A Non-Linear Continuous-Time Generalized Predictive Control for a Planar Cable-Driven Parallel Robot

Inel, Fouad; Medjbouri, Ali; Carbone, Giuseppe

doi:10.3390/act10050097

Cited by 6 publications

(4 citation statements)

References 38 publications

(45 reference statements)

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“…Aghaseyedabdollah et al [48] discussed the design of supervisory adaptive fuzzy sliding mode control with the fuzzy PID sliding surface for a planar cable-driven parallel robot. Inel et al [49] addressed a nonlinear continuous-time generalized predictive control for a planar cable-driven parallel robot.…”

Section: Pick-and-place Trajectory Tracking Controlmentioning

confidence: 99%

Pick–and–Place Trajectory Planning and Robust Adaptive Fuzzy Tracking Control for Cable–Based Gangue–Sorting Robots with Model Uncertainties and External Disturbances

et al. 2022

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A suspended cable−based parallel robot (CBPR) composed of four cables and an end−grab is employed in a pick−and−place operation of moving target gangues (MTGs) with different shapes, sizes, and masses. This paper focuses on two special problems of pick−and−place trajectory planning and trajectory tracking control of the cable−based gangue−sorting robot in the operation space. First, the kinematic and dynamic models for the cable−based gangue−sorting robots are presented in the presence of model uncertainties and unknown external disturbances. Second, to improve the sorting accuracy and efficiency of sorting system with cable−based gangue−sorting robot, a four-phase pick−and−place trajectory planning scheme based on S-shaped acceleration/deceleration algorithm and quintic polynomial trajectory planning method is proposed, and moreover, a robust adaptive fuzzy tracking control strategy is presented against inevitable uncertainties and unknown external disturbances for trajectory tracking control of the cable−based gangue−sorting robot, where the stability of a closed-loop control scheme is proved with Lyapunov stability theory. Finally, the performances of pick−and−place trajectory planning scheme and robust adaptive tracking control strategy are evaluated through different numerical simulations within Matlab software. The simulation results show smoothness and continuity of pick−and−place trajectory for the end−grab as well as the effectiveness and efficiency to guarantee a stable and accurate pick−and−place trajectory tracking process even in the presence of various uncertainties and external disturbances. The pick−and−place trajectory generation scheme and robust adaptive tracking control strategy proposed in this paper lay the foundation for accurate sorting of MTGs with the robot.

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Section: Pick-and-place Trajectory Tracking Controlmentioning

confidence: 99%

Pick–and–Place Trajectory Planning and Robust Adaptive Fuzzy Tracking Control for Cable–Based Gangue–Sorting Robots with Model Uncertainties and External Disturbances

et al. 2022

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“…Recently, control algorithms based on or incorporating CTC have been used in a wide variety of applied engineering research areas, such as motion control of miscellaneous robot manipulators with open and closed (or parallel) kinematic chains [10,11] and cabledriven robots [12]; overhead crane payload sway control [13]; attitude control of drone-like multi-rotor aircraft [14]; operation of a musculoskeletal therapy device with artificial muscles [15]; gait planning for bipedal robots [16]. However, in almost all cases, separate integral, proportional, and derivative gains or parameters are used to "tune" the controlled system for the optimal trajectory tracking, which makes finding the ideal parameter set a complicated task.…”

Section: Approximate Modelmentioning

confidence: 99%

Experimental and Simulation-Based Performance Analysis of a Computed Torque Control (CTC) Method Running on a Double Rotor Aeromechanical Testbed

et al. 2021

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Concept of closed loop control appears in many fields of engineering sciences, where the output quantity of some physical system must be forced to follow some prescribed function over time, e.g., when a robotic arm endpoint must track a desired trajectory or path given as timed series of spatial coordinates. The classic approach for solving this kind of problem involves a PID compensation block, and the necessary input signal for keeping the controlled process in the vicinity of the desired trajectory is calculated as the weighted sum of momentary deviation, deviation integral, and deviation derivative relative to the reference path. However, despite the obvious advantages, practical usability, and simplicity of the PID controllers, their performance is limited when they are utilized for controlling nonlinear systems. Even with linear systems, their proper operation requires an accurate system model and precise tuning process for finding the best weight values for the proportional, integral, and derivative effects, and the planned closed loop behavior might change significantly as the parameters of the controlled plant change over time. In this article, a computed torque-based controller is presented, which has only one adjustable parameter ensuring precise trajectory tracking even with significantly alternated model constants. The practical usability of the offered algorithm is evaluated and verified by simulations and experiments performed on a simple mechanical bi-rotor testbed playing the role of controlled plant.

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“…Model predictive control (MPC) is an advanced control method based on time-domain optimization techniques in presence of constraints (Han et al, 2018; Inel et al, 2021; Li et al, 2021; Tian and Wang, 2022). Recently, along with the development of computer technology and the availability of models of many engineering subjects, the construction and implementation of MPCs in hardware systems is not a challenging problem anymore.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, the feedback loop is added to the control compensator, and the error between the actual control signal and the controller output is predicted to compensate for the time-delay in the output channel, leading to a significant improvement in the system’s performance. A novel nonlinear algorithm is developed by Inel et al (2021) for the trajectory tracking of a planar cable-driven parallel robot based on the finite horizon continuous-time minimization of a quadratic predicted cost function. The main idea of MPC is that, at each sampling instant, it takes the current output measurements, inputs, dynamic states, and mathematical model of the plant to predict the future system states and calculate a consequence of the control signal over a finite horizon by optimizing an objective function subject to some constraints, and then apply only the first element in the sequence to control the system.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy Lyapunov-based model predictive control for parallel platform driving simulators

Mạnh

Nguyen

Duy

et al. 2022

Transactions of the Institute of Measurement and Control

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The paper proposes an adaptive Lyapunov-based nonlinear model predictive control (MPC) to cope with the problems in nonlinear systems subjecting to system constraints and unknown disturbances of the parallel car driving simulator. Commonly, standard nonlinear controllers could guarantee the overall system stability for the parallel structure. However, the constraints tend to impact the control performance and stability adversely. Therefore, MPC plays a vital role in the proposed technique to explicitly consider all the practical constraints and simultaneously enhance the system’s robustness. Nevertheless, the accuracy of the modeling process has a significant effect on the MPC performance, and thus, the convergence cannot be guaranteed in the presence of the model uncertainties. To overcome this problem, by the merit of the fuzzy adaptive law, the control system takes the disturbances and unmodelled parameters into account. Moreover, the feasibility and stability of the approach, which is the fundamental problem of MPC, are ensured according to the Lyapunov-based nonlinear controller, backstepping aggregated with sliding mode control (SMC), and hence inherit advantages of these controls. Simulation results show the efficiency and superior constituted controllers of the proposed method.

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A Non-Linear Continuous-Time Generalized Predictive Control for a Planar Cable-Driven Parallel Robot

Cited by 6 publications

References 38 publications

Pick–and–Place Trajectory Planning and Robust Adaptive Fuzzy Tracking Control for Cable–Based Gangue–Sorting Robots with Model Uncertainties and External Disturbances

Pick–and–Place Trajectory Planning and Robust Adaptive Fuzzy Tracking Control for Cable–Based Gangue–Sorting Robots with Model Uncertainties and External Disturbances

Experimental and Simulation-Based Performance Analysis of a Computed Torque Control (CTC) Method Running on a Double Rotor Aeromechanical Testbed

Adaptive fuzzy Lyapunov-based model predictive control for parallel platform driving simulators

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