Smart controller for conical tank system using reinforcement learning algorithm

Ramanathan, Prabhu; Mangla, Kamal Kant; Satpathy, Sitanshu

doi:10.1016/j.measurement.2017.11.007

Cited by 28 publications

(9 citation statements)

References 25 publications

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“…It acts in such a way that it tries to maximize cumulative reward over time (Pack Kaelbling et al, 1996). Designing of the smart controller with robustness for the conical tank is discussed, and it states that machine learning technique can improve the performance of conical tank controller as compared to PID and fuzzy-based controller and also it eliminates the need of linearizing the non-linearity associated with a model of system (Ramanathan et al, 2018). Use of reinforcement learning with neural network for mitigating the effect of discretizing is presented by using this technique to one of the process industry application and it shows the impact of selection of discretization level on system performance (Noel and Pandian, 2014).…”

Section: Reinforcement Learningmentioning

confidence: 99%

Development of buck power converter circuit with ANN RL algorithm intended for power industry

Purohit

Manna

Mani

et al. 2020

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Purpose This paper aims to deal with application of artificial intelligence for solving real time control complication adhered with the controlled operation of a buck power converter. This type of converter finds application for power conversion at various levels for the direct current-direct current power industry to step down the input voltage. Design/methodology/approach Use of ANN-RL (Artificial Neural Networks- Reinforcement Learning)-based control algorithm to control buck power converter shows robustness against parameter and load variation. Because of non-linearity instigated by element used for switching, control of this converter becomes an arduous control predicament. All the classical control techniques are based on an approximate linear model of the step down converter and these techniques fail to handle actual non-linearity. Findings In this paper, a reinforcement learning-based algorithm has been used to handle and control buck power converter output voltage, without approximating the model of converter. The non-linearity instigated in converter is subjected to state of switch. Model of buck power converter is defined as a multi-step decision problem so that it can be solved using mathematical model of Markov decision process (MDP) and, in turn, reinforcement learning can be implemented. As MDP model is available for a discrete state system so model of converter has to be discretized and then value iteration is applied and output is analyzed. Load regulation and integral time absolute error analysis is done to show efficacy of this technique. Originality/value To mitigate the effect of discretization function approximation using neural network is applied. MATrix LABoratory has been used for implementation and result indicates an improvement in the overall response.

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Section: Reinforcement Learningmentioning

confidence: 99%

Development of buck power converter circuit with ANN RL algorithm intended for power industry

Purohit

Manna

Mani

et al. 2020

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“…It is one of the classical nonlinear control problem whose objective is to control the level of the tank. This shape not only contributes the proper mixing of liquids and also guarantee to drainage of solid wastes [47]. This type structure widely used in food factories, petroleum industries and chemical industries.…”

Section: Introductionmentioning

confidence: 99%

“…Vijayalakshmi et al [48] developed linear parameter varying model and then controlled using adaptive PI controller. Ramanathan et al [47] presented reinforcement learning technique based on Q-learning algorithm for a conical tank level control. Ravi et al [49] demonstrated regime-based multi-model adaptive control strategy for decoupling-based decentralized PI controller for a conical tank system.…”

Section: Introductionmentioning

confidence: 99%

Optimal tuning of FOPID controller based on PSO algorithm with reference model for a single conical tank system

Rajesh¹

2019

SN Appl. Sci.

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The fractional order proportional integral derivative controller (FOPID) replaces the conventional PID controller for its immense merits such as its simple structure, better set point tracking, high disturbance rejection, higher capability of handling model uncertainties in nonlinear and real time applications. This paper addresses the effectiveness of the FOPID controller on a level control of single conical system in real time. It is one of the classical nonlinear control problem and its shape not only contributes the proper mixing of liquids and also guarantee to drainage of solid wastes. This study, a dual loop controller is proposed and constructed with a master process and slave process for a level control of a single conical tank system. In the slave process, the output response obtained by the traditional PID controller combined with the conical tank reference model and compared with the output response of the master process. The obtained error signal is used to dynamically correct the parameters of the FOPID controller in the master process. The tuning of FOPID controller is a challenging task due to its presence of extra parameters and it efficiently carried out by particle swarm optimization (PSO) algorithm. The obtained result reveals that the Proposed PSO optimized FOPID controller with reference model demonstrated the better set point tracking, smooth controller response than other conventional integer order controllers.

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“…So, several control algorithms have been proposed, implemented and adopted over the past few decades and the quest for new development of CT control still continues. Smart controller based on reinforcement learning algorithm in [1], linear machine inequality (LMI) tuned PI controller in [4], neural network based predictive controller in conjunction with PID controller in [5] which is further extended with simplified additive autoregressive exogenous models in [6], LabVIEW based PID controller in [7], Simulink-PLC based level control in [8], tuned PID controller in [9]. In In the work of [2] the performance of model predictive, MPC,PI and PI-plusfeedforward controllers are compared and the performance of MPC are more acceptable in terms of disturbance handling and time response criteria.…”

Section: Introductionmentioning

confidence: 99%

Model Identification and Level Control of Coupled Tanks System

Okubanjo¹,

Alexander²,

Peter³

et al. 2020

JESR

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The Couple Tank (CT) system remains as a benchmark to investigate and test new emerging control schemes in the process industry since its dynamic emulates many factual system in the field of process control. In this paper, we examine the performance evaluation of two control algorithms, proportional derivative controller (PD) and proportional-integral-derivative controller (PID). The dynamics of the CT system is experimentally derived by system identification method and validated with a mathematical model that depicts the dynamic behaviour of the coupled tank system. Furthermore, the control schemes are expanded on the model obtained through system identification method. The simulation results showed that the PD controller did not meet all the specified control objectives. To improve the response an integral controller was incorporated to the PD controller and the response was compared to that of the PID controller and uncompensated system. The results revealed that the PID controller satisfied all the control goals. However, the PD controller was more satisfactory in terms of time response criteria.

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Smart controller for conical tank system using reinforcement learning algorithm

Cited by 28 publications

References 25 publications

Development of buck power converter circuit with ANN RL algorithm intended for power industry

Development of buck power converter circuit with ANN RL algorithm intended for power industry

Optimal tuning of FOPID controller based on PSO algorithm with reference model for a single conical tank system

Model Identification and Level Control of Coupled Tanks System

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