Approach In Trajectory Tracking Problems Research Articles

Adaptive fuzzy sliding mode control using supervisory fuzzy control for 3 DOF planar robot manipulators

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.

Fuzzy Self-Tuning Precompensation PD Control with Gravity Compensation of 3 DOF Planar Robot Manipulators

Industrial robot control covers nonlinearity, uncertainty and external perturbation considered in control laws design. Proportional and Derivative (PD) with gravity compensation control is well-known control used in manipulators to ensure global asymptotic stability for fixed symmetrical positive definite gain matrices. To enhance PD with gravity compensation controller performance, in this paper, we propose hybrid fuzzy PD control precompensation with gravity compensation, consisting of a fuzzy logic-based precompensator followed by hybrid fuzzy PD with gravity compensation controller. Hybrid fuzzy control is done by a Supervisory Hierarchical Fuzzy Controller (SHFC) for tuning conventional controller Proportional and Derivative gains based on actual tracking location and velocity error. Hierarchical hybrid fuzzy control consists of an intelligent upper supervisory fuzzy controller and a lower direct conventional PD controller. Numerical simulations using the dynamic model of a three DOF planar rigid robot manipulator with uncertainty show the effectiveness of the approach in trajectory tracking problems. Our results show that the proposal controller has performance superior to a conventional controller.

Hybrid bacterial foraging and particle swarm optimisation for fuzzy precompensated control of flexible manipulator

This paper presents hybrid approach combining the social foraging behaviour of Escherichia coli bacteria and particle swarm optimisation for optimising hybrid fuzzy precompensated proportional-derivative (PD) controller in trajectory control of two-link rigid-flexible manipulator. Numerical simulation using the dynamic model of the two-link rigid-flexible manipulator shows the effectiveness of the approach in trajectory tracking problems. The use of fuzzy precompensation has superior performance in terms of improvement in transient and steady state response, robustness to variations in loading conditions and ease to use in practice. Comparative evaluation with respect to genetic algorithm, particle swarm and bacterial foraging-based optimisation is presented to validate the controller design. The proposed algorithm performs local search through the chemotactic movement operation of bacterial foraging whereas the global search over the entire search space is accomplished by a particle swarm operator and so satisfactory tracking precision could be achieved using the approach.

New hybrid adaptive neuro-fuzzy algorithms for manipulator control with uncertainties–Comparative study

Control of an industrial robot includes nonlinearities, uncertainties and external perturbations that should be considered in the design of control laws. In this paper, some new hybrid adaptive neuro-fuzzy control algorithms (ANFIS) have been proposed for manipulator control with uncertainties. These hybrid controllers consist of adaptive neuro-fuzzy controllers and conventional controllers. The outputs of these controllers are applied to produce the final actuation signal based on current position and velocity errors. Numerical simulation using the dynamic model of six DOF puma robot arm with uncertainties shows the effectiveness of the approach in trajectory tracking problems. Performance indices of RMS error, maximum error are used for comparison. It is observed that the hybrid adaptive neuro-fuzzy controllers perform better than only conventional/adaptive controllers and in particular hybrid controller structure consisting of adaptive neuro-fuzzy controller and critically damped inverse dynamics controller.

Adaptive Neuro-Fuzzy Inference System based control of six DOF robot manipulator

The dynamics of robot manipulators are highly nonlinear with strong couplings existing between joints and are frequently subjected to structured and unstructured uncertainties. Fuzzy Logic Controller can very well describe the desired system behavior with simple if-then relations owing the designer to derive if-then rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). This paper presents the control of six degrees of freedom robot arm (PUMA Robot) using Adaptive Neuro Fuzzy Inference System (ANFIS) based PD plus I controller. Numerical simulation using the dynamic model of six DOF robot arm shows the effectiveness of the approach in trajectory tracking problems. Comparative evaluation with respect to PID, Fuzzy PD+I controls are presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using ANFIS controller than PID and Fuzzy PD+I controllers Abstract The dynamics of robot manipulators are highly nonlinear with strong couplings existing between joints and are frequently subjected to structured and unstructured uncertainties. Fuzzy Logic Controller can very well describe the desired system behavior with simple if-then relations owing the designer to derive if-then rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). This paper presents the control of six degrees of freedom robot arm (PUMA Robot) using Adaptive Neuro Fuzzy Inference System (ANFIS) based PD plus I controller. Numerical simulation using the dynamic model of six DOF robot arm shows the effectiveness of the approach in trajectory tracking problems. Comparative evaluation with respect to PID, Fuzzy PD+I controls are presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using ANFIS controller than PID and Fuzzy PD+I controllers Abstract The dynamics of robot manipulators are highly nonlinear with strong couplings existing between joints and are frequently subjected to structured and unstructured uncertainties. Fuzzy Logic Controller can very well describe the desired system behavior with simple if-then relations owing the designer to derive if-then rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). This paper presents the control of six degrees of freedom robot arm (PUMA Robot) using Adaptive Neuro Fuzzy Inference System (ANFIS) based PD plus I controller. Numerical simulation using the dynamic model of six DOF robot arm shows the effectiveness of the approach in trajectory tracking problems. Comparative evaluation with respect to PID, Fuzzy PD+I controls are presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using ANFIS controller than PID and Fuzzy PD+I controllers

Fuzzy PD+I control of a six DOF robot manipulator

PurposeThe purpose of this paper is to present the control of a six degrees of freedom (DOF) robot arm (PUMA robot) using fuzzy PD + I controller. Numerical simulation using the dynamic model of six DOF robot arm shows the effectiveness of the approach in trajectory tracking problems. Comparative evaluation with respect to PID and fuzzy PID controls are presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using fuzzy PD + I controller combination than fuzzy PID controller.Design/methodology/approachControl of a six DOF robot arm (PUMA Robot) using fuzzy PD + I controller.FindingsThe performance of fuzzy PD + I controllers improves appreciably compared to their respective fuzzy PID only or conventional PID counterparts.Originality/valueComplexity of the proposed fuzzy PID controller is minimized as possible and only two design variables are used to adjust the rate of variations of the proportional gain and derivative gain.