Citation Top 10 Articles in the CTT journal
Part I: Control Theory and Core Algorithms
2. Learning-based adaptive optimal output regulation of linear and nonlinear systems: an overview
Cite this article: Gao, W., Jiang, ZP. Learning-based adaptive optimal output regulation of linear and nonlinear systems: an overview. Control Theory Technol. 20, 1–19 (2022).
https://doi.org/10.1007/s11768-022-00081-3
3. Safety stabilization of switched systems with unstable subsystems
*4. ADRC in output and error form: connection, equivalence, performance
Abstract: In this work, we investigate two specific linear ADRC structures, namely output- and error-based. The former is considered a “standard” version of ADRC, a title obtained primarily thanks to its simplicity and effectiveness, which have spurred its adoption across multiple industries. The latter is found to be especially appealing to practitioners as its feedback error-driven structure bares similarities to conventional control solutions, like PI and PID. In this paper, we describe newly found connections between the two considered ADRC structures, which allowed us to formally establish conditions for their equivalence. Furthermore, the conducted comprehensive performance comparison between output- and error-based ADRCs has facilitated the identification of specific modules within them, which can now be conveniently used as building blocks, thus aiding the control designers in customizing ADRC-based solutions and making them most suitable for their applications.
Cite this article: Madonski, R., Herbst, G. & Stankovic, M. ADRC in output and error form: connection, equivalence, performance. Control Theory Technol. 21, 56–71 (2023).
https://doi.org/10.1007/s11768-023-00129-y
5. Consensus control of feedforward nonlinear multi-agent systems: a time-varying gain method
Cite this article: Li, H., Zhang, X. & Pan, W. Consensus control of feedforward nonlinear multi-agent systems: a time-varying gain method. Control Theory Technol. 20, 46–53 (2022).
https://doi.org/10.1007/s11768-022-00083-1
6. Design of semi-tensor product-based kernel function for SVM nonlinear classification
Cite this article: Xue, S., Zhang, L. & Zhu, Z. Design of semi-tensor product-based kernel function for SVM nonlinear classification. Control Theory Technol. 20, 456–464 (2022).
https://doi.org/10.1007/s11768-022-00120-z
*10. On the notions of normality, locality, and operational stability in ADRC
Abstract: Treating plant dynamics as an ideal integrator chain disturbed by the total disturbance is the hallmark of active disturbance rejection control (ADRC). To interpret its effectiveness and success, to explain why so many vastly different dynamic systems can be treated in this manner, and to answer why a detailed, accurate, and global mathematical model is unnecessary, is the target of this paper. Driven by a motivating example, the notions of normality and locality are introduced. Normality shows that, in ADRC, the plant is normalized to an integrator chain, which is called local nominal model and locally describes the plant’s frequency response in the neighborhood of the expected gain crossover frequency. Locality interprets why ADRC can design the controller only with the local information of the plant. With normality and locality, ADRC can be effective and robust, and obtain operational stability discussed by T. S. Tsien. Then viewing proportional-integral-derivative (PID) control as a low-frequency approximation of second-order linear ADRC, the above results are extended to PID control. A controller design framework is proposed to obtain the controller in three steps: (1) choose an integrator chain as the local nominal model of the plant; (2) select a controller family corresponding to the local nominal model; and (3) tune the controller to guarantee the gain crossover frequency specification. The second-order linear ADRC and the PID control are two special cases of the framework.
Part II: Engineering Applications & Practical Problems
1. A hybrid genetic algorithm for the electric vehicle routing problem with time windows
Cite this article: Liu, Q., Xu, P., Wu, Y. et al. A hybrid genetic algorithm for the electric vehicle routing problem with time windows. Control Theory Technol. 20, 279–286 (2022).
https://doi.org/10.1007/s11768-022-00091-1
7. A new path planning method for bevel-tip flexible needle insertion in 3D space with multiple targets and obstacles
Cite this article: Tan, Z., Zhang, D., Liang, Hg. et al. A new path planning method for bevel-tip flexible needle insertion in 3D space with multiple targets and obstacles. Control Theory Technol. 20, 525–535 (2022).
https://doi.org/10.1007/s11768-022-00113-y
8. Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution
Cite this article: Xu, F., Tsunogawa, H., Kako, J. et al. Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution. Control Theory Technol. 20, 145–160 (2022).
https://doi.org/10.1007/s11768-022-00086-y
*9. Modular supervisory control for multi-floor manufacturing processes
Abstract: Due to space availability limitations and high land costs, there is an increasing development of multi-floor manufacturing (MFM) systems in urban and industrial areas. The problem of coordination in a multi-floor manufacturing process, in the Ramadge Wonham framework, is introduced. The manufacturing chain of each floor and the elevator system are modeled in the form of finite deterministic automata. The models of the multi-floor manufacturing process are parametric with respect to the number of floors and the number of manufacturing machines on each floor. The coordination desired performance is formulated in the form of desired regular languages in analytic forms. The languages are realized by appropriate supervisors in the form of finite deterministic automata. The models of the supervisors are also parametric with respect to the number of floors and the number of manufacturing machines on each floor. The total control of the coordination of the multi-floor manufacturing process is accomplished via a modular supervisory control architecture. The complexity of the supervisors as well as the complexity of the total modular supervisory architecture are determined in analytic forms with respect to the number of floors and the number of manufacturing machines on each floor. The special case of a two floor manufacturing process is presented as an illustrative example.
Cite this article: Koumboulis, F.N., Fragkoulis, D.G. & Michos, A.A. Modular supervisory control for multi-floor manufacturing processes. Control Theory Technol. 21, 148–160 (2023).
https://doi.org/10.1007/s11768-023-00135-0
