment shows the improvements of the results compare to conventional SMC.