Distributed stochastic MPC for linear systems with probabilistic constraints and quantisation

Zhao, Guanglei; Shida, Yang

doi:10.1049/iet-cta.2019.0030

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…19 There are lots of applications in which MPC is combined with multi-agent systems because of its particular features. Using MPC in the formation of multi-agent systems, 20 avoiding collisions of agents, 21 handling constraints, 22 dead-times, and easier working with multi-variable problems have made MPC popular in the applications related to multi-agent systems. In this article, MPC is used to be combined with W-MSR method to steer the agents to a predefined point in the presence of constraints.…”

Section: Introductionmentioning

confidence: 99%

Predictive consensus tracking of multi‐agent systems in the presence of Byzantine agents and connection loss of reference signals

Rezaei,

Bagheri,

Hashemzadeh

2023

Optim Control Appl Methods

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This article proposes the consensus tracking of multi‐agent systems, in the presence of Byzantine agents and loss of connection between agents and the reference signal. The main purpose of this work is to steer a multi‐agent system to the desired reference, assuming that there are some agents which send incorrect information to the other agents, and the connection between the agents and the reference signal may lose. Using graph properties alongside the weighted‐mean‐subsequence‐reduced (W‐MSR) algorithm led to a novel control method for multi‐agent systems. With the aid of model predictive control (MPC), the agents reach the desired consensus with connection loss of reference signals, misbehaving agents in the system, and constraints on the input signals. Combining MPC with the W‐MSR algorithm results in an algorithm called reference‐based‐W‐MSR (RBW‐MSR), which has made the consensus point fixed according to a predetermined reference. A theorem is illustrated to guarantee the consensus with the interruption of reference signals and agents. The effectiveness of the proposed algorithm is illustrated via simulation results.

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Section: Introductionmentioning

confidence: 99%

Predictive consensus tracking of multi‐agent systems in the presence of Byzantine agents and connection loss of reference signals

Rezaei,

Bagheri,

Hashemzadeh

2023

Optim Control Appl Methods

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“…To do this, we model the multi-vehicle interactive system using a Distributed SMPC (DSMPC) framework [14][15][16]. In this framework, each vehicle interacts with its surrounding vehicles by observing their current states and predicting their future behaviors and avoiding potential collisions.…”

Section: Introductionmentioning

confidence: 99%

Distributed Stochastic Model Predictive Control for a Microscopic Interactive Traffic Model

et al. 2023

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Stochastic Model Predictive Control (SMPC) has attracted increasing attention for autonomous driving in recent years, since it enables collision-free maneuvers and trajectory planning and can deal with uncertainties in a non-conservative way. Many promising strategies have been proposed on how to use SMPC to select appropriate maneuvers and plan safe trajectories in uncertain environments. The limitation of these approaches is that they focus on scenarios where only one vehicle is controlled by SMPC and is, thus, reacting to the surrounding vehicles; however, the surrounding vehicles do not react to the SMPC-controlled vehicle, which means there is no mutual interaction. However, when multiple autonomous vehicles are driving on the road, each individual vehicle will take the behavior of the other surrounding vehicles into account and adjust its individual decisions accordingly in trajectory planning. This paper, therefore, examines in simulations how the interactive control system of multiple SMPC-controlled vehicles behave based on a Distributed SMPC (DSMPC) framework. For a three-lane highway scenario, we first investigate the effects of the risk parameter of the collision avoidance probabilistic constraint on non-interactive and interactive vehicle systems and provide insights into how to parameterize the controllers in interactive vehicle systems.

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“…In this way, the SMPC results in less conservatism in constraints satisfaction and achieves better control performance, as the worst-case scenarios unlikely occur. Generally, SMPC methods can be classified into two main categories [8]: the analytic approximation methods [9][10][11] and the scenario-based methods [12][13][14]. The former ones reformulate the probabilistic constraints in a deterministic form by means of constraint tightening, whereas the latter ones generate a sufficient number of randomly sampled realisations to satisfy the chance constraints.…”

Section: Introductionmentioning

confidence: 99%

Adaptive stochastic model predictive control of linear systems using Gaussian process regression

2020

IET Control Theory & Appl

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This paper presents a stochastic model predictive control method for linear time‐invariant systems subject to state‐dependent additive uncertainties modelled by Gaussian process (GP). The new method is developed by re‐building the tube‐based model predictive control framework with chance constraints via adaptive constraint tightening. In particular, the tightened constraint set is constructed by forecasting the confidence region of uncertainty. Utilising this adaptive strategy, the Gaussian process based stochastic model predictive control (GP‐SMPC) algorithm is designed by applying the adaptive tightened constraints in all prediction horizons. To reduce the computation load, the one‐step GP‐SMPC algorithm is further developed by imposing the tightened constraints only to the first‐step nominal state and the worst‐case constraints to the remaining steps. Under the assumption that the state‐dependent uncertainties are bounded, the recursive feasibility of the designed optimisation problem is ensured, and the closed‐loop system stability is guaranteed. The effectiveness and advantage over existing methods are verified via simulation and comparison studies.

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Distributed stochastic MPC for linear systems with probabilistic constraints and quantisation

Cited by 6 publications

References 29 publications

Predictive consensus tracking of multi‐agent systems in the presence of Byzantine agents and connection loss of reference signals

Predictive consensus tracking of multi‐agent systems in the presence of Byzantine agents and connection loss of reference signals

Distributed Stochastic Model Predictive Control for a Microscopic Interactive Traffic Model

Adaptive stochastic model predictive control of linear systems using Gaussian process regression

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