Neuro-genetic PID autotuning: time invariant case

Lima, João M. G.; Ruano, A. E.

doi:10.1016/s0378-4754(99)00124-x

Cited by 12 publications

(10 citation statements)

References 4 publications

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“…The control architecture was detailed in [29], where only continuous time was considered, for convenience purposes such architecture is repeated (Figure 1).…”

Section: Pid Controller Tuning Problemmentioning

confidence: 99%

“…The PID auto-tuning consists of evaluating and delivering online accurate PID parameters optimized for control criteria. As shown in Figure 1, it should be considered a simple control loop (the block control system), in connection with the tuning block composed of the modeling and optimizer blocks, as explained in [29].…”

Section: Pid Controller Tuning Problemmentioning

confidence: 99%

“…, (Ceil round towards plus infinity). Thus, m is a fractional part of T, it can be evaluated after knowing l, λ and T. Finally, we can deduct [29] from (2):…”

Section: Z Means That the Laplace Transform (Expression In Variable S)mentioning

confidence: 99%

“…and production planning problems in [17] and [18]. The performance of MPGA with individuals hierarchically structured in trees was also compared against other optimization methods in [29]. The authors report superior results using MPGA when finding solutions for benchmark multi-modal functions.…”

Section: Introduction and Related Workmentioning

confidence: 99%

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Implementation on <i>FPGA</i> of Neuro-Genetic PID Controllers Auto-Tuning

Rosa¹,

Arantes²,

Toledo³

et al. 2022

IIM

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The present paper studies the use of genetic algorithm to optimize the tuning of the Proportional, Integral and Derivative (PID) controller. Two control criteria were considered, the integral of the time multiplied by the absolute error (ITAE), and the integral of the time multiplied by the absolute output (ITAY). The time variant plant tested is a first-order plant with time delay. We aim at a real time implementation inside a digital board, so, the previous continuous approach was discretized and tested; the corresponding control algorithm is presented in this paper. The genetic algorithms and the PID controller are executed using the soft processor NIOS II in the Field Programmable Gate Array (FPGA). The computational results show the robustness and versatility of this technology.

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“…The control architecture was detailed in [29], where only continuous time was considered, for convenience purposes such architecture is repeated (Figure 1).…”

Section: Pid Controller Tuning Problemmentioning

confidence: 99%

Section: Pid Controller Tuning Problemmentioning

confidence: 99%

“…, (Ceil round towards plus infinity). Thus, m is a fractional part of T, it can be evaluated after knowing l, λ and T. Finally, we can deduct [29] from (2):…”

Section: Z Means That the Laplace Transform (Expression In Variable S)mentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Implementation on <i>FPGA</i> of Neuro-Genetic PID Controllers Auto-Tuning

Rosa¹,

Arantes²,

Toledo³

et al. 2022

IIM

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“…A hybrid Neuro-Genetic approach to the problem of self-tuning of the PID controller is presented in [15]. In this solution the controlled systems are identified by the Neural Auto-tuner returning appropriately scaled PID controller gains.…”

Section: Related Workmentioning

confidence: 99%

Self-tuning run-time reconfigurable PID controller

Pelc¹

2011

Archives of Control Sciences

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Self-tuning run-time reconfigurable PID controller MARIUSZ PELC Digital PID control algorithm is one of the most commonly used algorithms in the control systems area. This algorithm is very well known, it is simple, easily implementable in the computer control systems and most of all its operation is very predictable. Thus PID control has got well known impact on the control system behavior. However, in its simple form the controller have no reconfiguration support. In a case of the controlled system substantial changes (or the whole control environment, in the wider aspect, for example if the disturbances characteristics would change) it is not possible to make the PID controller robust enough. In this paper a new structure of digital PID controller is proposed, where the policy-based computing is used to equip the controller with the ability to adjust it's behavior according to the environmental changes. Application to the electro-oil evaporator which is a part of distillation installation is used to show the new controller structure in operation.

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