RBF-based adaptive sliding mode controller with extended state observer for load following of nuclear power plant

Hui, Jiuwu; Yuan, Jingqi

doi:10.1016/j.nucengdes.2019.110465

Cited by 23 publications

(8 citation statements)

References 26 publications

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“…In addition, compared with the traditional sliding mode control method proposed in reference [17], this paper introduces the idea of model prediction into the design of sliding mode controller, which provides ideas for more new sliding mode control methods. Compared with the sliding mode control method proposed in reference [13], the PSMC method proposed in this paper has weaker chattering in control input, which improves the practicability of the controller.…”

Section: Discussionmentioning

confidence: 96%

“…The initial outlet temperature of reactor is set as T o0 =300℃, the average fuel temperature is set as T fl0 =650℃ (the initial temperature setting is only required for simulation). The value of physical parameters in the reactor model is found in reference [13]. The simulation time is set to 500s, and the model is built in the MATLAB/Simulink platform, as shown in Figure 3.…”

Section: Simulation Analysis Of Psmcmentioning

confidence: 99%

“…Aiming at the power control problem of advanced boiling-water reactor, Huang et al [12] designed a multiple-input multiple-output fuzzy adaptive recursive sliding mode controller (FARSMC), which eliminates the chattering phenomenon of the sliding mode controller and realizes the parameter adaptation by using fuzzy logic. Hui and Yuan [13] designed a second-order sliding mode controller for reactor power based on state observer, and used neural network to approximate some state variables in the reactor model, which reduced the chattering effect of the controller; Wang et al [14] established the reactor model based on T-S fuzzy theory, and used the continuous switching function as the sliding mode function to reduce the chattering of the controller.…”

Section: Introductionmentioning

confidence: 99%

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Nuclear power predictive sliding mode control based on state observer

Tan¹,

Zhou²

2023

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023)

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Nuclear reactor is the energy core of nuclear power plant. In order to ensure the efficient and stable operation of nuclear power plant and continuously output the thermal power generated by nuclear fission, it is necessary to control the power of reactor under operating conditions. The nonlinear mathematical model of nuclear reactor core power is established by perturbation method for point-reactor kinetics equation, and a reduced-order Luenberger state observer is designed to observe and track the state variables that cannot be measured directly in the model. On this basis, a sliding mode control algorithm based on model prediction is designed to realize the state feedback control of reactor power. Compared with the traditional PID control algorithm and verified by simulation with MATLAB/Simulink software. The simulation results show that in the up and down stage of load power, the speed fluctuation of control rod of sliding mode controller (0.5-mm/s) is higher than that of PID controller (0.07-mm/s), but in the steady-state conditions, the relative power tracking error of sliding mode controller (4×10-4) is less than that of PID controller (3×10-3). The predictive sliding mode controller designed in this paper has better comprehensive performance than PID controller.

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Section: Discussionmentioning

confidence: 96%

Section: Simulation Analysis Of Psmcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nuclear power predictive sliding mode control based on state observer

Tan¹,

Zhou²

2023

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023)

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“…This method could suppress buffeting effectively and achieve finite-time convergence, but the convergence of the tracking error was not good enough in the case of bounded disturbance. The combination of SMC and interference estimation is a potential concept [16]- [22]. Non-linear interference observer (NDO) is a common classic method.…”

Section: Introductionmentioning

confidence: 99%

Design of RBFNN-Based Adaptive Sliding Mode Control Strategy for Active Rehabilitation Robot

Zhang

2020

IEEE Access

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In accordance with the movement coordination principle of both lower limbs, a complete radial basis functions neural network based adaptive sliding mode control strategy (RBFVSMC) is proposed. The movement information on the non-affected side of patients is detected to drive the rehabilitation training. The nonlinear mathematical model of the rehabilitation robot system is firstly described. Based on the robotic dynamic model, a variable sliding mode control (VSMC) is proposed to stabilize the system. To reduce the buffeting problem caused by VSMC, the universal approximation of RBFNN is used to approach and compensate external disturbances and uncertainties. Besides, the buffeting phenomenon of sliding mode control is alleviated by replacing the sign function with a saturation function. The final asymptotic stability is guaranteed with Lyapunov criteria. Compared to proportional-integral-derivative (PID), radial basis functions neural network (RBFNN), continuous terminal SMC (CNTSMC), and decentralized adaptive robust controller (NDOBCTC), the effectiveness of the overall control scheme is demonstrated by cosimulation and human experiment in accordance to track following performance and disturbances rejection ability. INDEX TERMS Rehabilitation robot, radial basis functions neural network, variable sliding mode control, coordinated movement.

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“…To solve the above mention problems, the state observer is introduced into the SMC to estimate unknown sates in the complicate nonlinear systems. One of the effective methods is SMC using extended state observer (ESO) as states estimator 9,10 . The algorithm is demonstrated in Reference 11, an ESO involved in adaptive second‐order SMC scheme is employed to estimate unmeasurable states.…”

Section: Introductionmentioning

confidence: 99%

Sliding mode tracking control using finite‐time differentiator in high‐orders nonlinear systems with output stochastic signals

Yan

2020

Intl J Robust & Nonlinear

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This article presents sliding mode tracking control using finite-time nonlinear differentiator (FND) to the high-orders nonlinear systems that states are unknown and the output measurement signals involves stochastic disturbances. The FND using singular perturbation technique is applied to estimate the unknown states in the tracking system, and not rely on the model information under the condition of output stochastic signals. The stochastic noises involved in the output measurement signals are restrained sufficiently by the FNDs. The design of sliding mode tracking controller using FND reduces the chattering phenomenon efficiently. The analysis of systems stability and differentiator robustness is demonstrated. The comparative simulation results show that the sliding mode controller using FND track the desired trajectory in high accuracy with tracking errors fast convergence.

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RBF-based adaptive sliding mode controller with extended state observer for load following of nuclear power plant

Cited by 23 publications

References 26 publications

Nuclear power predictive sliding mode control based on state observer

Nuclear power predictive sliding mode control based on state observer

Design of RBFNN-Based Adaptive Sliding Mode Control Strategy for Active Rehabilitation Robot

Sliding mode tracking control using finite‐time differentiator in high‐orders nonlinear systems with output stochastic signals

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