Tip-trajectory tracking control of a deployable cable-driven robot via output redefinition

Khalilpour, S. A.; Khorrambakht, R.; Damirchi, Hamed; Taghirad, Hamid D.; Cardou, Philippe

doi:10.1007/s11044-020-09761-x

Cited by 14 publications

(11 citation statements)

References 33 publications

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“…Cable driven parallel robots (CDPRs) have been attracting an even greater attention of researchers in the fields of robotics and multibody system dynamics because of their benefits that include large workspaces and large payloads, which result in a lightweight and cheap design [1][2][3]. On the other hand, motion planning control of such robots is challenging since cables can pull but not push the end-effector, leading to a unilateral actuation (i.e., just tensile cable forces can be exerted).…”

Section: State Of the Art And Paper Motivationsmentioning

confidence: 99%

“…The idea of designing controllers for flexible systems by means of just simplified model of elasticity, or even by just imposing smooth motion, is often a need in the case of complicated or industrial systems or if computational effort of controller should be kept small, and some recent papers in the field of controlling multibody systems successfully address this issue (see, e.g., [3,22]). This idea of control, which does not exploit a model of the cable flexibility, recalls a dichotomy in the field of motion planning of vibrating systems: oscillation reduction can be addressed either through model-based approaches, which exploit the dynamic model of the flexible system (see, e.g., [23][24][25]), or by model-free approaches that wisely reduce jerk or snap (see, e.g., [26,27]) without considering the precise dynamic model of the oscillations.…”

Section: Paper Contributions and Underlying Idea Of The Control Schemementioning

confidence: 99%

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Model predictive control for path tracking in cable driven parallel robots with flexible cables: collocated vs. noncollocated control

et al. 2023

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This paper proposes a novel control scheme for precise path tracking control in cable driven parallel robots (CDPRs) with axially-flexible cables, with particular focus to the challenging case of cable suspended parallel robots (CSPRs). To handle model nonlinearities while ensuring small computational effort, a controller made by two sequential control actions is developed. The first term is a position-dependent, model predictive control (MPC) with embedded integrator to compute the optimal cable tensions ensuring accurate path tracking and fulfilling the feasibility constraints; bounds on the feasible tensions are also included. The second control term transforms the optimal tensions into the commanded motor torques, and hence currents, that are evaluated through the kinetostatic model of the electric motors used for winding and unwinding the cables. Control design is performed through the robot dynamics model, formulated with the assumption of rigid cables. Moreover, the proposed control strategy is presented in two different architectures, collocated control and noncollocated control. Flexibility is handled by penalizing large tension variations in the cost function adopted in the controller design, plus some hard constraints on the maximum tension derivatives. These features, together with the embedding of the integrator within the MPC formulation, ensure smooth control tensions that allow handling the axial flexibility of the cables, although it is not explicitly considered in the controller design.To assess the performances of the proposed control algorithm, a kinematically-determined robot with a suspended, lumped end-effector is simulated by also adopting very flexible cables. Additionally, a simplified dynamic model of the electrical dynamics and the sensor quantization are included to provide a realistic representation of the real environments. The results, together with the fair comparison with a benchmark, corroborate the effectiveness of the proposed approach, its robustness, and its feasibility in real-time controllers due to the wise reduction of the computational effort.

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Section: State Of the Art And Paper Motivationsmentioning

confidence: 99%

Section: Paper Contributions and Underlying Idea Of The Control Schemementioning

confidence: 99%

Model predictive control for path tracking in cable driven parallel robots with flexible cables: collocated vs. noncollocated control

et al. 2023

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“…Cable driven parallel robots (CDPRs) are gaining an increasing attention in the field of multibody system dynamics due to their benefits in terms of large workspaces and payloads, and small energy requirement. On the other hand, the features of cables set challenges in design [1,2], modeling [3,4], motion planning [5] and control [6,7]. Besides these difficulties, CDPR can be affected by cable failures that severely compromise the robot operation and cause hazardous situations that might result in damages to the system itself and the surroundings.…”

Section: Motivations and State Of The Artmentioning

confidence: 99%

Load observer for cable failure detection in Cable Driven Parallel Robots: a machine learning approach

Bettega,

Piva,

Richiedei

et al. 2023

Preprint

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This paper proposes a method for cable failure detection in Cable Driven Parallel Robots (CDPR) which is based on the exploitation of the load observer (LO) concept together with machine learning algorithms. By just exploiting the dynamic model of each actuator in the conditions of no load, a LO is designed for each motor to estimate the presence of a load coupled through a cable. Since the load instantaneously goes to zero for the motor with a broken cable, a simple but effective and robust signature of failure can be inferred, to provide reliable detection even in the case of various model mismatches. Additionally, the LO is not computationally demanding since just motor measurements are required, thus avoiding any direct measurement (and a dynamic model as well) on the end-effector. The detection of a failure in made through supervised classification algorithms based on artificial intelligence. The training of the machine learning algorithm is based on an “hybrid” approach: the dataset includes several failure cases which are numerically generated through a system digital twin developed through the multibody system theory, together with measurements of the real system in non-failing conditions. Different classification algorithms are considered, together with different sets of input variables to be fed to the classifier. Two numerical examples are proposed, by showing the method capability in handling both fully actuated and redundantly actuated CDPRs under cable failure.

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“…The authors derive kinetic formulations for large-scale deployable cable-driven robots. The model can better satisfy the stability condition [4]. Wang et al proposed a wavelet neural network fuzzy sliding mode controller for the nonlinear problem of the manipulator system.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence in Agricultural Picking Robot Displacement Trajectory Tracking Control Algorithm

Hua-ying

2022

Wireless Communications and Mobile Computing

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With the development and leap of artificial intelligence technology, more and more robots have penetrated into all walks of life. Today’s agriculture is changing in the direction of modernization and automation. On the one hand, because of rising labor costs, it cannot afford to consume a large amount of labor for agricultural operations. On the other hand, the population is growing rapidly, and traditional agricultural production, picking, and other links have been unable to keep pace with the development of the times. Therefore, it is very necessary to use artificial intelligence technology to transform traditional agriculture. The purpose of this paper is to use artificial intelligence technology to plan, track, and optimize the displacement trajectory of the agricultural picking robot, so as to improve the working efficiency of the picking robot. In this paper, the neural network, the D-H modeling method of the manipulator, and the forward and reverse motion of the manipulator are explained and analyzed, and based on the relevant algorithms of neural network, the manipulator is modeled, and then the, forward and reverse motion of the manipulator is analyzed in detail, and the digital model of the picking robot is constructed. Then, the angle and motion speed of each joint of the robot are analyzed to reduce the motion trajectory error caused by friction and other factors. Then, the simulation experiment of the displacement trajectory tracking control is carried out, and the linear trajectory motion and the arc trajectory motion are deeply analyzed, and the axis error is greatly reduced after 6 iterations. Finally, the displacement trajectory is optimized. The optimized total movement time is shortened by 6.84 seconds, which enables the picking robot to not only ensure work efficiency but also accurately complete the planned displacement trajectory. After continuous experiments on the algorithm model and the picking robot, the actual trajectory of the picking robot at 0.7 seconds can be expected. The trajectories are completely coincident, indicating that the neural network plays a very important role in the trajectory research of picking robots.

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Tip-trajectory tracking control of a deployable cable-driven robot via output redefinition

Cited by 14 publications

References 33 publications

Model predictive control for path tracking in cable driven parallel robots with flexible cables: collocated vs. noncollocated control

Model predictive control for path tracking in cable driven parallel robots with flexible cables: collocated vs. noncollocated control

Load observer for cable failure detection in Cable Driven Parallel Robots: a machine learning approach

Artificial Intelligence in Agricultural Picking Robot Displacement Trajectory Tracking Control Algorithm

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