An investigation on steering optimization for minimum turning radius of multi-axle crane based on MPC algorithm

Oh, Kwangseok; Seo, Jaho; Kim, Jin-Ho; Yi, Kyongsu

doi:10.1109/iccas.2015.7364691

Cited by 11 publications

(10 citation statements)

References 4 publications

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“…Moreover, in practical applications, the quality and flexibility of lifting ropes as an influential factor is neglected in modeling [5]. In accordance with the author's research, there are two major kinds of modeling in underactuated overhead cranes: the simplified model and the expansion model [6]. An overhead crane is mainly responsible for delivering goods to specified locations.…”

Section: Underactuated Overhead Cranes and Nonlinear Controlmentioning

confidence: 99%

Nonlinear Control Strategies and Planning for Underactuated Overhead Cranes

Slabber¹

2020

Eng. herit. j.

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Underactuated overhead cranes play an important role in engineering and construction, which also make nonlinear control strategies and planning on this basis become the current focus of academic research. Based on scholarly research findings, this paper carries out a theoretical study on nonlinear control strategies and planning for underactuated overhead cranes. To begin with, the underactuated system, underactuated overhead cranes and its nonlinear control are elucidated. Afterwards, the stabilization methods for front actuators are analyzed, and finally two nonlinear control methods are explored in the hope of providing some references for research in related fields.

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Section: Underactuated Overhead Cranes and Nonlinear Controlmentioning

confidence: 99%

Nonlinear Control Strategies and Planning for Underactuated Overhead Cranes

Slabber¹

2020

Eng. herit. j.

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“…We can substitute (11) and (13) into (14), and leave the polynomial with the variable U to be optimized. The cost function becomes…”

Section: B Optimal Solutionmentioning

confidence: 99%

“…The non-zero initial velocity state for braking can introduce strong residual vibrations of the load when the emergency stop is required. Although many works have been proposed to solve the stable control problem of the overhead crane, most of them assume the crane has zero initial velocity state, e.g., PID control [6], fuzzy control [7], [8], optimal control [9], sliding-mode control [10] - [12], model predictive control [13], [14], command shaping control [15] - [17]. To tackle non-zero initial velocity issue, Joaquim Maria Veciana et al designed control inputs by measuring the initial states using a feedback sensor and introducing an appropriate processing time delay in [5].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Safe Distance Prediction Using MPC for Bridge Cranes Considering Anti-Swing in Dynamic Environment

Chen¹,

Liu²,

Tian³

et al. 2020

Preprint

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<div>In dynamic environment, the suddenly appeared </div><div>human or other moving obstacles can affect the safety of the </div><div>bridge crane. For such dangerous situation, the bridge crane </div><div>must predict potential collisions between the payload and the </div><div>obstacle, keep safe distance while the swing of the payload must </div><div>be considered in the mean time. Therefore, the safe distance is </div><div>not a constant value, which must be adaptive to the relative </div><div>speed of the bridge crane. However, as far as we know, the </div><div>mathematical model between the safe distance and the relative </div><div>speed of the bridge crane has never been fully discussed. In </div><div>this paper, we propose a safe distance prediction method using </div><div>model prediction control (MPC), which can make sure that the </div><div>crane can stop before the obstacle, and avoid possible collisions, </div><div>while the relative speed and anti-swing are both considered. The </div><div>experimental results prove the effectiveness of our idea.</div>

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“…This optimal solution can be derived by applying the constraint to every input and state. 16 Figure 9 shows the concept outline of an MPC.…”

Section: Mpc Techniquementioning

confidence: 99%

“…. By rearranging equation (16) with equation 18, the quadratic objective function can be expressed as shown in equation (19)…”

Section: Mpc Techniquementioning

confidence: 99%

Model predictive control–based steering control algorithm for steering efficiency of a human driver in all-terrain cranes

Seo

Noh

2019

Advances in Mechanical Engineering

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All-terrain cranes with multi-axles have large inertia and long distances between the axles that lead to a slower dynamic response than normal vehicles. This has a significant effect on the dynamic behavior and steering performance of the crane. Therefore, the purpose of this study is to develop an optimal steering control algorithm with a reduced driver steering effort for an all-terrain crane and to evaluate the performance of the algorithm. For this, a model predictive control technique was applied to an all-terrain crane, and a steering control algorithm for the crane was proposed that could reduce the driver's steering effort. The steering performances of the existing steering system and the steering system applied with the newly developed algorithm were compared using MATLAB/Simulink and ADAMS with a human driver model for reasonable performance evaluation. The simulation was performed with both a double lane change scenario and a curved-path scenario that are expected to happen in road-steering mode.

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An investigation on steering optimization for minimum turning radius of multi-axle crane based on MPC algorithm

Cited by 11 publications

References 4 publications

Nonlinear Control Strategies and Planning for Underactuated Overhead Cranes

Nonlinear Control Strategies and Planning for Underactuated Overhead Cranes

Adaptive Safe Distance Prediction Using MPC for Bridge Cranes Considering Anti-Swing in Dynamic Environment

Model predictive control–based steering control algorithm for steering efficiency of a human driver in all-terrain cranes

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