TY - GEN
T1 - Nonlinear Fuzzy State-Space Modeling and LMI Fuzzy Control of Overhead Cranes
AU - Leite, Daniel
AU - Aguiar, Charles
AU - Pereira, Daniel
AU - Souza, Gustavo
AU - Skrjanc, Igor
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - The development of feedback control systems for overhead cranes is of great importance due to many potential applications and advantages over manual operation concerning stability and robustness. We represent the key nonlinear dynamics of cranes in a compact state-space fuzzy model. The fuzzy model assists the design of a fuzzy controller through parallel distributed compensation. A conservative linear-matrix-inequality feasibility problem is formulated so that a solution guarantees closed-loop Lyapunov stability, constrained inputs, quick positioning of the supporting cart and suppression of load oscillations. Due to the nonlinear nature of the fuzzy model and controller, Jacobian linearization at a hyperbolic equilibrium is avoided. The proposed fuzzy controller for cranes has shown to be effective, robust and able to move loads smoothly even after collisions. Constrained and smooth inputs avoid actuator saturation and tend to increase its lifetime.
AB - The development of feedback control systems for overhead cranes is of great importance due to many potential applications and advantages over manual operation concerning stability and robustness. We represent the key nonlinear dynamics of cranes in a compact state-space fuzzy model. The fuzzy model assists the design of a fuzzy controller through parallel distributed compensation. A conservative linear-matrix-inequality feasibility problem is formulated so that a solution guarantees closed-loop Lyapunov stability, constrained inputs, quick positioning of the supporting cart and suppression of load oscillations. Due to the nonlinear nature of the fuzzy model and controller, Jacobian linearization at a hyperbolic equilibrium is avoided. The proposed fuzzy controller for cranes has shown to be effective, robust and able to move loads smoothly even after collisions. Constrained and smooth inputs avoid actuator saturation and tend to increase its lifetime.
KW - Fuzzy systems
KW - crane systems
KW - model-based control
KW - multivariable control
UR - http://www.scopus.com/inward/record.url?scp=85073785612&partnerID=8YFLogxK
U2 - 10.1109/FUZZ-IEEE.2019.8858968
DO - 10.1109/FUZZ-IEEE.2019.8858968
M3 - Conference contribution
AN - SCOPUS:85073785612
T3 - IEEE International Conference on Fuzzy Systems
BT - 2019 IEEE International Conference on Fuzzy Systems, FUZZ 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Fuzzy Systems, FUZZ 2019
Y2 - 23 June 2019 through 26 June 2019
ER -