An adaptive chaos particle swarm optimization for tuning parameters of <scp>PID</scp> controller

Nie, Shan-Kun; Wang, Yujia; Xiao, Shanli; Liu, Zhifeng

doi:10.1002/oca.2314

Cited by 16 publications

(13 citation statements)

References 38 publications

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“…To our knowledge, the PSO algorithm has been applied to reservoir optimization for the first time since 2002, and a larger number of improvements in the PSO algorithm to adapt the optimal operation of reservoirs have emerged. Some researchers improve the efficiency of particle search ability by setting the initial feasible region and optimizing it near the feasible region [43,44]. Although this method improves the efficiency of particles, the selection of the initial feasible region has a greater impact on the results.…”

Section: Discussionmentioning

confidence: 99%

Application of Strongly Constrained Space Particle Swarm Optimization to Optimal Operation of a Reservoir System

Wang

et al. 2018

Sustainability

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In view of the low efficiency of the particle swarm algorithm under multiple constraints of reservoir optimal operation, this paper introduces a particle swarm algorithm based on strongly constrained space. In the process of particle optimization, the algorithm eliminates the infeasible region that violates the water balance in order to reduce the influence of the unfeasible region on the particle evolution. In order to verify the effectiveness of the algorithm, it is applied to the calculation of reservoir optimal operation. Finally, this method is compared with the calculation results of the dynamic programming (DP) and particle swarm optimization (PSO) algorithm. The results show that: (1) the average computational time of strongly constrained particle swarm optimization (SCPSO) can be thought of as the same as the PSO algorithm and lesser than the DP algorithm under similar optimal value; and (2) the SCPSO algorithm has good performance in terms of finding near-optimal solutions, computational efficiency, and stability of optimization results. SCPSO not only improves the efficiency of particle evolution, but also avoids excessive improvement and affects the computational efficiency of the algorithm, which provides a convenient way for particle swarm optimization in reservoir optimal operation.

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Section: Discussionmentioning

confidence: 99%

Application of Strongly Constrained Space Particle Swarm Optimization to Optimal Operation of a Reservoir System

Wang

et al. 2018

Sustainability

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“…In order to solve the problem, Patel [24] appended the immigration mechanism, 10% of the elite population and the inferior population were selected as the variant population, to the neural network adaptive PID controller (MN-PID). In addition, Nie [25] presented an adaptive chaos particles swarm optimization for tuning parameters of PID controller (CSP-PID) to avoid the local minima.…”

Section: Adaptive Controller Based On Neural Networkmentioning

confidence: 99%

Design and application of adaptive PID controller based on asynchronous advantage actor–critic learning method

Sun

Duan

et al. 2019

Wireless Netw

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To address the problems of the slow convergence and inefficiency in the existing adaptive PID controllers, we propose a new adaptive PID controller using the asynchronous advantage actor-critic (A3C) algorithm. Firstly, the controller can train the multiple agents of the actor-critic structures in parallel exploiting the multi-thread asynchronous learning characteristics of the A3C structure. Secondly, in order to achieve the best control effect, each agent uses a multilayer neural network to approach the strategy function and value function to search the best parameter-tuning strategy in continuous action space. The simulation results indicate that our proposed controller can achieve the fast convergence and strong adaptability compared with conventional controllers.

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“…• Apply the FDI and FCT tests (9), (10) at each time step to detect the fault. • After detecting the fault, identify the fault using the GLR approach (11)- (14), and (15). • Using (16) or (17), compensate the fault according to the type of fault that has occurred.…”

Section: Step 2 Fault-tolerant Schemementioning

confidence: 99%

“…10,11 The main reasons for popularity of the PID controllers are their flexibility and simplicity in practical implementations. This popularity has encouraged the formulation of a large number of methods for tuning the PID controllers (see, eg, other works [12][13][14][15][16][17] ).…”

Section: Introductionmentioning

confidence: 99%

Multiobjective fault‐tolerant fixed‐order/PID control of multivariable discrete‐time linear systems with unmeasured disturbances

Karimi

Batmani

Khosrowjerdi

2018

Optim Control Appl Methods

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In this paper, a multiobjective fault-tolerant fixed-order output feedback controller design technique is proposed for multivariable discrete-time linear systems with unmeasured disturbances. Initially, a multiobjective fixed-order controller is designed for the system by transforming the problem of tuning the parameters of the controller into a static output feedback problem and solving a mixed H 2 ∕H ∞ optimization problem with bilinear matrix inequalities.Subsequently, the fixed-order controller is used to construct the closed-loop system and an active fault-tolerant control scheme is applied using the input/output data collected from the controlled system. Motivated by its popularity in industry, the proposed method is also used to tune the parameters of proportional-integral-derivative controllers as a special case of structured controllers with the fixed order. Two numerical simulations are provided to demonstrate the design procedure and the flexibility of the proposed technique.

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An adaptive chaos particle swarm optimization for tuning parameters of PID controller

Cited by 16 publications

References 38 publications

Application of Strongly Constrained Space Particle Swarm Optimization to Optimal Operation of a Reservoir System

Application of Strongly Constrained Space Particle Swarm Optimization to Optimal Operation of a Reservoir System

Design and application of adaptive PID controller based on asynchronous advantage actor–critic learning method

Multiobjective fault‐tolerant fixed‐order/PID control of multivariable discrete‐time linear systems with unmeasured disturbances

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