Data-Driven Robust Approximate Optimal Tracking Control for Unknown General Nonlinear Systems Using Adaptive Dynamic Programming Method

Zhang, Huaguang; Cui, Lili; Zhang, Xin; Luo, Yanhong

doi:10.1109/tnn.2011.2168538

Cited by 567 publications

(46 citation statements)

References 40 publications

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“…Recently, data-driven approaches have been widely used in the control field to realize a variety of data-based linear and nonlinear systems, for prediction, evaluation, scheduling, monitoring, diagnosis, decision-making, and optimization [38,39,40,41,42,43,44,45,46]. ADP is a typical data-driven approach for control over an infinite horizon, which can avoid the “curse of dimensionality” problem and the reverse solving problem existing in DP [47]. Up to now, ADP has been applied to nonlinear zero-/nonzero-sum differential games [48,49], optimal tracking control problems [50], optimal control of intelligent grid [51,52], and optimal time slot scheduling of MAC protocol [53].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Dynamic Programming-Based Multi-Sensor Scheduling for Collaborative Target Tracking in Energy Harvesting Wireless Sensor Networks

Liu

Xiao

Shuai

et al. 2018

Sensors

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Collaborative target tracking is one of the most important applications of wireless sensor networks (WSNs), in which the network must rely on sensor scheduling to balance the tracking accuracy and energy consumption, due to the limited network resources for sensing, communication, and computation. With the recent development of energy acquisition technologies, the building of WSNs based on energy harvesting has become possible to overcome the limitation of battery energy in WSNs, where theoretically the lifetime of the network could be extended to infinite. However, energy-harvesting WSNs pose new technical challenges for collaborative target tracking on how to schedule sensors over the infinite horizon under the restriction on limited sensor energy harvesting capabilities. In this paper, we propose a novel adaptive dynamic programming (ADP)-based multi-sensor scheduling algorithm (ADP-MSS) for collaborative target tracking for energy-harvesting WSNs. ADP-MSS can schedule multiple sensors for each time step over an infinite horizon to achieve high tracking accuracy, based on the extended Kalman filter (EKF) for target state prediction and estimation. Theoretical analysis shows the optimality of ADP-MSS, and simulation results demonstrate its superior tracking accuracy compared with an ADP-based single-sensor scheduling scheme and a simulated-annealing based multi-sensor scheduling scheme.

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Section: Related Workmentioning

confidence: 99%

Adaptive Dynamic Programming-Based Multi-Sensor Scheduling for Collaborative Target Tracking in Energy Harvesting Wireless Sensor Networks

Liu

Xiao

Shuai

et al. 2018

Sensors

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“…Recently, to overcome the measurement problems of critical variables in complex system, some soft-sensor method, based on neural network technology, was used [10,20,29]. Especially, the feed-forward neural network (FNN) is widely used for soft-sensor modeling in complex nonlinear dynamic system because its effectiveness and simplicity [8,11,30]. In [18], the identifier and controller based FNN was designed to control WWTP.…”

Section: Eq Soft-sensor Modulementioning

confidence: 99%

A Hybrid Intelligent Optimal Control System Design for Wastewater Treatment Process

Qiao¹,

Han²,

Han³

et al. 2017

ITC

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Due to the characteristics of large lag and high nonlinearity, the optimizing operation of wastewater treatment process (WWTP) is difficult to be designed. To solve this problem, a control optimization system based on hybrid intelligent technology is proposed in this paper. This system includes a feed-forward compensator, a feedback supervision module, a pre-setting module and a soft-sensor module. To obtain the minimum energy consumption (EC) under effluent standards, the set-points of the dissolved oxygen concentration and nitrate nitrogen concentration are adjusted through feed-forward compensation, feedback correction and online estimation. Finally, the proposed approach is applied on the WWTP simulation model. Compared to the proportional-integral-derivative (PID) and data-driven adaptive optimal controller (DDAOC) methods, simulation results of the method proposed in this paper show better performance.

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“…One inconvenience is how long this optimal convergence will take to occur. A control for a recurrent NN, described in [13], was an optimum by adding an extra coefficient to compensate for the error within a small bound in an unknown necessary learning time.…”

Section: Optimum Coefficientmentioning

confidence: 99%

Neural Net Gains Estimation Based on an Equivalent Model

Cruz

Juárez

Muñoz

et al. 2016

Computational Intelligence and Neuroscience

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A model of an Equivalent Artificial Neural Net (EANN) describes the gains set, viewed as parameters in a layer, and this consideration is a reproducible process, applicable to a neuron in a neural net (NN). The EANN helps to estimate the NN gains or parameters, so we propose two methods to determine them. The first considers a fuzzy inference combined with the traditional Kalman filter, obtaining the equivalent model and estimating in a fuzzy sense the gains matrix A and the proper gain K into the traditional filter identification. The second develops a direct estimation in state space, describing an EANN using the expected value and the recursive description of the gains estimation. Finally, a comparison of both descriptions is performed; highlighting the analytical method describes the neural net coefficients in a direct form, whereas the other technique requires selecting into the Knowledge Base (KB) the factors based on the functional error and the reference signal built with the past information of the system.

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Data-Driven Robust Approximate Optimal Tracking Control for Unknown General Nonlinear Systems Using Adaptive Dynamic Programming Method

Cited by 567 publications

References 40 publications

Adaptive Dynamic Programming-Based Multi-Sensor Scheduling for Collaborative Target Tracking in Energy Harvesting Wireless Sensor Networks

Adaptive Dynamic Programming-Based Multi-Sensor Scheduling for Collaborative Target Tracking in Energy Harvesting Wireless Sensor Networks

A Hybrid Intelligent Optimal Control System Design for Wastewater Treatment Process

Neural Net Gains Estimation Based on an Equivalent Model

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