Model-free adaptive control law for nuclear superheated-steam supply systems

Dong, Zhe; Pan, Yamin; Zhang, Zuoyi; Dong, Yuan; Huang, Xiaojin

doi:10.1016/j.energy.2017.06.033

Cited by 57 publications

(26 citation statements)

References 36 publications

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“…To apply the PMP-based EMS in real time without offline calculation, an MFAC controller was designed to adjust the co-state value λ online. As shown in Figure 17, the MFAC controller mainly consists of a control module and an estimating module, where the currently estimated variables with the control variables are calculated and updated in a discrete-time state [50,51]. The real-time adjustment of the co-state for PMP is treated as a discrete-time nonlinear problem, which can be successfully realized by the MFAC controller.…”

Section: Mfac Controller Designmentioning

confidence: 99%

Integrated Component Optimization and Energy Management for Plug-In Hybrid Electric Buses

Liu

Zhao

et al. 2019

Processes

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The complicated coupling of component design together with energy management has brought a significant challenge to the design, optimization, and control of plug-in hybrid electric buses (PHEBs). This paper proposes an integrated optimization methodology to ensure the optimum performance of a PHEB with a view toward designing and applications. First, a novel co-optimization method is proposed for redesigning the driveline parameters offline, which combines a nondominated sorting genetic algorithm-II (NSGA-II) with dynamic programming to eliminate the impact of the coupling between the component design and energy management. Within the new method, the driveline parameters are optimally designed based on a global optimal energy management strategy, and fuel consumption and acceleration time can be respectively reduced by 4.71% and 4.59%. Second, a model-free adaptive control (MFAC) method is employed to realize the online optimal control of energy management on the basis of Pontryagin's minimum principle (PMP). Particularly, an MFAC controller is used to track the predesigned linear state-of-charge (SOC), and its control variable is regarded as the co-state of the PMP. The main finding is that the co-state generated by the MFAC controller gradually converges on the optimal one derived according to the prior known driving cycles. This implies that the MFAC controller can realize a real-time application of the PMP strategy without acquiring the optimal co-state by offline calculation. Finally, the verification results demonstrated that the proposed MFAC-based method is applicable to both the typical and unknown stochastic driving cycles, meanwhile, and can further improve fuel economy compared to a conventional proportional-integral-differential (PID) controller.Processes 2019, 7, 477 2 of 23 on a single-objective (or multiobjective) optimization problem of driveline matching, component sizing, topology design, or the EMS [8][9][10][11]. For the predefined single-shaft coaxial parallel plug-in hybrid electric bus (PHEB) in our research, component sizing and the energy management control are extremely significant to fuel economy. To guarantee the optimality of dynamic performance and fuel economy, the driveline design and the EMS should be simultaneously considered, as they are strongly coupled [12]. Previous work has disposed of the combined optimization problem in a bilevel manner, where the outer loop is for the former and the inner loop is for the latter [8,13]. Many optimization algorithms have also been extensively applied to solve this problem, such as a genetic algorithm (GA) [4,14], particle swarm optimization (PSO) [11,15], and simulated annealing (SA) [9,16]. Most of them have been utilized to optimally design the component parameters for an outer loop, while a rule-based EMS is nested in an inner loop. However, the optimization results were suboptimal and influenced by the established rules due to the coupling relationship between the component design and EMS [3,7]. To overcome this drawback, another categ...

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Section: Mfac Controller Designmentioning

confidence: 99%

Integrated Component Optimization and Energy Management for Plug-In Hybrid Electric Buses

Liu

Zhao

et al. 2019

Processes

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“…Theorem 1. If (2) satisfies Assumptions 1-3, the MFASMC-PFDL algorithm (14) can render e(k) asymptotically stable and x 1 (k) will be bounded. Proof: In order to prove the stability of the MFASMC-PFDL algorithm, an estimate of the boundedness of the PG has been given in the literature [30].…”

Section: Model-free Adaptive Sliding Mode Controllermentioning

confidence: 99%

“…The aforementioned approaches require the use of the CSTR dynamic model in the controller design which may be difficult to obtain accurately in practice. Model-free adaptive control [13][14][15][16][17] (MFAC) is a method that does not requires any information on the mathematical model. It has been successfully applied to control problems in the fields of oil refining, chemical, electrical, light industry and urban road systems.…”

Section: Introductionmentioning

confidence: 99%

Model-free Adaptive Sliding Mode Control for Continuous Stirred Tank Reactor

Feng

Cao

et al. 2018

2018 37th Chinese Control Conference (CCC)

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The continuous stirred tank reactor (CSTR) is representative of a typical class of chemical equipment, where the dynamics is strongly nonlinear. Two difficult issues in control of a CSTR are the difficulty of accurate modeling and the suppression of external disturbances. Driven by these challenging issues and demanding expectations around performance levels, this paper proposes a control solution based on the model-free data-driven linearization method and sliding mode control. After giving the corresponding stability proof, the effectiveness of the method is validated by MATLAB simulation. Experimental results are also presented to further test the proposed method.

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“…Some promising nonlinear control (NC) methods have been developed, e.g., the sliding mode control introduced by Shtessel, Huang, Qaiser and Ansarifar [4][5][6][7], the physics-based techniques given by Dong [8,9], and the feedback linearization method with disturbance observer proposed by Eom [10]. The previous works [4][5][6][7][8][9][10] show satisfactory results in enhancing closed-loop stability and robustness for NPPs. However, since most reported works are only focused on stability issues of NPPs operation, the operational efficiency needs to be further optimized.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Matrix Control for the Thermal Power of MHTGR-Based Nuclear Steam Supply System

et al. 2018

Self Cite

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The modular high temperature gas-cooled reactor (MHTGR) based nuclear steam supplying system (NSSS) is constituted by an MHTGR, a once-through steam generator (OTSG) and can generate superheated steam for industrial heat or electric power generation. The wide range closed-loop stability is achieved by the recently proposed coordinated control law, in which the neutron flux and the temperatures of both main steam and primary coolant are chosen as controlled variables, and the flowrates of both primary and secondary loop and the control rod speed are chosen as manipulated variables. However, the thermal power is only controlled in open loop manner and hence could be further optimized through feedback. Motivated by this, a dynamic matrix control (DMC) is proposed for optimizing the thermal power of MHTGR based NSSS. A simple step-response model with the thermal power response data is utilized in designing the DMC. The design objective of DMC is to optimize the deviation of the thermal power from its reference under its rate constraint. Then, by the virtue of strong stability of existing control law and optimization ability of DMC, a cascade control structure is implemented for the thermal power optimization, with the coordinated control law in the inner loop and DMC in the outer loop. Numerical simulation results show the satisfactory improvement of thermal power response. This cascade control structure inherits the advantages of both proportional-integral-differential (PID) control and DMC, by which the zeros offset and the short settling time of thermal power are realized.

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Model-free adaptive control law for nuclear superheated-steam supply systems

Cited by 57 publications

References 36 publications

Integrated Component Optimization and Energy Management for Plug-In Hybrid Electric Buses

Integrated Component Optimization and Energy Management for Plug-In Hybrid Electric Buses

Model-free Adaptive Sliding Mode Control for Continuous Stirred Tank Reactor

Dynamic Matrix Control for the Thermal Power of MHTGR-Based Nuclear Steam Supply System

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