Particle Swarm Optimization-Based Multivariable Generalized Predictive Control for an Overhead Crane

Smoczek, J.; Szpytko, Janusz

doi:10.1109/tmech.2016.2598606

Cited by 105 publications

(38 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recently, MPC has still been relevant in different crane control approaches as [29][30][31][32][33]. Concerning GPC, [34,35] used this strategy to control an overhead crane system, while [36] applied a GPC to control an offshore crane operation. Hence, in this section, the GPC described in [28] is adapted and rewritten to control crane systems.…”

Section: Multivariable Gpc Formulationmentioning

confidence: 99%

See 1 more Smart Citation

Explicit GPC Control Applied to an Approximated Linearized Crane System

Fonseca

Dantas

Dórea

et al. 2019

Journal of Control Science and Engineering

View full text Add to dashboard Cite

This paper proposes a MIMO Explicit Generalized Predictive Control (EGPC) for minimizing payload oscillation of a Gantry Crane System subject to input and output constraints. In order to control the crane system efficiently, the traditional GPC formulation, based on online Quadratic Programming (QP), is rewritten as a multiparametric quadratic programming problem (mp-QP). An explicit Piecewise Affine (PWA) control law is obtained and holds the same performance as online QP. To test effectiveness, the proposed method is compared with two GPC formulations: one that handle constraints (CGPC) and another that does not handle constraints (UGPC). Results show that both EGPC and CGPC have better performance, reducing the payload swing when compared to UGPC. Also both EGPC and CGPC are able to control the system without constraint violation. When comparing EGPC to CGPC, the first is able to calculate (during time step) the control action faster than the second. The simulations prove that the overall performance of EGPC is superior to the other used formulations.

show abstract

Section: Multivariable Gpc Formulationmentioning

confidence: 99%

“…The mp-QP parameters expressed in (34) are defined based on GPC formulation presented in Sections 3 and 4, as follows:…”

Section: Explicit Gpcmentioning

confidence: 99%

Explicit GPC Control Applied to an Approximated Linearized Crane System

Fonseca

Dantas

Dórea

et al. 2019

Journal of Control Science and Engineering

View full text Add to dashboard Cite

show abstract

“…On the nonlinear spectra, optimal control based methods have been used, like model predictive control (Wu et al, 2015;Jolevski and Bego, 2015;Käpernick and Graichen, 2013;Khatamianfar and Savkin, 2014;Vukov et al, 2012;Chen et al, 2016;Smoczek and Szpytko, 2017) and the linear quadratic Gaussian predictive approach (Spathopoulos and Fragopoulos, 2004;2001;Smoczek, 2015). The other well established nonlinear methods that have been applied due to their robustness are adaptive control (Nguyen et al, 2015;Cho and Lee, 2008;Fang et al, 2012;Sun et al, 2014;Yang and Shen, 2011;Tar et al, 2010;Fujioka and Singhose, 2015a;Fujioka et al, 2015;Lee et al, 2013) and sliding mode control.…”

Section: Introductionmentioning

confidence: 99%

A Trajectory Planning Based Controller to Regulate an Uncertain 3D Overhead Crane System

Aguilar-Ibáñez

Suárez-Castañón

2019

International Journal of Applied Mathematics and Computer Science

View full text Add to dashboard Cite

We introduce a control strategy to solve the regulation control problem, from the perspective of trajectory planning, for an uncertain 3D overhead crane. The proposed solution was developed based on an adaptive control approach that takes advantage of the passivity properties found in this kind of systems. We use a trajectory planning approach to preserve the accelerations and velocities inside of realistic ranges, to maintaining the payload movements as close as possible to the origin. To this end, we carefully chose a suitable S-curve based on the Bezier spline, which allows us to efficiently handle the load translation problem, considerably reducing the load oscillations. To perform the convergence analysis, we applied the traditional Lyapunov theory, together with Barbalat’s lemma. We assess the effectiveness of our control strategy with convincing numerical simulations.

show abstract

“…Due to their inability to cope with perturbations and disturbances, some researchers combined them with closed‐loop controllers to improve the control performance . As for closed‐loop control schemes, it can be further categorized into proportional integral derivative, linear quadratic regulator, model predictive control (MPC), adaptive control, sliding mode control, etc. The closed‐loop control schemes listed above are all model‐based methods, which suggests that their control performances severely depend on the accurate mathematical modeling of overhead crane systems.…”

Section: Introductionmentioning

confidence: 99%

A unified symplectic pseudospectral method for motion planning and tracking control of 3D underactuated overhead cranes

Wang

Liu

Zhang

et al. 2019

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

Summary In this paper, a unified symplectic pseudospectral method for motion planning and tracking control of 3D underactuated overhead cranes is proposed. A feasible reference trajectory taking constraints into consideration is first generated offline by the symplectic pseudospectral optimal control method. Then, a trajectory tracking model predictive controller also based on the symplectic pseudospectral method is developed to track the reference trajectory. At each sampling instant, the trajectory tracking controller works by solving an open‐loop optimal control problem where linearized system dynamics is used instead to improve the computational efficiency. Since the symplectic pseudospectral optimal control method is the core algorithm for both offline trajectory planning and online trajectory tracking, constraints on state variables and control inputs can be easily imposed and hence theoretically guaranteed in solutions. By selecting proper weighted matrices on tracking error and control, the developed controller could achieve control objectives in both accurate trolley positioning and fast suppressing of residual swing angles. Simulations for 3D overhead crane systems in the presence of perturbations in initial conditions, an abrupt variation of system parameter, and various external disturbances demonstrate that the developed controller is robust and of excellent control performance.

show abstract

Particle Swarm Optimization-Based Multivariable Generalized Predictive Control for an Overhead Crane

Cited by 105 publications

References 41 publications

Explicit GPC Control Applied to an Approximated Linearized Crane System

Explicit GPC Control Applied to an Approximated Linearized Crane System

A Trajectory Planning Based Controller to Regulate an Uncertain 3D Overhead Crane System

A unified symplectic pseudospectral method for motion planning and tracking control of 3D underactuated overhead cranes

Contact Info

Product

Resources

About