Auto-Tuning PID Distributed Power Control for Next-Generation Passive Optical Networks

Santos, Layhon Roberto Rodrigues dos; Durand, Fábio Renan; Goedtel, Alessandro; Abrão, Taufik

doi:10.1364/jocn.10.00d110

Cited by 15 publications

(11 citation statements)

References 33 publications

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“…Generally, the structure of a PID controller combines three control parameters, i.e., K P , K I and K D . Each parameter affects a different response of the system: K P reduces the error, K I increases the speed of response and reduces the error when the operation of the system is changed but overshoot occurs, and K D reduces the overshoot from K I and improves the stability of the system [7], [26]. The common closed-loop diagram of autotuning the PID controller is shown in Figure 1.…”

Section: Pid Controller and Objective Designmentioning

confidence: 99%

“…Nowadays, PID controllers are widely applied and represent the most preferred choice of controller in many applications, such as power plants, industrial and mechanical systems, robotics [1]- [4], wind turbines [5], passive optical networks [6], [7], load frequency control (LFC) systems [8], [9], hydraulic turbine regulation systems [3], and radial active magnetic bearings [4], because they provide excellent performance, reliability, and robustness and are characterized by flexibility, low cost,a simple structure and ease of design [3], [10]- [13]. To obtain good closed-loop performance of PID controllers, appropriately adjusting three…”

Section: Introductionmentioning

confidence: 99%

“…These methods adjust the parameter based on the experience of the designer and require time for tuning. In practice, the system control is dynamic, and these approaches are ineffective for high order system modeling [7], [15], [16]. Recently, optimal tuning of the PID parameter by applying artificial intelligence (AI) has been implemented.…”

Section: Introductionmentioning

confidence: 99%

“…At present, many AI methods have been proposed to autotune the PID parameter, such as fuzzy logic [2], [10], [19], [20], neural networks (NNs) [1], [7], [21], particle swarm optimization (PSO) algorithms [6], [22], [23], hybrid firefly (FA) and pattern search [8], the ant lion optimization (ALO) algorithm [24], the whale optimization algorithm (WOA) [25], cuckoo search (CS) [10], [26], bacterial foraging optimization [27], genetic algorithms [28], the cosine algorithm [29], the bat algorithm [12], ant colony optimization (ACO) [13], [30], differential evolution (DE) [31], World Cup optimization (WCO) [32], evaluation algorithms (EAs) [33], [34], gray wolf optimization (GWO) [35], nature-inspired algorithms [17], chaotic invasive weed optimization [36], [37], flower pollination algorithm (FPA) [38] and firefly algorithm (FFA) [39]. Although many AI methods have been proposed to autotune the PID parameter, the challenges of long execution time and convergence persist.…”

Section: Introductionmentioning

confidence: 99%

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PID Controller Autotuning Design by a Deterministic Q-SLP Algorithm

Pongfai

Zhang

et al. 2020

IEEE Access

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The proportional integral and derivative (PID) controller is extensively applied in many applications. However, three parameters must be properly adjusted to ensure effective performance of the control system: the proportional gain (K P), integral gain (K I) and derivative gain (K D). Therefore, the aim of this paper is to optimize and improve the stability, convergence and performance in autotuning the PID parameter by using a deterministic Q-SLP algorithm. The proposed method is a combination of the swarm learning process (SLP) algorithm and Q-learning algorithm. The Q-learning algorithm is applied to optimize the weight updating of the SLP algorithm based on the new deterministic rule and closed-loop stabilization of the learning rate. To validate the global optimization of the deterministic rule, it is proven based on the Bellman equation, and the stability of the learning process is proven with respect to the Lyapunov stability theorem. Additionally, to demonstrate the superiority of the performance and convergence in autotuning the PID parameter, simulation results of the proposed method are compared with those based on the central position control (CPC) system using the traditional SLP algorithm, the whale optimization algorithm (WOA) and improved particle swarm optimization (IPSO). The comparison shows that the proposed method can provide results superior to those of the other algorithms with respect to both performance indices and convergence. INDEX TERMS Autotuning gain, central position control system, Q-learning algorithm, PID controller, swarm learning process algorithm, optimal control.

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Section: Pid Controller and Objective Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PID Controller Autotuning Design by a Deterministic Q-SLP Algorithm

Pongfai

Zhang

et al. 2020

IEEE Access

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“…In [22,23], it is pointed out that the variation in operating characteristics not only affects the performance of the existing control system, but also endangers the safety and stability of the unit in the process of operation. In [24][25][26][27], it is pointed out that the control strategy of each control loop in the power plant generally adopts the proportional integral (PI) control structure. The main reason is that the PI control strategy has strong robustness, and it is easy to adjust parameters.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Performance Assessment of Primary Frequency Modulation for a Power Control System Based on MATLAB

Wang

2018

Processes

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The primary frequency modulation (PFM) performance of a power control system (PCS) is an important factor affecting the security and stability of a power grid. The traditional control method is proportional integral (PI) control. In order to improve its dynamic control performance, a control method based on the combination of internal model control (IMC) and PI is proposed. Using the method of theoretical assessment and system identification, a simple simulated model of the typical PCS is established. According to the principle of system identification and the least square estimation (LSE) algorithm, the mathematical models of a generator and a built-in model are established. According to the four dynamic performance indexes, the main and auxiliary assessment index of the PCS are defined, and the benchmark and the result of the performance assessment are given. According to three different structures, the PFM dynamic performance of the PCS is analyzed separately. According to the dynamic performance assessment index of PFM, the structure of the control system and the influence of different parameters on the performance of the PCS are analyzed under ideal conditions. The appropriate control structure and controller parameters are determined. Secondly, under the non-ideal condition, the influence of the actual valve flow coefficient on the performance of the control system is studied under two different valve control modes. The simulation results show that the internal model combined with PI has better dynamic control performance and stronger robustness than the traditional PI control, and it also has better application prospects for thermal power plants.

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