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  • 1.
    Cheng, Lihong
    et al.
    School of Electro-Mechanical Engineering, Xidian University, Xi’an, China.
    Feng, Lei
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Model abstraction for discrete-event systems using a SAT solver2023In: IEEE Access, E-ISSN 2169-3536, Vol. 11, p. 17334-17347Article in journal (Refereed)
    Abstract [en]

    Model abstraction for finite state automata is beneficial to reduce the complexity of discrete-event systems (DES), enhances the readability and facilitates the control synthesis and verification of DES. Supremal quasi-congruence computation is an effective way for reducing the state space of DES. Effective algorithms on the supremal quasi-congruence relation have been developed based on the graph theory. This paper proposes a new approach to translate the supremal quasi-congruence computation into a satisfiability (SAT) problem that determines whether there exists an assignment for Boolean variables in the state-to-coset allocation matrix. If the result is satisfied, then the supremal quasi-congruence relation exists and the minimum equivalence classes is obtained. Otherwise, it indicates that there is no such supremal quasi-congruence relation, and a new set of observable events needs to be modified or reselected for the original system model. The satisfiability problem on the computation of supremal quasi-congruence relation is solved by different methods, which are respectively implemented by mixed integer linear programming (MILP) in MATLAB, binary linear programming (BLP) in CPLEX, and a SAT-based solver (Z3Py). Compared with the MILP and BLP methods, the SAT method is more efficient and stable. The computation time of model abstraction for large-scale systems by Z3Py solver is significantly reduced.

  • 2.
    Liu, Tianrui
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Leveling controller for a forwarder with pendulum arms2022Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    See file

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  • 3.
    Chen, DeJiu
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Su, Peng
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).
    Ottikkutti, Suranjan
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Embedded Control Systems.
    Vartholomeos, Panagiotis
    Tahmasebi, Kaveh Nazem
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Embedded Control Systems.
    Karamousadakis, Michalis
    Analyzing Dynamic Operational Conditions of Limb Prosthetic Sockets with a Mechatronics-Twin Framework2022In: Applied Sciences, E-ISSN 2076-3417, Vol. 12, no 3, p. 986-986Article in journal (Refereed)
    Abstract [en]

    Lower limb prostheses offer a solution to restore the ambulation and self-esteem of amputees. One key component is the prosthetic socket that serves as the interface between prosthetic device and amputee stump and thereby has a wide range of impacts on efficient fitting, appropriate load transmission, operational stability, and control. For the design and optimization of a prosthetic socket, an understanding of the actual intra-socket operational conditions becomes therefore necessary. This is however a difficult task due to the inherent complexity and restricted observability of socket operation. In this study, an innovative mechatronics-twin framework that integrates advanced biomechanical models and simulations with physical prototyping and dynamic operation testing for effective exploration of operational behaviors of prosthetic sockets with amputees is proposed. Within this framework, a specific Stewart manipulator is developed to enable dynamic operation testing, in particular for a well-managed generation of dynamic intra-socket loads and behaviors that are otherwise difficult to observe or realize with the real amputees. A combination of deep learning and Bayesian Inference algorithms is then employed for analyzing the intra-socket load conditions and revealing possible anomalous. 

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