An active fault diagnostic (AFD) approach for diagnosis of actuator faults is proposed. The AFD a... more An active fault diagnostic (AFD) approach for diagnosis of actuator faults is proposed. The AFD approach excites the system by injecting a so-called excitation input. Here, the input is designed off-line based on sensitivity analysis such that the maximum sensitivity for each individual system parameter is obtained. Using maximum sensitivity, results in a better precision in the estimation of the
An active fault diagnostic (AFD) approach for diagnosis of actuator faults is proposed. The AFD a... more An active fault diagnostic (AFD) approach for diagnosis of actuator faults is proposed. The AFD approach excites the system by injecting a so-called excitation input. Here, the input is designed off-line based on sensitivity analysis such that the maximum sensitivity for each individual system parameter is obtained. Using maximum sensitivity, results in a better precision in the estimation of the
This paper suggests an improved method for predictive control of hybrid systems with mixed inputs... more This paper suggests an improved method for predictive control of hybrid systems with mixed inputs. The algorithm takes into account the real nonlinear system as a model of a hybrid system, which is based on building a tree of evolution. Where the branch & bound (B&B) technique is applied for discrete controls in which an embedded nonlinear programming approach (Pattern search) is associated with each node of the tree in order to provide the continuous controls and explore the tree. Once the whole nodes of the tree are explored, the corresponding input is exploited to the system and the procedure is repeated. The performance of the resulting predictive control system is demonstrated on a motorboat simulation case study.
Performance of modern control systems typically relies on a number of strongly interconnected com... more Performance of modern control systems typically relies on a number of strongly interconnected components. Compo-nent malfunctions may degrade performance of the system or even result in loss of functionality. In applications such as climate control systems for livestock ...
A new bounded-error approach for the identification of discrete time hybrid systems in the piece-... more A new bounded-error approach for the identification of discrete time hybrid systems in the piece-wise affine (PWA) form is introduced. The PWA identification problem involves the estimation of the number of affine submodels, the parameters of affine submodels and the partition of the PWA map from data. By imposing a bound on the identification error, we formulate the PWA identification problem as a MIN PFS problem (partition into a minimum number of feasible subsystems) and propose a greedy clustering-based method for tackling it. The proposed approach yields to better results than the greedy randomized relaxation algorithm used in previous methods. Also, it is not sensitive to the overestimation of model orders and changes in the tuning parameters and therefore finding a right combination of the tuning parameters of the algorithm to get a model with prescribed bounded prediction error is simple
An active fault diagnostic (AFD) approach for diagnosis of actuator faults is proposed. The AFD a... more An active fault diagnostic (AFD) approach for diagnosis of actuator faults is proposed. The AFD approach excites the system by injecting a so-called excitation input. Here, the input is designed off-line based on sensitivity analysis such that the maximum sensitivity for each individual system parameter is obtained. Using maximum sensitivity, results in a better precision in the estimation of the
An active fault diagnostic (AFD) approach for diagnosis of actuator faults is proposed. The AFD a... more An active fault diagnostic (AFD) approach for diagnosis of actuator faults is proposed. The AFD approach excites the system by injecting a so-called excitation input. Here, the input is designed off-line based on sensitivity analysis such that the maximum sensitivity for each individual system parameter is obtained. Using maximum sensitivity, results in a better precision in the estimation of the
This paper suggests an improved method for predictive control of hybrid systems with mixed inputs... more This paper suggests an improved method for predictive control of hybrid systems with mixed inputs. The algorithm takes into account the real nonlinear system as a model of a hybrid system, which is based on building a tree of evolution. Where the branch & bound (B&B) technique is applied for discrete controls in which an embedded nonlinear programming approach (Pattern search) is associated with each node of the tree in order to provide the continuous controls and explore the tree. Once the whole nodes of the tree are explored, the corresponding input is exploited to the system and the procedure is repeated. The performance of the resulting predictive control system is demonstrated on a motorboat simulation case study.
Performance of modern control systems typically relies on a number of strongly interconnected com... more Performance of modern control systems typically relies on a number of strongly interconnected components. Compo-nent malfunctions may degrade performance of the system or even result in loss of functionality. In applications such as climate control systems for livestock ...
A new bounded-error approach for the identification of discrete time hybrid systems in the piece-... more A new bounded-error approach for the identification of discrete time hybrid systems in the piece-wise affine (PWA) form is introduced. The PWA identification problem involves the estimation of the number of affine submodels, the parameters of affine submodels and the partition of the PWA map from data. By imposing a bound on the identification error, we formulate the PWA identification problem as a MIN PFS problem (partition into a minimum number of feasible subsystems) and propose a greedy clustering-based method for tackling it. The proposed approach yields to better results than the greedy randomized relaxation algorithm used in previous methods. Also, it is not sensitive to the overestimation of model orders and changes in the tuning parameters and therefore finding a right combination of the tuning parameters of the algorithm to get a model with prescribed bounded prediction error is simple
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