Robust MIMO water level control in interconnected twin-tanks using second order sliding mode control

Khan, Mohammad Khalid; Spurgeon, Sarah K.

doi:10.1016/j.conengprac.2005.02.001

Cited by 98 publications

(46 citation statements)

References 18 publications

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“…The stability and tracking performance of the composite controller consisting of the NDO (11) and (12) and the sliding mode controller (14) are established in Theorem 1.…”

Section: Remarkmentioning

confidence: 99%

“…Theorem 1 Consider the nonlinear system (1) with the sliding mode controller (14) where the sliding mode function is defined by (2), and the disturbance d(t) is approximated using disturbance observer (11) and (12). The system tracking error and disturbance approximation error converge to zero.…”

Section: Remarkmentioning

confidence: 99%

“…In [13], a new robust adaptive control architecture was proposed for operation of an inverted-pendulum system. A novel second-order SMC algorithm was presented for a class of MIMO nonlinear systems in input-output form with application to a twin tank system in [14]. These results mainly use the SMC to handle systems with large uncertainties, nonlinearities and model errors.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sliding mode control for a class of uncertain nonlinear system based on disturbance observer

Chen

2009

Adaptive Control & Signal

224

130

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In this paper, a sliding mode control (SMC) scheme is proposed for a class of nonlinear systems based on disturbance observers. For a nonlinear system, the disturbance that cannot be directly measured is estimated using a nonlinear disturbance observer. By choosing an appropriate nonlinear gain function, the disturbance observer can well approximate the unknown disturbance. Based on the output of the disturbance observer, an SMC scheme is presented for the nonlinear system, and the stability of the closed-loop system is established using Lyapunov method. Finally, two simulation examples are presented to illustrate the features and the effectiveness of the proposed disturbance-observer-based SMC scheme.

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“…The stability and tracking performance of the composite controller consisting of the NDO (11) and (12) and the sliding mode controller (14) are established in Theorem 1.…”

Section: Remarkmentioning

confidence: 99%

Section: Remarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sliding mode control for a class of uncertain nonlinear system based on disturbance observer

Chen

2009

Adaptive Control & Signal

224

130

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“…Khan and Spurgeon presented a second order sliding mode control algorithm for a class of MIMO nonlinear system. This algorithm has been applied for robust control of liquid level in interconnected twin-tanks [4]. Intelligent control including fuzzy control [5,6], neural network control [7], and genetic algorithms [8] have also been applied to the coupled tanks system.…”

Section: Introductionmentioning

confidence: 99%

Hardware Implementation of the Neural Network Predictive Controller for Coupled Tank System

Aldair¹

2014

AJEEE

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In this paper, a neural network based predictive controller is designed for controlling the liquid level of the coupled tank system. The controlled process is a nonlinear system; therefore, a nonlinear prediction method can be a better match in a predictive control strategy. The neural network predictive controller that is discussed in this paper uses a neural network model of a nonlinear plant to predict future plant performance. The simulation results are compared with PID control. The results show that the effectiveness of using the neural predictive controller for the coupled tank system. The Simulink Toolbox in MATLAB has been used to simulate the controlled system with the proposed controller. The VHDL has been used to describe the implementation of neural controller. Xilinx ISE Project Navigator Version 10.1 is used to obtain the compilation and timing test results as well as the synthesized design. The hardware implementation of the neural network predictive controller using FPGA board is proposed. To make sure that the FPGA board works like the simulated neural predictive controller, MATLAB programme is used to compare between the set of the data that are obtained from the ModelSim program and the set of the data that are obtained from the MATLAB Simulink model. Simulation results show that the FPGA board can be used as neural predictive controller for controlling the liquid level of the coupled tank system.

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“…Problem statement: Typical description for the system uncertainty caused by system faults (Khan and Spurgeon, 2006) can be represented with: System description in decoupled fashion sliding mode: Consider a second-order nonlinear system, which can be represented by the following state-space model in a canonical form: The disturbance is assumed to be bounded as d(t) D(t).…”

Section: Introductionmentioning

confidence: 99%

Piecewise Sliding Mode Decoupling Fault Tolerant Control System

Youssef¹

2010

American Journal of Applied Sciences

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Problem statement: Proposed method in the present study could deal with fault tolerant control system by using the so called decentralized control theory with decoupling fashion sliding mode control, dealing with subsystems instead of whole system and to the knowledge of the author there is no known computational algorithm for decentralized case, Approach: In this study we present a decoupling strategy based on the selection of sliding surface, which should be in piecewise sliding surface partition to apply the PwLTool which have as purpose in our case to delimit regions where sliding mode occur, after that as Results: We get a simple linearized model selected in those regions which could depict the complex system, Conclusion: With the 3 water tank level system as example we implement this new design scenario and since we are interested in networked control system we believe that this kind of controller implementation will not be affected by network delays.

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Robust MIMO water level control in interconnected twin-tanks using second order sliding mode control

Cited by 98 publications

References 18 publications

Sliding mode control for a class of uncertain nonlinear system based on disturbance observer

Sliding mode control for a class of uncertain nonlinear system based on disturbance observer

Hardware Implementation of the Neural Network Predictive Controller for Coupled Tank System

Piecewise Sliding Mode Decoupling Fault Tolerant Control System

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