Self-Triggered Stochastic MPC for Linear Systems With Disturbances

Sun, Zhongqi; Rostampour, Vahab; Cao, Ming

doi:10.1109/lcsys.2019.2918763

Cited by 9 publications

(7 citation statements)

References 24 publications

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“…Another impressive method is minmax MPC, which includes two optimisation problems [9], [10]. Furthermore, stochastic MPC provides an alternative way to deal with stochastic disturbance [11], [12]. The use of statistic properties of disturbance makes it possible to achieve better control performance than the worst case-based MPC algorithms.…”

mentioning

confidence: 99%

Model Predictive Control With Preview: Recursive Feasibility and Stability

Fang

Chen

2022

IEEE Control Syst. Lett.

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This paper proposes a stabilising model predictive control (MPC) scheme with preview information of disturbance for nonlinear systems. The proposed MPC algorithm is able to not only reject disturbance by making use of disturbance preview information as necessary, but also take advantage of the disturbance if it is good for a control task. This is realised by taking into account both the task (e.g. reference trajectory) and disturbance preview in the prediction horizon when performing online optimisation. Conditions are established to ensure recursive feasibility and stability under disturbance. First the disturbance within the horizon is augmented with the state to form a new composite system and then the stage cost function is modified accordingly. With the help of input-to-state stability theory, a terminal cost and a terminal constraint are constructed and added to the MPC algorithm with preview to guarantee its recursive feasibility and stability under a pre-bounded disturbance. Numerical simulation results demonstrate the effectiveness of the proposed MPC algorithm.

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confidence: 99%

Model Predictive Control With Preview: Recursive Feasibility and Stability

Fang

Chen

2022

IEEE Control Syst. Lett.

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“…First of all, we will propose a procedure to find a terminal constraint set X f to satisfy the condition (12) in Assumption 4 based on the linearised system (23).…”

Section: B the Calculation Of The Terminal Conditionsmentioning

confidence: 99%

“…The dual stage cost in the min-max MPC method leads to heavy computational burden, which poses an obstacle for its engineering implementation. Furthermore, the stochastic MPC provides an alternative way to deal with stochastic disturbance [11], [12]. The use of statistic properties of disturbance makes it possible to achieve better control performance than the worst case-based MPC algorithms.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control with Preview: Recursive Feasibility and Stability

Fang¹,

Chen²

2022

Preprint

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This paper proposes a stabilising model predictive control (MPC) scheme with preview information of disturbance for nonlinear systems. The proposed MPC algorithm is able to not only reject disturbance by making use of disturbance preview information as necessary, but also take advantage of the disturbance if it is good for a control task. This is realised by taking into account both the task (e.g. reference trajectory) and disturbance preview in the prediction horizon when performing online optimisation. Conditions are established to ensure recursive feasibility and stability under the disturbance. First the disturbance within the horizon is augmented with the state to form a new composite system and then the stage cost function is modified accordingly. With the help of input-to-state stability theory, a terminal cost and a terminal constraint are constructed and added to the MPC algorithm with preview to guarantee its recursive feasibility and stability under a pre-bounded disturbance. Numerical simulation results demonstrate the effectiveness of the proposed MPC algorithm.

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“…It should be pointed out that when the measurement error meets the pre‐defined threshold condition, the control signal will be updated immediately and will be applicable to the control system, which means that the system states need to be continuously monitored. Considering the feasibility of actual physical implementation, a self‐triggered (ST) control method 11‐13 has been widely concerned because of its outstanding advantage in eliminating the need for continuous supervision, that is, this control method is only based on the current state measurement, and the next sampling time can be scheduled at the current time. In Reference 11, a stochastic self‐triggered control approach was put forward for linear system with probabilistic constraints to reduce the amount of controller update so as to effectively reduce the use of communication resources.…”

Section: Introductionmentioning

confidence: 99%

Self‐triggered adaptive prescribed‐time tracking control for stochastic nonlinear systems

Zhu

Pan

et al. 2023

Adaptive Control & Signal

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Summary This paper proposes a self‐triggered (ST) adaptive prescribed‐time tracking control method for a class of stochastic nonlinear systems. Different from the existing results, an improved ST mechanism is proposed by adding a judgment condition to reduce the negative effect of excessive design interval on system performance. Based on the one‐to‐one mapping and backstepping technique, an adaptive prescribed‐time tracking control method is proposed, which can make the error converge to the predefined precision set within the predetermined time. Simultaneously, applying the Lyapunov stability method, the boundedness of all signals in the closed‐loop system can be ensured. Finally, a detailed simulation example is provided to show the effectiveness of the proposed control strategy.

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Self-Triggered Stochastic MPC for Linear Systems With Disturbances

Cited by 9 publications

References 24 publications

Model Predictive Control With Preview: Recursive Feasibility and Stability

Model Predictive Control With Preview: Recursive Feasibility and Stability

Model Predictive Control with Preview: Recursive Feasibility and Stability

Self‐triggered adaptive prescribed‐time tracking control for stochastic nonlinear systems

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