Model Predictive Control for a Linear Parameter Varying Model of an UAV

Cavanini, Luca; Ippoliti, Gianluca; Camacho, Eduardo F.

doi:10.1007/s10846-021-01337-x

Cited by 37 publications

(18 citation statements)

References 49 publications

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“…Where the hessian matrix and the column vector are sequences of the optimal control inputs [24]- [27].…”

Section: A Adaptive Model Predictive Control (Ampc) Designmentioning

confidence: 99%

“…Since the LPV was stated with the scheduling parameters directly affecting the system dynamics and the input control signal manipulated by the AMPC process, the approximate relationship between the LPV scheduled state-space matrices and the control signals can be defined in a linear state equation (17). Therefore, iterating the discrete time LPV scheduled state space matrices S p = ( p , p , p ) used to predict the scheduling parameters reference, and keep the AMPC parameters updated every time instant k over a prediction horizon Np [24]- [27]. ∆X p (k|k) = ℱ p X p (k) + ℳ p ∆U(k) (17) Where the predicted future state vector ∆X p (k), the future input control sequence ∆U(k) are the LIT vector based on the scheduling parameters S p are incremented at the time instant k over a prediction horizon Np as below…”

Section: A Adaptive Model Predictive Control (Ampc) Designmentioning

confidence: 99%

“…By incrementing the expression (17), the prediction of the scheduling parameters is estimated to keep the AMPC optimization of the proposed objective cost function, and the constraints (11) updated each time instant k over the considered prediction horizon Np. To get the QP problem's (11) solved instantly over a prediction horizon interval, the scheduling parameters reference need to be defined, and the LPV scheduled statespace matrices calculated as below [24]- [27].…”

Section: A Adaptive Model Predictive Control (Ampc) Designmentioning

confidence: 99%

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Adaptive Model Predictive Control for DC-DC Power Converters With Parameters’ Uncertainties

Albira¹,

Zohdy

2021

IEEE Access

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This research investigates the Adaptive Model Predictive Controller (AMPC) and Linear Parameter-Varying (LPV) control system for a direct current (dc-dc) buck-boost converter, considering the parameters' uncertainty. The LPV model and the AMPC are explicitly constructed to perform a robust control design for the proposed dc-dc converter. The LPV model was created out of a set of linearized systems at different operating conditions to perform Linear Time-Invariant (LTI) models. Due to the dc-dc converter's nonlinear characteristic, the performed LTI models might have declination, which the AMPC can perfectly address by adapting the prediction model for the changes in the operating conditions. The proposed AMPC control system was implemented in a simulation environment as well as in a real-time environment on an Arduino Mega 2560 microcontroller to test its robustness and quality. The proposed AMPC control system works well compared with some existing control system algorithms at different prediction horizons. Also, the comparison considers the designed Gain Scheduling Proportional Integral (G.S-PI) and the regular Model Predictive (reg-MPC) Controllers were implemented without using the LPV model to test their performance against the proposed converter's parameters uncertainties.INDEX TERMS dc-dc buck-boost converter, MPC controller, AMPC controller, LPV model, uncertainty modeling, Quadratic Programming (QP), and Optimization. MOHAMED E. ALBIRA. (Student Member, IEEE) received the H.D degree in electrical and electronics engineering technology from Collage of Technical and Science, Misurata, Libya, in 2002, the M.S.

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“…Where the hessian matrix and the column vector are sequences of the optimal control inputs [24]- [27].…”

Section: A Adaptive Model Predictive Control (Ampc) Designmentioning

confidence: 99%

Section: A Adaptive Model Predictive Control (Ampc) Designmentioning

confidence: 99%

Section: A Adaptive Model Predictive Control (Ampc) Designmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Model Predictive Control for DC-DC Power Converters With Parameters’ Uncertainties

Albira¹,

Zohdy

2021

IEEE Access

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“…Model predictive control methods have been employed by various industries over the past few decades [48][49][50][51][52]. MPC relies on the dynamic model of the process.…”

Section: Model Predictive Control (Mpc)-linear and Nonlinearmentioning

confidence: 99%

Evaluation of a Combined MHE-NMPC Approach to Handle Plant-Model Mismatch in a Rotary Tablet Press

et al. 2021

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The transition from batch to continuous processes in the pharmaceutical industry has been driven by the potential improvement in process controllability, product quality homogeneity, and reduction of material inventory. A quality-by-control (QbC) approach has been implemented in a variety of pharmaceutical product manufacturing modalities to increase product quality through a three-level hierarchical control structure. In the implementation of the QbC approach it is common practice to simplify control algorithms by utilizing linearized models with constant model parameters. Nonlinear model predictive control (NMPC) can effectively deliver control functionality for highly sensitive variations and nonlinear multiple-input-multiple-output (MIMO) systems, which is essential for the highly regulated pharmaceutical manufacturing industry. This work focuses on developing and implementing NMPC in continuous manufacturing of solid dosage forms. To mitigate control degradation caused by plant-model mismatch, careful monitoring and continuous improvement strategies are studied. When moving horizon estimation (MHE) is integrated with NMPC, historical data in the past time window together with real-time data from the sensor network enable state estimation and accurate tracking of the highly sensitive model parameters. The adaptive model used in the NMPC strategy can compensate for process uncertainties, further reducing plant-model mismatch effects. The nonlinear mechanistic model used in both MHE and NMPC can predict the essential but complex powder properties and provide physical interpretation of abnormal events. The adaptive NMPC implementation and its real-time control performance analysis and practical applicability are demonstrated through a series of illustrative examples that highlight the effectiveness of the proposed approach for different scenarios of plant-model mismatch, while also incorporating glidant effects.

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“…Tianyi He [22] proposed an innovative design of smooth-switching LPV Dynamic Output-Feedback (DOF) controllers and designed a family of LPV controllers on each subregion, as well as switching smoothness among adjacent subregions. Luca Cavanini [23] designed an MPC to drive a UAV based on the LPV model using a subspace identification technique. Rui Wang [24] solved an optimal control problem for aero-engines based on a switched LPV model and presented an optimal control method for the switched LPV system.…”

Section: Introductionmentioning

confidence: 99%

Gain-Scheduled Model Predictive Control for a Commercial Vehicle Air Brake System

Deng

2021

Processes

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This paper presents a control-oriented Linear Parameter-Varying (LPV) model for commercial vehicle air brake systems with the electro-pneumatic proportional valve based on the nonlinear mathematical model, a set of discrete-time linearized models at different target pressures with the q-Markov Cover system identification method. The scheduled parameters for the LPV model were the brake chamber pressure, which was controlled by the electro-pneumatic proportional valve. On the basis of the LPV model, a family of Model Predictive Control (MPC) controllers with a Kalman filter was designed at each operation point. Then, the gain-scheduled MPC was designed over the entire operating range with the switched strategy, which was validated by experimental data. Furthermore, compared with the PID controller, the performance of the system was improved with a gain-scheduled MPC controller.

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Model Predictive Control for a Linear Parameter Varying Model of an UAV

Cited by 37 publications

References 49 publications

Adaptive Model Predictive Control for DC-DC Power Converters With Parameters’ Uncertainties

Adaptive Model Predictive Control for DC-DC Power Converters With Parameters’ Uncertainties

Evaluation of a Combined MHE-NMPC Approach to Handle Plant-Model Mismatch in a Rotary Tablet Press

Gain-Scheduled Model Predictive Control for a Commercial Vehicle Air Brake System

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