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Changyin Sun - One of the best experts on this subject based on the ideXlab platform.
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neural network control of a two link flexible Robotic Manipulator using assumed mode method
IEEE Transactions on Industrial Informatics, 2019Co-Authors: Hejia Gao, Chen Zhou, Changyin SunAbstract:In this paper, the n-dimensional discretized model of the two-link flexible Manipulator is developed by the assumed mode method (AMM). Subsequently, based on the discretized dynamic model, both full-state feedback control and output feedback control are investigated to achieve the trajectory tracking and vibration suppression. In order to guarantee the stability strictly, uniform ultimate boundedness (UUB) of the closed-loop system is realized by the Lyapunov's stability. Furthermore, through appropriately choosing control parameters, the states of the system will converge to zero within a small neighborhood. Eventually, extensive simulations and experiments on the Quanser platform for a two-link Robotic Manipulator are carried out to demonstrate the feasibility of the proposed neural network controller.
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boundary vibration control for a flexible timoshenko Robotic Manipulator
Iet Control Theory and Applications, 2018Co-Authors: Hui Qin, Changyin SunAbstract:The authors investigate the dynamic modelling problem and the active vibration control design problem for a flexible Timoshenko Robotic Manipulator in this study. For the practical Robotic Manipulator, the authors analyse the dynamic characteristic considering the shear deformation and elastic deflection of the flexible arm link and describe this flexible system with a coupled partial differential equation and ordinary differential equations (PDE–ODEs) model. Furthermore, the authors design the active vibration control and disturbance observers to solve the vibration problem of the flexible Robotic Manipulator under the external disturbances, namely, to reduce the shear deformation and the deflection of the flexible Timoshenko link. Besides, the Manipulator is driven to track the given angular position with an active angular controller, simultaneously. The effectiveness of the designed vibration control laws is analysed by the theoretical analysis and verified by the numerical simulation.
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neural learning based control for a constrained Robotic Manipulator with flexible joints
IEEE Transactions on Neural Networks, 2018Co-Authors: Zichen Yan, Yongkun Sun, Changyin SunAbstract:Nowadays, the control technology of the Robotic Manipulator with flexible joints (RMFJ) is not mature enough. The flexible-joint Manipulator dynamic system possesses many uncertainties, which brings a great challenge to the controller design. This paper is motivated by this problem. In order to deal with this and enhance the system robustness, the full-state feedback neural network (NN) control is proposed. Moreover, output constraints of the RMFJ are achieved, which improve the security of the robot. Through the Lyapunov stability analysis, we identify that the proposed controller can guarantee not only the stability of flexible-joint Manipulator system but also the boundedness of system state variables by choosing appropriate control gains. Then, we make some necessary simulation experiments to verify the rationality of our controllers. Finally, a series of control experiments are conducted on the Baxter. By comparing with the proportional–derivative control and the NN control with the rigid Manipulator model, the feasibility and the effectiveness of NN control based on flexible-joint Manipulator model are verified.
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robust adaptive vibration control for an uncertain flexible timoshenko Robotic Manipulator with input and output constraints
International Journal of Systems Science, 2017Co-Authors: Changyin SunAbstract:ABSTRACTThe problems of the constraints and the vibration suppression are investigated for the flexible Timoshenko Robotic Manipulator in this paper. Robust adaptive boundary control laws with the disturbance observes are designed to guarantee the convergence of the feedback flexible Timoshenko Robotic Manipulator system with the uncertain parameters and the states are proven to be uniform bounded. In addition, the proposed boundary controls are verified to be effectiveness by the numeral experiments.
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neural network control of a flexible Robotic Manipulator using the lumped spring mass model
IEEE Transactions on Systems Man and Cybernetics, 2017Co-Authors: Changyin Sun, Jie HongAbstract:Adaptive neural networks (NNs) are employed for control design to suppress vibrations of a flexible Robotic Manipulator. To improve the accuracy in describing the elastic deflection of the flexible Manipulator, the system is modeled via the lumped spring-mass approach. Full-state feedback control as well as output feedback control are proposed separately. Aiming at achieving the control objective, uniform ultimate boundedness of the closed-loop system is ensured. Numerical simulations for the lumped model of the flexible Robotic system are carried out to verify the performance of the NN control. Finally, the experiments are given to further validate the feasibility of the proposed NN controllers on the Quanser platform.
Richa Sharma - One of the best experts on this subject based on the ideXlab platform.
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design of two layered fractional order fuzzy logic controllers applied to Robotic Manipulator with variable payload
Soft Computing, 2016Co-Authors: Richa Sharma, Prerna Gaur, A P MittalAbstract:Two-layered fractional order fuzzy logic controller (TL-FOFLC) is implemented for Robotic Manipulator.The optimal controller parameters are obtained with cuckoo search algorithm.Robustness analysis of proposed scheme is done with its integer order design, conventional FLC and PID controllers.The proposed scheme outperforms its integer order design, conventional FLC and PID controller. The Robotic Manipulators are highly coupled and nonlinear systems wherein the time-varying parameters and uncertainties adversely affect the characteristics and response of these systems. Hence, these systems require an effective and robust controller to handle such complexities which is a difficult challenge for control engineers. This paper presents two-layered fractional order fuzzy logic controller (TL-FOFLC) scheme for a two-link planar rigid Robotic Manipulator with payload for trajectory tracking task. For the optimal design, the controller parameters of the proposed scheme are obtained with potential meta-heuristic technique named as cuckoo search algorithm (CSA). In order to ensure effectiveness, the performance of proposed TL-FOFLC is compared with that of its integer order design approach, i.e., two-layered FLC (TL-FLC), single-layered FLC (SL-FLC), and the conventional proportional-integral-derivative (PID) controllers. Further, the robustness testing is carried out for parameter variations and external disturbance rejection.
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an adaptive pid like controller using mix locally recurrent neural network for Robotic Manipulator with variable payload
Isa Transactions, 2016Co-Authors: Richa Sharma, Vikas Kumar, Prerna Gaur, A P MittalAbstract:Being complex, non-linear and coupled system, the Robotic Manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link Robotic Manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller.
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performance analysis of two degree of freedom fractional order pid controllers for Robotic Manipulator with payload
Isa Transactions, 2015Co-Authors: Richa Sharma, Prerna Gaur, Alok MittalAbstract:The Robotic Manipulators are multi-input multi-output (MIMO), coupled and highly nonlinear systems. The presence of external disturbances and time-varying parameters adversely affects the performance of these systems. Therefore, the controller designed for these systems should effectively deal with such complexities, and it is an intriguing task for control engineers. This paper presents two-degree of freedom fractional order proportional-integral-derivative (2-DOF FOPID) controller scheme for a two-link planar rigid Robotic Manipulator with payload for trajectory tracking task. The tuning of all controller parameters is done using cuckoo search algorithm (CSA). The performance of proposed 2-DOF FOPID controllers is compared with those of their integer order designs, i.e., 2-DOF PID controllers, and with the traditional PID controllers. In order to show effectiveness of proposed scheme, the robustness testing is carried out for model uncertainties, payload variations with time, external disturbance and random noise. Numerical simulation results indicate that the 2-DOF FOPID controllers are superior to their integer order counterparts and the traditional PID controllers.
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performance analysis of fractional order fuzzy pid controllers applied to a Robotic Manipulator
Expert Systems With Applications, 2014Co-Authors: Richa Sharma, K P S Rana, Vineet KumarAbstract:A two-link Robotic Manipulator is a Multi-Input Multi-Output (MIMO), highly nonlinear and coupled system. Therefore, designing an efficient controller for this system is a challenging task for the control engineers. In this paper, the Fractional Order Fuzzy Proportional-Integral-Derivative (FOFPID) controller for a two-link planar rigid Robotic Manipulator for trajectory tracking problem is investigated. Robustness testing of FOFPID controller for model uncertainties, disturbance rejection and noise suppression is also investigated. To study the effectiveness of FOFPID controller, its performance is compared with other three controllers namely Fuzzy PID (FPID), Fractional Order PID (FOPID) and conventional PID. For tuning of parameters of all the controllers, Cuckoo Search Algorithm (CSA) optimization technique was used. Two performance indices namely Integral of Absolute Error (IAE) and Integral of Absolute Change in Controller Output (IACCO) having equal weightage for both the links are considered for minimization. Numerical simulation results clearly indicate the superiority of FOFPID controller over the other controllers for trajectory tracking, model uncertainties, disturbance rejection and noise suppression.
Joe Viola - One of the best experts on this subject based on the ideXlab platform.
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Fractional order PID for tracking control of a parallel Robotic Manipulator type delta
ISA Transactions, 2018Co-Authors: L. Angel, Joe ViolaAbstract:This paper presents the tracking control for a Robotic Manipulator type delta employing fractional order PID controllers with computed torque control strategy. It is contrasted with an integer order PID controller with computed torque control strategy. The mechanical structure, kinematics and dynamic models of the delta robot are descripted. A SOLIDWORKS/MSC-ADAMS/MATLAB cosimulation model of the delta robot is built and employed for the stages of identification, design, and validation of control strategies. Identification of the dynamic model of the robot is performed using the least squares algorithm. A linearized model of the Robotic system is obtained employing the computed torque control strategy resulting in a decoupled double integrating system. From the linearized model of the delta robot, fractional order PID and integer order PID controllers are designed, analyzing the dynamical behavior for many evaluation trajectories. Controllers robustness is evaluated against external disturbances employing performance indexes for the joint and spatial error, applied torque in the joints and trajectory tracking. Results show that fractional order PID with the computed torque control strategy has a robust performance and active disturbance rejection when it is applied to parallel Robotic Manipulators on tracking tasks.
Vineet Kumar - One of the best experts on this subject based on the ideXlab platform.
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performance analysis of fractional order fuzzy pid controllers applied to a Robotic Manipulator
Expert Systems With Applications, 2014Co-Authors: Richa Sharma, K P S Rana, Vineet KumarAbstract:A two-link Robotic Manipulator is a Multi-Input Multi-Output (MIMO), highly nonlinear and coupled system. Therefore, designing an efficient controller for this system is a challenging task for the control engineers. In this paper, the Fractional Order Fuzzy Proportional-Integral-Derivative (FOFPID) controller for a two-link planar rigid Robotic Manipulator for trajectory tracking problem is investigated. Robustness testing of FOFPID controller for model uncertainties, disturbance rejection and noise suppression is also investigated. To study the effectiveness of FOFPID controller, its performance is compared with other three controllers namely Fuzzy PID (FPID), Fractional Order PID (FOPID) and conventional PID. For tuning of parameters of all the controllers, Cuckoo Search Algorithm (CSA) optimization technique was used. Two performance indices namely Integral of Absolute Error (IAE) and Integral of Absolute Change in Controller Output (IACCO) having equal weightage for both the links are considered for minimization. Numerical simulation results clearly indicate the superiority of FOFPID controller over the other controllers for trajectory tracking, model uncertainties, disturbance rejection and noise suppression.
Prerna Gaur - One of the best experts on this subject based on the ideXlab platform.
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design of two layered fractional order fuzzy logic controllers applied to Robotic Manipulator with variable payload
Soft Computing, 2016Co-Authors: Richa Sharma, Prerna Gaur, A P MittalAbstract:Two-layered fractional order fuzzy logic controller (TL-FOFLC) is implemented for Robotic Manipulator.The optimal controller parameters are obtained with cuckoo search algorithm.Robustness analysis of proposed scheme is done with its integer order design, conventional FLC and PID controllers.The proposed scheme outperforms its integer order design, conventional FLC and PID controller. The Robotic Manipulators are highly coupled and nonlinear systems wherein the time-varying parameters and uncertainties adversely affect the characteristics and response of these systems. Hence, these systems require an effective and robust controller to handle such complexities which is a difficult challenge for control engineers. This paper presents two-layered fractional order fuzzy logic controller (TL-FOFLC) scheme for a two-link planar rigid Robotic Manipulator with payload for trajectory tracking task. For the optimal design, the controller parameters of the proposed scheme are obtained with potential meta-heuristic technique named as cuckoo search algorithm (CSA). In order to ensure effectiveness, the performance of proposed TL-FOFLC is compared with that of its integer order design approach, i.e., two-layered FLC (TL-FLC), single-layered FLC (SL-FLC), and the conventional proportional-integral-derivative (PID) controllers. Further, the robustness testing is carried out for parameter variations and external disturbance rejection.
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an adaptive pid like controller using mix locally recurrent neural network for Robotic Manipulator with variable payload
Isa Transactions, 2016Co-Authors: Richa Sharma, Vikas Kumar, Prerna Gaur, A P MittalAbstract:Being complex, non-linear and coupled system, the Robotic Manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link Robotic Manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller.
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performance analysis of two degree of freedom fractional order pid controllers for Robotic Manipulator with payload
Isa Transactions, 2015Co-Authors: Richa Sharma, Prerna Gaur, Alok MittalAbstract:The Robotic Manipulators are multi-input multi-output (MIMO), coupled and highly nonlinear systems. The presence of external disturbances and time-varying parameters adversely affects the performance of these systems. Therefore, the controller designed for these systems should effectively deal with such complexities, and it is an intriguing task for control engineers. This paper presents two-degree of freedom fractional order proportional-integral-derivative (2-DOF FOPID) controller scheme for a two-link planar rigid Robotic Manipulator with payload for trajectory tracking task. The tuning of all controller parameters is done using cuckoo search algorithm (CSA). The performance of proposed 2-DOF FOPID controllers is compared with those of their integer order designs, i.e., 2-DOF PID controllers, and with the traditional PID controllers. In order to show effectiveness of proposed scheme, the robustness testing is carried out for model uncertainties, payload variations with time, external disturbance and random noise. Numerical simulation results indicate that the 2-DOF FOPID controllers are superior to their integer order counterparts and the traditional PID controllers.
