Flatness-based control by quasi-static feedback illustrated on a cascade of two chemical reactors

Rudolph, Joachim

doi:10.1080/002071700219821

Cited by 13 publications

(5 citation statements)

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“…23 In recent years, it has been gradually introduced into the control field of power generation, electric motors, power electronics, etc. 24 The basic meaning is that in a nonlinear system, if it is possible to find a set of output vectors and that set of output vectors and its finite order differential algebra can represent all the input vectors and state vectors of the system without any integration. Then this nonlinear system is considered to be flat and can be called differential flat system.…”

Section: Differential Flatness Controlmentioning

confidence: 99%

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Differential flat & PSO based photovoltaic maximum power point tracking control under partial shading condition

Li,

Wang,

Wang

et al. 2023

Measurement and Control

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The use of maximum power point tracking (MPPT) technology has significantly increased the conversion efficiency of PV modules. However, the presence of partial shading in PV arrays can lead to multi-peaked output curves, which traditional MPPT methods struggle to track due to falling into local maximum power points. The paper proposes a MPPT control algorithm based on the combination of differential flat control (DFBC) and adaptive particle swarm optimization (APSO) algorithm. The PSO output value is used as the feed-forward feedback input of differential flat, and a second-order controller is used to track the reference flat trajectory, achieving global MPPT through differential flat control. The algorithm can overcome the system oscillation caused by the randomness of the PSO algorithm with the initialized particle position and the existence of control lag misjudgment. Simulation and experimental results show that the algorithm not only solves the problem that the traditional MPPT algorithm cannot find the global maximum power point, but also solves the problems that the traditional particle swarm algorithm has large randomness, slow convergence speed, and easy to produce large oscillations. The algorithm has greatly improved the tracking accuracy, tracking speed and response speed, realizing fast and accurate response to external changes, reducing energy loss, and improving the dynamic tracking performance of the system.

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Section: Differential Flatness Controlmentioning

confidence: 99%

“…23 In recent years, it has been gradually introduced into the control field of power generation, electric motors, power electronics, etc. 24…”

Section: Maximum Power Control Algorithm For Pv Systemsmentioning

confidence: 99%

Differential flat & PSO based photovoltaic maximum power point tracking control under partial shading condition

Li,

Wang,

Wang

et al. 2023

Measurement and Control

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show abstract

“…To solve this problem more easily, Delaleau and Rudolph suggested tracking control for a flat system based on a quasi‐static state feedback. This approach has been applied successfully for chemical reactors and gantry cranes . The main advantage of using the quasi‐static state feedback in the design of flatness‐based tracking control consists in the fact that there are no extra dynamics integrated into the control loop as in dynamic feedback linearization.…”

Section: Flatness‐based Tracking Controlmentioning

confidence: 99%

Optimal trajectory generation and robust flatness–based tracking control of quadrotors

Abadi

Mekki

Ramdani

2019

Optim Control Appl Methods

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This work proposes an optimal trajectory generation and a robust flatness-based tracking controller design to create a new performance guidance module for the quadrotor in dense indoor environments. The properties of the differential flatness, the B-spline, and the direct collocation method are exploited to convert the constrained optimization problem into a nonlinear programming one, which can be easily resolved by a classic solver. After that, the obtained optimal reference trajectory is applied to the dynamic quadrotor model and two different flatness-based controllers, namely, one based on feedback linearization and one based on feedforward linearization, are developed and compared to ensure the trajectory tracking despite the existence of disturbances and parametric uncertainties. Numerical simulation is executed to evaluate the proposed optimal trajectory generation approach and the robust tracking strategies. It turns out that the controller based on feedforward linearization outperforms the feedback linearization one in robustness and permits obtaining a performance guidance law for an uncertain quadrotor system.

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“…has been studied in [35]. System (11) comprises material and enthalpy balances, where c Ai and c Bi denote the concentrations of substance A and B in reactor i 2 {1, 2}, respectively.…”

Section: Feedback Linearization In Terms Of Symbolic Computationmentioning

confidence: 99%

Controller design for nonlinear multi-input – multi-output systems based on an algorithmic plant description

Röbenack

2007

Mathematical and Computer Modelling of Dynamical Systems

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Complicated multi-domain systems are usually described in terms of modelling languages. These models are mainly used for simulation. We discuss the usage of an algorithmic plant description for nonlinear controller design based on differential geometric concepts. The design procedures themselves are often formulated in terms of Lie derivatives. These derivatives are typically computed symbolically. The symbolic manipulations require an explicit description of the plant by formulae and expressions. Moreover, the symbolic computations can be very time consuming for complex and large-scale systems. These problems can be circumvented by automatic differentiation. This paper is concerned with the controller design of algorithmically modelled plants utilizing automatic differentiation.

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Flatness-based control by quasi-static feedback illustrated on a cascade of two chemical reactors

Cited by 13 publications

References 0 publications

Differential flat & PSO based photovoltaic maximum power point tracking control under partial shading condition

Differential flat & PSO based photovoltaic maximum power point tracking control under partial shading condition

Optimal trajectory generation and robust flatness–based tracking control of quadrotors

Controller design for nonlinear multi-input – multi-output systems based on an algorithmic plant description

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