Takagi–Sugeno fuzzy generalized predictive control for a class of nonlinear systems

Shi, Ke; Wang, Bin; Yang, Lingyu; Jian, Shikang; Bi, Jikai

doi:10.1007/s11071-017-3443-z

Cited by 21 publications

(13 citation statements)

References 30 publications

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“…The sampling period was set to 0.01 s. The value of the modeling time domain N was 20, with two inputs and one output. The following equations were obtained after sampling was constructed for the constrained QP solution and the coefficient matrix A a a = [ ] 11 12 was used to calculate the predicted value of the output at any time. On the basis of the status of the proportional and servo valves, the constraints of the input signal were set as follows: Figure 6 displays the simulation model for dual-valve parallel PID-MPC composite control (similar to the model of PID control).…”

Section: Amesim-simulink Co-simulation Modeling For Single-valve Controlmentioning

confidence: 99%

“…Because predictive control can provide real-time optimal control under the constraint conditions through a predictive model, rolling optimization, and feedback correction, a dual-valve proportional-integral-derivative (PID)-multivariable predictive control (MPC) algorithm based on predictive control is proposed. [8][9][10][11][12] The PID-MPC controller uses the PID and MPC systems as the inner and outer loops, respectively. This article is organized as follows: In section "Mathematical model of the dualvalve parallel electro-hydraulic servo system," the mathematical model of the electrohydraulic servo system using proportional and servo valves is provided.…”

Section: Introductionmentioning

confidence: 99%

“…11 and A 12 are the dynamic matrices comprising a 11 and a 12 , respectively. ) is the input increment of the proportional control loop. )…”

mentioning

confidence: 99%

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Dual-valve parallel prediction control for an electro-hydraulic servo system

Zhu

et al. 2019

Science Progress

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To improve the dynamic response performance of a high-flow electro-hydraulic servo system, scholars have conducted considerable research on the synchronous and time-sharing controls of multiple valves. However, most scholars have used offline optimization to improve control performance. Thus, control performance cannot be dynamically adjusted or optimized. To repeatedly optimize the performance of multiple valves online, this study proposes a method for connecting a high-flow proportional valve in parallel with a low-flow servo valve. Moreover, this study proposes an algorithm in which a proportional–integral–derivative system and multivariable predictive control system are used as an inner loop and outer loop, respectively. The simulation and experimental results revealed that dual-valve parallel control could effectively improve the control accuracy and dynamic response performance of an electro-hydraulic servo system and that the proportional-integral-derivative–multivariable predictive control controller could further dynamically improve the control accuracy.

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Section: Amesim-simulink Co-simulation Modeling For Single-valve Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dual-valve parallel prediction control for an electro-hydraulic servo system

Zhu

et al. 2019

Science Progress

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“…The parameters are selected as T r = 0.01 s and T s = 0.001 s. The system initial value is x = [5 60 5]. The state trajectories of the controlled brushless DC motor system (44) with the FPFC control law (34), the existing fuzzy scheme in [37] and the existing fuzzy GPC (FGPC) in [38] are presented in Fig. 4.…”

Section: Numerical Simulationsmentioning

confidence: 99%

Fuzzy predictive functional control of a class of non‐linear systems

Wang

Yang

et al. 2019

IET Control Theory & Applications

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“…The control objective is to locate the crane at the desired position, whereby the trolley motor moves the load as fast as possible. At the same time, the oscillations that could destabilize the system must be minimized [22]. Due to this, it is essential to design control schemes that consider the under-actuation, a large number of linearities, possible faults, and robustness to the payload oscillations.…”

Section: Introductionmentioning

confidence: 99%

Robust qLPV Tracking Fault-Tolerant Control of a 3 DOF Mechanical Crane

López‐Estrada

Santos-Estudillo

Valencia‐Palomo

et al. 2020

MCA

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The main aim of this paper is to propose a robust fault-tolerant control for a three degree of freedom (DOF) mechanical crane by using a convex quasi-Linear Parameter Varying (qLPV) approach for modeling the crane and a passive fault-tolerant scheme. The control objective is to minimize the load oscillations while the desired path is tracked. The convex qLPV model is obtained by considering the nonlinear sector approach, which can represent exactly the nonlinear system under the bounded nonlinear terms. To improve the system safety, tolerance to partial actuator faults is considered. Performance requirements of the tracking control system are specified in an H∞ criteria that guarantees robustness against measurement noise, and partial faults. As a result, a set of Linear Matrix Inequalities is derived to compute the controller gains. Numerical experiments on a realistic 3 DOF crane model confirm the applicability of the control scheme.

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Takagi–Sugeno fuzzy generalized predictive control for a class of nonlinear systems

Cited by 21 publications

References 30 publications

Dual-valve parallel prediction control for an electro-hydraulic servo system

Dual-valve parallel prediction control for an electro-hydraulic servo system

Fuzzy predictive functional control of a class of non‐linear systems

Robust qLPV Tracking Fault-Tolerant Control of a 3 DOF Mechanical Crane

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