Consistency of control performance in 3d overhead cranes under payload mass uncertainty
- Hoang, Uyen, Le, Hai, Thai, Nguyen, Pham, Hung, Nguyen, Linh
- Authors: Hoang, Uyen , Le, Hai , Thai, Nguyen , Pham, Hung , Nguyen, Linh
- Date: 2020
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 9, no. 4 (2020), p.
- Full Text:
- Reviewed:
- Description: The paper addresses the problem of effectively and robustly controlling a 3D overhead crane under the payload mass uncertainty, where the control performance is shown to be consistent. It is proposed to employ the sliding mode control technique to design the closed-loop controller due to its robustness, regardless of the uncertainties and nonlinearities of the under-actuated crane system. The radial basis function neural network has been exploited to construct an adaptive mechanism for estimating the unknown dynamics. More importantly, the adaptation methods have been derived from the Lyapunov theory to not only guarantee stability of the closed-loop control system, but also approximate the unknown and uncertain payload mass and weight matrix, which maintains the consistency of the control performance, although the cargo mass can be varied. Furthermore, the results obtained by implementing the proposed algorithm in the simulations show the effectiveness of the proposed approach and the consistency of the control performance, although the payload mass is uncertain. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Hoang, Uyen , Le, Hai , Thai, Nguyen , Pham, Hung , Nguyen, Linh
- Date: 2020
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 9, no. 4 (2020), p.
- Full Text:
- Reviewed:
- Description: The paper addresses the problem of effectively and robustly controlling a 3D overhead crane under the payload mass uncertainty, where the control performance is shown to be consistent. It is proposed to employ the sliding mode control technique to design the closed-loop controller due to its robustness, regardless of the uncertainties and nonlinearities of the under-actuated crane system. The radial basis function neural network has been exploited to construct an adaptive mechanism for estimating the unknown dynamics. More importantly, the adaptation methods have been derived from the Lyapunov theory to not only guarantee stability of the closed-loop control system, but also approximate the unknown and uncertain payload mass and weight matrix, which maintains the consistency of the control performance, although the cargo mass can be varied. Furthermore, the results obtained by implementing the proposed algorithm in the simulations show the effectiveness of the proposed approach and the consistency of the control performance, although the payload mass is uncertain. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
An efficient adaptive fuzzy hierarchical sliding mode control strategy for 6 degrees of freedom overhead crane
- Pham, Hung, Hoang, Quoc-Dong, Pham, Minh, Do, Dung, Phi, Nha-Hoang, Hoang, Duy, Le, Hai, Kim, Thai, Nguyen, Linh
- Authors: Pham, Hung , Hoang, Quoc-Dong , Pham, Minh , Do, Dung , Phi, Nha-Hoang , Hoang, Duy , Le, Hai , Kim, Thai , Nguyen, Linh
- Date: 2022
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 11, no. 5 (2022), p.
- Full Text:
- Reviewed:
- Description: The paper proposes a new approach to efficiently control a three-dimensional overhead crane with 6 degrees of freedom (DoF). Most of the works proposing a control law for a gantry crane assume that it has five output variables, including three positions of the trolley, bridge, and pulley and two swing angles of the hoisting cable. In fact, the elasticity of the hoisting cable, which causes oscillation in the cable direction, is not fully incorporated into the model yet. Therefore, our work considers that six under-actuated outputs exist in a crane system. To design an efficient controller for the 6 DoF crane, it first employs the hierarchical sliding mode control approach, which not only guarantees stability but also minimizes the sway and oscillation of the overhead crane when it transports a payload to a desired location. Moreover, the unknown and uncertain parameters of the system caused by its actuator nonlinearity and external disturbances are adaptively estimated and inferred by utilizing the fuzzy inference rule mechanism, which results in efficient operations of the crane in real time. More importantly, stabilization of the crane controlled by the proposed algorithm is theoretically proved by the use of the Lyapunov function. The proposed control approach was implemented in a synthetic environment for the extensive evaluation, where the obtained results demonstrate its effectiveness. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Pham, Hung , Hoang, Quoc-Dong , Pham, Minh , Do, Dung , Phi, Nha-Hoang , Hoang, Duy , Le, Hai , Kim, Thai , Nguyen, Linh
- Date: 2022
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 11, no. 5 (2022), p.
- Full Text:
- Reviewed:
- Description: The paper proposes a new approach to efficiently control a three-dimensional overhead crane with 6 degrees of freedom (DoF). Most of the works proposing a control law for a gantry crane assume that it has five output variables, including three positions of the trolley, bridge, and pulley and two swing angles of the hoisting cable. In fact, the elasticity of the hoisting cable, which causes oscillation in the cable direction, is not fully incorporated into the model yet. Therefore, our work considers that six under-actuated outputs exist in a crane system. To design an efficient controller for the 6 DoF crane, it first employs the hierarchical sliding mode control approach, which not only guarantees stability but also minimizes the sway and oscillation of the overhead crane when it transports a payload to a desired location. Moreover, the unknown and uncertain parameters of the system caused by its actuator nonlinearity and external disturbances are adaptively estimated and inferred by utilizing the fuzzy inference rule mechanism, which results in efficient operations of the crane in real time. More importantly, stabilization of the crane controlled by the proposed algorithm is theoretically proved by the use of the Lyapunov function. The proposed control approach was implemented in a synthetic environment for the extensive evaluation, where the obtained results demonstrate its effectiveness. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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