Decentralized fault tolerant model predictive control of discrete-time interconnected nonlinear systems

Naghavi, S. Vahid; Safavi, Ali Akbar; Kazerooni, Maryam

doi:10.1016/j.jfranklin.2013.12.005

Cited by 21 publications

(16 citation statements)

References 18 publications

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“…MP control can account for certain nonlinearities entering a system by injecting the nonlinear effects into the controller and then compensating for them. Unfortunately, this typically has to be done externally to the controller and can only give bounded input-bounded output (BIBO) stability in many cases [13]. It should be noted that the situations where these nonlinearities can be accounted for are fairly limited at the current stage of the research.…”

Section: Background On Fault-tolerant Controlmentioning

confidence: 99%

Artificial Immune Systems: An Overview for Faulting Actuators

Kidd

2019

Actuators

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This paper reviews Artificial Immune Systems (AIS) that can be implemented to compensate for actuators that are in a faulted state or operating abnormally. Eventually, all actuators will fail or wear out, and these actuator faults must be managed if a system is to operate safely. The AIS are adaptive algorithms which are inherently well-suited to these situations by treating these faults as infections that must be combated. However, the computational intensity of these algorithms has caused them to have limited success in real-time situations. With the advent of distributed and cloud-based computing these algorithms have begun to be feasible for diagnosing faulted actuators and then generating compensating controllers in near-real-time. To encourage the application of AIS to these situations, this work presents research for the fundamental operating principles of AIS, their applications, and a brief case-study on their applicability to fault compensation by considering an overactuated rover with four independent drive wheels and independent front and rear steering.

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Section: Background On Fault-tolerant Controlmentioning

confidence: 99%

Artificial Immune Systems: An Overview for Faulting Actuators

Kidd

2019

Actuators

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“…[5][6][7][8] This problem is further intensified in multi-input multi-output (MIMO) dynamical systems given the interactions between various subsystems [9][10][11][12][13] and also when considering the combination with sector-bounded modeling uncertainty in the dynamical model. 14,15 Recent publications such as the work of Naghavi et al 16 have addressed the issue of time-delay uncertainty in MPC using adaptive estimation algorithms. However, noting the intensive computations required for nonlinear MPC, 17 the use of passive control algorithms is potentially beneficial in real-time application of a control scheme.…”

Section: Introductionmentioning

confidence: 99%

“…Recent publications such as the work of Naghavi et al 16 have addressed the issue of time-delay uncertainty in MPC using adaptive estimation algorithms. However, noting the intensive computations required for nonlinear MPC, 17 the use of passive control algorithms is potentially beneficial in real-time application of a control scheme.…”

Section: Introductionmentioning

confidence: 99%

Optimally designed Lyapunov–Krasovskii terminal costs for robust stable–feasible model predictive control of uncertain time-delay nonlinear dynamical systems

Yaqubi

Homaeinezhad

2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article details a new Model Predictive Control algorithm ensuring robust stability and control feasibility for uncertain nonlinear multi-input multi-output dynamical systems considering uncertain time-delay effects. The proposed control algorithm is based on construction of a Lyapunov–Krasovskii functional as terminal cost. Incorporation of this terminal cost into the Model Predictive Control optimization problem and calculation of the associated admissible set result in robust feasibility and robust stability of closed-loop system in presence of uncertain time-delay effects and bounded disturbance signals. The Lyapunov–Krasovskii functional term is constructed with respect to predicted sliding functions over the prediction horizon and considers the effects of dynamical variations over the prediction horizon in generation of control inputs. As dynamical variations are investigated in a sample-to-sample basis, feasible sliding regions are updated at each sample as well. Finally, based on expression of sliding functions as a combination of dynamical variations and input-based terms, required control inputs are calculated in the admissible bound by the optimization algorithm. Construction of control scheme on this basis permits straightforward calculation of robust stability and feasibility conditions for a general class of uncertain nonlinear system in finite prediction horizon whereas in the previous works, often-restrictive conditions were considered for the investigated dynamical systems. Numerical illustrations indicate precision and efficiency of control algorithm and improved stability and convergence rate for multivariable nonlinear dynamical systems considering uncertain time-delay effects. Finally, hardware-in-the-loop implementation indicates applicability of the proposed scheme in real-time control applications particularly in case appropriate compromises between optimality and calculation speed are considered.

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“…For example, heuristic defense mechanisms for dual decomposition DMPC are proposed in [12], [13] to minimize the effects of attacks of the price based coordination mechanism. Finally, it must be noticed that problems due to mutual interaction can also arise as a consequence of faulty components [14], [15].…”

Section: Introductionmentioning

confidence: 99%

A Distributed Model Predictive Control Scheme with Robustness Against Noncompliant Controllers

Maestre

Trodden

Ishii

2018

2018 IEEE Conference on Decision and Control (CDC)

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A tube-based distributed model predictive control (DMPC) scheme is proposed for dynamically coupled linear systems. The control scheme is designed to guarantee local performance even when neighboring controllers are not complying with the requirements of the algorithm (e.g., they are malicious or faulty). The resulting conservativeness is minimized, for controllers aim to minimize their state and input constraint sets to reduce mutual disturbances. Also, sufficient conditions for feasibility and exponential stability are given. Finally, these ideas are illustrated and assessed with respect to other robust DMPC via a simulated example.

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Decentralized fault tolerant model predictive control of discrete-time interconnected nonlinear systems

Cited by 21 publications

References 18 publications

Artificial Immune Systems: An Overview for Faulting Actuators

Artificial Immune Systems: An Overview for Faulting Actuators

Optimally designed Lyapunov–Krasovskii terminal costs for robust stable–feasible model predictive control of uncertain time-delay nonlinear dynamical systems

A Distributed Model Predictive Control Scheme with Robustness Against Noncompliant Controllers

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