Dynamic Output Feedback Robust MPC for LPV Systems Subject to Input Saturation and Bounded Disturbance

Xubin Ping,Zhiwu Li,Abdulrahman Al-Ahmari 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.3

For linear parameter varying (LPV) systems with unknown scheduling parameters and bounded disturbance,a synthesis approach of dynamic output feedback robust model predictive control (OFRMPC) with inputsaturation is investigated. By pre-specifying partial controller parameters, a main optimization problem is solvedby convex optimization to reduce the on-line computational burden. The main optimization problem guarantees thatthe estimated state and estimation error converge within the corresponding invariant sets such that recursive feasibilityand robust stability are guaranteed. The consideration of input saturation in the main optimization problemimproves the control performance. Two numerical examples are given to illustrate the effectiveness of the approach.

Enhanced Sliding Mode Control and Online Estimation of Optimal Slip Ratio for Railway Vehicle Braking Systems

Jianfeng Liu,Qing Peng,Zhiwu Huang,Weirong Liu,Heng Li 한국정밀공학회 2018 International Journal of Precision Engineering and Vol.19 No.5

This paper proposes an optimal anti-locking control scheme so as to improve the braking performance of railway vehicles. The controlling effect of sliding mode control is improved, and the optimal slip ratio is achieved by extreme seeking algorithm. Firstly, a substitute function for the conventional sign function is proposed. Secondly, a closed loop observer for braking systems is used to enhance the estimation value of adhesion force, which can also be used for calculating reference speed. Finally Sliding Mode Controlbased controller needs to be entered a reference slip ratio called optimal slip ratio, which is searched by extreme seeking algorithm from the functional relationship between slip ratio and friction coefficient. Thus, the maximum adhesion is achieved despite wheel/ rail surface changes. The simulation result demonstrates the effect of real-time adjustment for braking torque, which guarantees the braking performance.

Instability analysis under part-load conditions in centrifugal pump

Weixiang Ye,Renfang Huang,Zhiwu Jiang,Xiaojun Li,ZuChao Zhu,Xianwu Luo 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.1

In this study, a centrifugal pump with a specific speed of 39.12 m×min -1 ×m 3 s -1 is treated to analyze the flow instability under part-load conditions by numerical simulation and experimental test. For calculations, the RANS method, coupled with the k-ω SST turbulence model, is adopted. Numerical results at different operation points are compared with available experimental data, such as hydraulic performance and flow field information by particle image velocimetry. The numerical and experiment results agree well. The flow simulation indicates a strong reverse flow at the passage upstream impeller inlet, and the energy loss in the impeller is the largest under partload conditions among all flow components in the pump. In one impeller revolution, one blade-to-blade flow passage is always nearly blocked off by the rotating stall occurring at the impeller inlet for each instant, and the blockage induces a jet flow with large velocity at the next blade-to-blade flow passage along the rotational direction of the impeller. The blockage and the jet flow in the blade-to-blade flow passages will make the flow unstable inside the impeller and cause performance breakdown and pressure vibration under part-load conditions for the pump.

Filtering and Intrusion Detection Approach for Secured Reconfigurable Mobile Systems

Rim Idriss,Adlen Loukil,Mohamed Khalgui,Zhiwu Li,Abdulrahman Al-Ahmari 대한전기학회 2017 Journal of Electrical Engineering & Technology Vol.12 No.5

This paper deals with reconfigurable secured mobile systems where the reconfigurability has the potential of providing a required adaptability to change the system requirements. The reconfiguration scenario is presented as a run-time automatic operation which allows security mechanisms and the addition-removal-update of software tasks. In particular, there is a definite requirement for filtering and intrusion detection mechanisms that will use fewer resources and also that will improve the security on the secured mobile devices. Filtering methods are used to control incoming traffic and messages, whereas, detection methods are used to detect malware events. Nevertheless, when different reconfiguration scenarios are applied at run-time, new security threats will be emerged against those systems which need to support multiple security objectives: Confidentiality, integrity and availability. We propose in this paper a new approach that efficiently detects threats after reconfigurable scenarios and which is based on filtering and intrusion detection methods. The paper’s contribution is applied to Android where the evaluation results demonstrate the effectiveness of the proposed middleware in order to detect the malicious events on reconfigurable secured mobile systems and the feasibility of running and executing such a system with the proposed solutions.

Filtering and Intrusion Detection Approach for Secured Reconfigurable Mobile Systems

Idriss, Rim,Loukil, Adlen,Khalgui, Mohamed,Li, Zhiwu,Al-Ahmari, Abdulrahman The Korean Institute of Electrical Engineers 2017 Journal of Electrical Engineering & Technology Vol.12 No.5

This paper deals with reconfigurable secured mobile systems where the reconfigurability has the potential of providing a required adaptability to change the system requirements. The reconfiguration scenario is presented as a run-time automatic operation which allows security mechanisms and the addition-removal-update of software tasks. In particular, there is a definite requirement for filtering and intrusion detection mechanisms that will use fewer resources and also that will improve the security on the secured mobile devices. Filtering methods are used to control incoming traffic and messages, whereas, detection methods are used to detect malware events. Nevertheless, when different reconfiguration scenarios are applied at run-time, new security threats will be emerged against those systems which need to support multiple security objectives: Confidentiality, integrity and availability. We propose in this paper a new approach that efficiently detects threats after reconfigurable scenarios and which is based on filtering and intrusion detection methods. The paper's contribution is applied to Android where the evaluation results demonstrate the effectiveness of the proposed middleware in order to detect the malicious events on reconfigurable secured mobile systems and the feasibility of running and executing such a system with the proposed solutions.

A Novel Method for Lithium-ion Battery Remaining Useful Life Prediction Using Time Window and Gradient Boosting Decision Trees

Zhiyong Zheng,Jun Peng,Kunyuan Deng,Kai Gao,Heng Li,Bin Chen,Yingze Yang,Zhiwu Huang 전력전자학회 2019 ICPE(ISPE)논문집 Vol.2019 No.5

Lithium-ion battery remaining useful life (RUL) is a key parameter on battery management system. Many machine learning methods are applied to RUL predictions, but they generally suffer from two limitations: (i) the extracted features fail to reflect the information hidden in the historical degradation status, and (ii) the accuracy cannot be guaranteed in the evaluation of battery degradation due to the non-linearity. In this paper, a new prediction method is proposed combining the time window (TW) and Gradient Boosting Decision Trees (GBDT). First, the energy (VCE) and the fluctuation index (VFI) of voltage signal are verified and selected as features. Then, a TW based feature extraction method is designed to extract features from the historical discharge process. After that, GBDT is adopted to model the relation of features and RUL. The proposed method is implemented on a recognized battery degradation dataset, and the advantages in accuracy are proven.

A Convexity Approach to Dynamic Output Feedback Robust MPC for LPV Systems with Bounded Disturbances

Xubin Ping,Sen Yang,Baocang Ding,Tarek Raïssi,Zhiwu Li 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.6

A convexity approach to dynamic output feedback robust model predictive control (OFRMPC) is proposed for linear parameter varying (LPV) systems with bounded disturbances. At each sampling time, the model parameters and disturbances are assumed to be unknown but bounded within pre-specified convex sets. Robust stability conditions on the augmented closed-loop system are derived using the techniques of robust positively invariant (RPI) set and the S-procedure. A convexity method reformulates the non-convex bilinear matrix inequalities (BMIs) problem as a convex optimization one such that the on-line computational burden is significantly reduced. The on-line optimized dynamic output feedback controller parameters steer the augmented states to converge within RPI sets and recursive feasibility of the optimization problem is guaranteed. Furthermore, bounds of the estimation error set are refreshed by updating the shape matrix of the future ellipsoidal estimation error set. The dynamic OFRMPC approach guarantees that the disturbance-free augmented closed-loop system (without consideration ofdisturbances) converges to the origin. In addition, when the system is subject to bounded disturbances, the augmented closed-loop system converges to a neighborhood of the origin. Two simulation examples are given to verify the effectiveness of the approach.

Optimistic Fault Diagnosis in Discrete Event Systems by Labeled Petri Nets and Basis Markings

Guanghui Zhu,Jiafeng Zhang,Zhong Zheng,Shan Luan,Te Chen,Qiang Ma,Zhiwu Li 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.6

This paper deals with the fault diagnosis problem of discrete event systems modeled with labeled Petri nets. Its main contributions are threefold. First, depending on whether a diagnosis function examines the fault transitions that possibly occur after the last observed event, we formally divide the diagnosis functions into two types: optimistic and pessimistic, which aims to facilitate the exploration of different diagnosis approaches. Second, a framework is proposed, which extends a given diagnosis approach for Petri nets to the case of labeled Petri nets. The main idea of the framework is to compute and combine the diagnosis results of observable transition sequences corresponding to an observed word. Third, we convert a basis-marking-based approach that is originally pessimistic to the optimistic case and prove the correctness of this conversion.