An Efficient and Stabilizing Model Predictive Control of Switched Systems

Hariprasad, K.; Bhartiya, Sharad

doi:10.1109/tac.2016.2613909

Cited by 21 publications

(16 citation statements)

References 30 publications

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“…These examples are presented to prove the concept, namely, to show the difference between MLD and EMLD systems and that the proposed complexity reduction conditions occur in practice. Furthermore, they are the basic node of any large‐scale power electronic or process control system in the sense that a more complex system can be obtained by their series and parallel connections . The application of the proposed ideas to large‐scale problems is the topic of future research.…”

Section: Examplesmentioning

confidence: 99%

“…Finally, matrices A, B 1 , B 2 , B 3 , and B 4 in the overall MLD model in (1) are obtained according to (47), (29a), and (22) as in (48)…”

Section: Proposition 2 Each Deterministic and Nonblocking Dha Can Bementioning

confidence: 99%

“…Furthermore, they are the basic node of any large-scale power electronic or process control system in the sense that a more complex system can be obtained by their series and parallel connections. 34,47,48,68 The application of the proposed ideas to large-scale problems is the topic of future research.…”

Section: Examplesmentioning

confidence: 99%

“…As another benchmark, a tank system is considered as a basic component of any process control system. Similar benchmark systems consider the overall system as a switched system without any need to the automaton‐based dynamic . In this paper, these systems are dealt with from a different angle by considering a reset dynamic during tank emptying.…”

Section: Introductionmentioning

confidence: 99%

“…Similar benchmark systems consider the overall system as a switched system without any need to the automaton-based dynamic. 7,31,47,48 In this paper, these systems are dealt with from a different angle by considering a reset dynamic during tank emptying. This implies that, in contrast to the existing modeling techniques, the tank system is modeled as a DHA, including discrete states.…”

Section: Introductionmentioning

confidence: 99%

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On the complexity and dynamical properties of mixed logical dynamical systems via an automaton‐based realization of discrete‐time hybrid automaton

Hejri

Giua

Mokhtari

2018

Intl J Robust & Nonlinear

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Summary Modeling of hybrid systems using mixed logical dynamical (MLD) systems is an art. The MLD framework often introduces numerous constraints and auxiliary binary and continuous variables, which, in turn, increase the computational complexity of the optimization problems. This paper presents an automaton‐based realization for discrete‐time hybrid automaton (DHA) with both controlled and uncontrolled switching phenomena by which it is attempted to develop efficient translation techniques to MLD systems and reduce the total number of decision variables in the MLD model. Based on this DHA model, a modified version of MLD systems, which is called extended MLD (EMLD) is formally defined and represented. EMLD is derived based on the concept of forward evolution in which the one‐step delay between the change of discrete states and continuous dynamics of the existing evolution in the conventional MLD systems is eliminated. The result is that the size of EMLD model in terms of the number of variables, which determines the complexity of the synthesis problems on MLD systems, is reduced. The dynamical properties such as the nonblocking, determinism, and well‐posedness of the proposed DHA and its equivalent MLD models are investigated, and the necessary and sufficient conditions are derived. The effectiveness of the proposed ideas is shown by the numerical examples.

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Section: Examplesmentioning

confidence: 99%

“…Finally, matrices A, B 1 , B 2 , B 3 , and B 4 in the overall MLD model in (1) are obtained according to (47), (29a), and (22) as in (48)…”

Section: Proposition 2 Each Deterministic and Nonblocking Dha Can Bementioning

confidence: 99%

Section: Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

On the complexity and dynamical properties of mixed logical dynamical systems via an automaton‐based realization of discrete‐time hybrid automaton

Hejri

Giua

Mokhtari

2018

Intl J Robust & Nonlinear

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Adaptive event‐triggered robust H_∞ control for Takagi–Sugeno fuzzy networked Markov jump systems with time‐varying delay

Aslam

Qaisar

Majid

et al. 2022

Asian Journal of Control

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This paper considers an adaptive event‐triggered robust H∞ control for the Takagi–Sugeno (T‐S) fuzzy under the networked Markov jump systems (NMJSs) with time‐varying delay. First, a new adaptive event‐triggered scheme is developed to guarantee the T‐S fuzzy NMJSs, and as a result, communication energy consumption reduced while device efficiency is maintained. Besides, an asynchronous operation method is adopted to deal with the mismatched premise variables between the fuzzy system and the fuzzy controller. One of the main objectives of this article is to construct the fuzzy state‐feedback controller (mode‐dependent) in a closed‐loop form for stochastic stability for all admissible parameter uncertainties with an H∞ performance index. Different from the conventional triggering mechanism, in this paper, the parameters of the triggering function are based on a new adaptive law that is obtained online rather than a predefined constant. To achieve the less conservative control design, a new type of stochastic Lyapunov–Krasovskii functional is designed by decomposing method, in which the delay interval transforms into various equidistant subintervals in terms of linear matrix inequalities. An example of a truck‐trailer application is used to demonstrate the effectively of the proposed algorithms.

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Nonlinear switched model predictive control with multiple Lyapunov functions for trajectory tracking and obstacle avoidance of nonholonomic systems

Hai

Yang

Wang

et al. 2023

Intl J Robust & Nonlinear

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In this paper, we investigate trajectory tracking and obstacle avoidance for a nonholonomic system subject to external disturbances by a nonlinear switched model predictive control (MPC) strategy. In the nonlinear switched MPC strategy, a potential field is introduced in a cost function to guarantee a smooth path for the nonholonomic system. A switched mechanism is designed to ensure switching stability by setting multiple Lyapunov functions in different areas. Different from traditional switched mechanisms, an average dwell time closely related to stability conditions is proposed to balance safety and stability in the whole process. Recursive feasibility is presented for the nonlinear switched MPC strategy in trajectory tracking and obstacle avoidance. Simulation results are provided to show effectiveness and superiority of the nonlinear switched MPC strategy by a two‐wheeled mobile vehicle.

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An Efficient and Stabilizing Model Predictive Control of Switched Systems

Cited by 21 publications

References 30 publications

On the complexity and dynamical properties of mixed logical dynamical systems via an automaton‐based realization of discrete‐time hybrid automaton

On the complexity and dynamical properties of mixed logical dynamical systems via an automaton‐based realization of discrete‐time hybrid automaton

Adaptive event‐triggered robust H_∞ control for Takagi–Sugeno fuzzy networked Markov jump systems with time‐varying delay

Nonlinear switched model predictive control with multiple Lyapunov functions for trajectory tracking and obstacle avoidance of nonholonomic systems

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An Efficient and Stabilizing Model Predictive Control of Switched Systems

Cited by 21 publications

References 30 publications

On the complexity and dynamical properties of mixed logical dynamical systems via an automaton‐based realization of discrete‐time hybrid automaton

On the complexity and dynamical properties of mixed logical dynamical systems via an automaton‐based realization of discrete‐time hybrid automaton

Adaptive event‐triggered robust H∞ control for Takagi–Sugeno fuzzy networked Markov jump systems with time‐varying delay

Nonlinear switched model predictive control with multiple Lyapunov functions for trajectory tracking and obstacle avoidance of nonholonomic systems

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Adaptive event‐triggered robust H_∞ control for Takagi–Sugeno fuzzy networked Markov jump systems with time‐varying delay