Data-Driven Model-Free Controllers

Precup, Radu; Roman, Raul‐Cristian; Safaei, Ali

doi:10.1201/9781003143444

Cited by 35 publications

(43 citation statements)

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“…The Denavit-Hartenberg methodology can be used to obtain the dynamic model in ( 1) and ( 2) and simplify it to justify the control scheme. As pointed out in [52], x 1 (rad) and x 2 (rad) are the angles that describe the payload position in xz plane, x 3 (m) ∈ [−0.25, 0.25] is the first (controlled) output of the tower crane system, i.e. the cart position, x 4 (rad) ∈ [−1.57, 1.57] is the second (controlled) output of the tower crane system, i.e.…”

Section: Process Modelmentioning

confidence: 79%

“…Using the coordinate systems associated to each element of the crane and the centers of mass introduced in Figure 1 (b), the nonlinear state-space model of the tower crane system is [52]…”

Section: Process Modelmentioning

confidence: 99%

“…Figure 1: The block diagram of the tower crane system laboratory equipment (adapted from [75], [76] and [77]) (a) and coordinate systems associated to each element of the crane and the centers of mass (adapted from [52]) (b).…”

Section: Process Modelmentioning

confidence: 99%

“…The first new contribution of this paper is the performance improvement of control systems for a representative family of Multi Input-Multi Output (MIMO) systems represented by tower crane systems, using the first approach mentioned above, namely and ILC control structure with PD learning functions. The MIMO control system structure makes use of low-cost fuzzy controllers included in the three SISO control loops (for cart position control, arm angular position control and payload position control) subjected to ILC, and the fuzzy controllers are represented by first order discretetime intelligent PI controllers with Takagi-Sugeno-Kang PD fuzzy terms presented in [51] and [52].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

Precup¹,

Roman²,

Hedrea³

et al. 2022

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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This paper is dedicated to the memory of Prof. Ioan Dzitac, one of the fathers of this journal and its founding Editor-in-Chief till 2021. The paper addresses the performance improvement of three Single Input-Single Output (SISO) fuzzy control systems that control separately the positions of interest of tower crane systems, namely the cart position, the arm angular position and the payload position. Three separate low-cost SISO fuzzy controllers are employed in terms of first order discrete-time intelligent Proportional-Integral (PI) controllers with Takagi-Sugeno-Kang Proportional-Derivative (PD) fuzzy terms. Iterative Learning Control (ILC) system structures with PD learning functions are involved in the current iteration SISO ILC structures. Optimization problems are defined in order to tune the parameters of the learning functions. The objective functions are defined as the sums of squared control errors, and they are solved in the iteration domain using the recent metaheuristic Slime Mould Algorithm (SMA). The experimental results prove the performance improvement of the SISO control systems after ten iterations of SMA.

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Section: Process Modelmentioning

confidence: 79%

“…Using the coordinate systems associated to each element of the crane and the centers of mass introduced in Figure 1 (b), the nonlinear state-space model of the tower crane system is [52]…”

Section: Process Modelmentioning

confidence: 99%

Section: Process Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

Precup¹,

Roman²,

Hedrea³

et al. 2022

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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show abstract

“…Zamfirache et al [15] proposed a control method that combines the deep Q-learning (DQL) algorithm and the meta-heuristic gravitational search algorithm (GSA). Precup et al introduced a series of data-driven, model-free controllers [16,17].…”

Section: Introductionmentioning

confidence: 99%

An AUV Target-Tracking Method Combining Imitation Learning and Deep Reinforcement Learning

Mao

Gao

Zhang

et al. 2022

JMSE

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This study aims to solve the problem of sparse reward and local convergence when using a reinforcement learning algorithm as the controller of an AUV. Based on the generative adversarial imitation (GAIL) algorithm combined with a multi-agent, a multi-agent GAIL (MAG) algorithm is proposed. The GAIL enables the AUV to directly learn from expert demonstrations, overcoming the difficulty of slow initial training of the network. Parallel training of multi-agents reduces the high correlation between samples to avoid local convergence. In addition, a reward function is designed to help training. Finally, the results show that in the unity simulation platform test, the proposed algorithm has a strong optimal decision-making ability in the tracking process.

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Fuzzy-based collective pitch control for wind turbine via deep reinforcement learning

Nabeel,

Lasheen,

Elshafei

et al. 2024

ISA Transactions

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Data-Driven Model-Free Controllers

Cited by 35 publications

References 0 publications

Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

An AUV Target-Tracking Method Combining Imitation Learning and Deep Reinforcement Learning

Fuzzy-based collective pitch control for wind turbine via deep reinforcement learning

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