Data-Driven Model-Predictive Control for Nonlinear Systems With Stochastic Sampling Interval

Han, Honggui; Fu, Shijia; Sun, Haoyuan; Qiao, Junfei

doi:10.1109/tsmc.2022.3220550

Cited by 13 publications

(4 citation statements)

References 36 publications

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“…To make LBMPC tractable, the performance and safety tasks are decoupled by using reachability analysis [13], [14]. Variations include formulation of robust or stochastic MPC with state-dependent uncertainty for data-driven linear models [15], Gaussian processed (GP) [16], kernel regression model [17], Koopman operator model [18], recurrent neural networks [19], [20], fuzzy neural networks [21], [22] selforganizing radial basis function neural networks [23], or iterative model updates for linear systems with bounded uncertainties and robustness guarantees [24]. For a comprehensive review of LBMPC approaches we refer the reader to a recent review [25] and references therein.…”

Section: B Related Work 1) Learning-based Model Predictive Controlmentioning

confidence: 99%

“…where x i,k denotes the ith system dynamics state, and p, b, c, d are scalar-valued parameters defining the volume, shape, and center of the eliptic obstacle defined by (21).…”

Section: Example 4-parametric Obstacle Avoidance Problemmentioning

confidence: 99%

“…where the first term penalizes the terminal state condition with parametric reference r N , the second term penalizes the energy used in control actions, while the third and fourth terms represent control action and state smoothing penalties. For scaling the control objective terms we use following weight factor values Q r = 1.0, Q dx = 1.0, Q du = 10.0, Q u = 10.0, and we use Q h = 100.0 for state constraint penalties that include the obstacle avoidance constraint (21) and box constraints on states and control actions. For datasets we sample 30 000 uniformly distributed initial state conditions x i 0 , constraints parameters p i , b i , c i , d i , and terminal state references r i N , and use one third of data for train, validation, and test set, respectively.…”

Section: Example 4-parametric Obstacle Avoidance Problemmentioning

confidence: 99%

See 2 more Smart Citations

Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees

Drgoňa,

Tuor,

Vrabie

2024

IEEE Trans. Syst. Man Cybern, Syst.

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We present differentiable predictive control (DPC), a method for offline learning of constrained neural control policies for nonlinear dynamical systems with performance guarantees. We show that the sensitivities of the parametric optimal control problem can be used to obtain direct policy gradients. Specifically, we employ automatic differentiation (AD) to efficiently compute the sensitivities of the model predictive control (MPC) objective function and constraints penalties. To guarantee safety upon deployment, we derive probabilistic guarantees on closed-loop stability and constraint satisfaction based on indicator functions and Hoeffding's inequality. We empirically demonstrate that the proposed method can learn neural control policies for various parametric optimal control tasks. In particular, we show that the proposed DPC method can stabilize systems with unstable dynamics, track time-varying references, and satisfy nonlinear state and input constraints. Our DPC method has practical time savings compared to alternative approaches for fast and memoryefficient controller design. Specifically, DPC does not depend on a supervisory controller as opposed to approximate MPC based on imitation learning. We demonstrate that, without losing performance, DPC is scalable with greatly reduced demands on memory and computation compared to implicit and explicit MPC while being more sample efficient than model-free reinforcement learning (RL) algorithms.

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Section: B Related Work 1) Learning-based Model Predictive Controlmentioning

confidence: 99%

“…where x i,k denotes the ith system dynamics state, and p, b, c, d are scalar-valued parameters defining the volume, shape, and center of the eliptic obstacle defined by (21).…”

Section: Example 4-parametric Obstacle Avoidance Problemmentioning

confidence: 99%

Section: Example 4-parametric Obstacle Avoidance Problemmentioning

confidence: 99%

See 1 more Smart Citation

Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees

Drgoňa,

Tuor,

Vrabie

2024

IEEE Trans. Syst. Man Cybern, Syst.

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show abstract

“…It has already been utilized in various fields, such as car automation, haptic collaboration over the Internet, wastewater treatment processes, F-404 aircraft engine system, and unmanned aerial vehicles (UAVs). [8][9][10][11][12] Although networked control systems bring a lot of convenience to the realization of the control, in the practical process, the limitation of network bandwidth will bring a series of problems resulting in the deterioration of system performance. To reduce the network communication burden, the sampled-data control technique is used.…”

Section: Introductionmentioning

confidence: 99%

Observer‐based sampled‐data control for networked systems with consecutive packet dropouts

Sun

Han

Sun

et al. 2023

Intl J Robust & Nonlinear

Self Cite

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This paper investigates the observer‐based sampled‐data control problem for networked systems with consecutive packet dropouts. A new consecutive packet dropout model is established to describe the phenomenon of packet dropout in both the sensor‐controller (S‐C) and controller‐actuator (C‐A) channels. First, the considered system with observer‐based control scheme is studied in the discrete‐time domain by using equivalent discretization transformation. In order to simplify subsequent analysis, matrix exponential computation is employed to transform the discrete‐time system into a more tractable form. Then, the proposed new consecutive packet dropout model is taken into consideration to establish the stochastic mean square exponential stability condition. Furthermore, a novel two‐step synthesis approach is used to design the observer‐based controller. Finally, the effectiveness of the proposed observer‐based sampled‐data control approach is verified using a numerical example. In addition, the advantages of the proposed approach are validated through comparisons.

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Data-driven control of singularly perturbed hybrid systems with multi-rate sampling

He,

Zhu,

Chen

et al. 2024

ISA Transactions

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Data-Driven Model-Predictive Control for Nonlinear Systems With Stochastic Sampling Interval

Cited by 13 publications

References 36 publications

Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees

Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees

Observer‐based sampled‐data control for networked systems with consecutive packet dropouts

Data-driven control of singularly perturbed hybrid systems with multi-rate sampling

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