Article
Automation & Control Systems
Lin Lin, Jie Zhong, Shiyong Zhu, Jianquan Lu
Summary: In this study, a general partial synchronization method for a specific type of Boolean control networks is proposed for the first time, and it is achieved through sampled-data feedback control. Unlike previous synchronization methods, this approach requires the total number of synchronized nodes to exactly maintain a fixed value within a finite number of steps, and it eliminates the need for predetermined synchronized nodes.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Automation & Control Systems
Yutian Wang, Tengfei Liu, Zhong-Ping Jiang
Summary: This paper investigates the switching supervisory control problem for a class of nonlinear systems with nonlinearly parameterized uncertainties. The authors propose a scale-free hysteresis switching mechanism to select the estimator that best emulates the plant, even in the presence of non-exponential decay rates of the estimation errors. The proposed methodology is validated through constructive designs of estimators and control laws for a subclass of nonlinear uncertain systems. Practical convergence is guaranteed even in the case of parameter mismatch.
Article
Computer Science, Information Systems
Jia-Yao Jhang, Jenq-Lang Wu, Chee-Fai Yung
Summary: This paper addresses the event-triggered stabilization problem for nonlinear control-affine systems under state constraints. A strong control Lyapunov barrier function (CLBF) method is applied for constructing continuous state-constrained stabilizing controllers, and sufficient conditions for the existence of strong CLBFs are derived. With the obtained state-constrained feedback laws, a new event-triggered policy is proposed for reducing the number of communication events without the input-to-state stability (ISS) assumption. It is proved that the Zeno behavior is excluded under this event-triggered policy, that is, the inter-execution times are lower bounded away from zero.
Article
Automation & Control Systems
Pratik Bajaria, Amol Yerudkar, Luigi Glielmo, Carmen Del Vecchio, Yuhu Wu
Summary: This work discusses the optimal feedback control strategies for probabilistic Boolean control networks (PBCNs). Reinforcement learning (RL) is explored to minimize the model design efforts and regulate PBCNs with high complexities. A Q-learning random forest (QLRF) algorithm is proposed to design state feedback controllers for stabilizing the PBCNs at a given equilibrium point. Furthermore, a Lyapunov function is defined by adopting QLRF-stabilized closed-loop PBCNs, and a method to construct the same is presented. A novel self-triggered control (STC) strategy is proposed by utilizing such Lyapunov functions, where the controller is recomputed according to a triggering schedule, resulting in an optimal control strategy while maintaining the stability of the closed-loop PBCN. The results are verified through computer simulations.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Automation & Control Systems
Soufiane Yahyaoui, Mohamed Ouzahra
Summary: This work investigates quadratic bilinear optimal control, focusing on the case of infinite-time horizon. By introducing a class of bilinear systems and expressing the optimal control as a time-varying feedback control, the study demonstrates the strong stability of the obtained optimal control under a controllability inequality. The techniques rely on linear semigroup theory and optimality conditions, with applications to transport and heat equations discussed.
OPTIMAL CONTROL APPLICATIONS & METHODS
(2021)
Article
Computer Science, Information Systems
Yanan Pan, Shihua Fu, Jianjun Wang, Weihai Zhang
Summary: This paper investigates the optimal output tracking problem of Boolean control networks (BCNs) with a constant reference signal. The goal is to design an appropriate control scheme such that each state evolves to its optimal output tracking trajectory. The paper defines the optimal output tracking cycle (OTC) for each state, presents a method to compute cycles of certain length in the system, and derives the upper bound of the length for the optimal OTC. A constructive algorithm is then proposed to design state feedback controllers that enable the system to achieve optimal output tracking. An illustrative example is provided to demonstrate the obtained results.
INFORMATION SCIENCES
(2023)
Article
Automation & Control Systems
Jenq-Lang Wu
Summary: This article discusses the design of optimal static output feedback controllers for linear systems with priori structural constraints. The barrier method and Lagrange multiplier method are used to derive an algorithm for solving this type of problem, and the convergence of the algorithm is proven. Numerical examples are provided to validate the effectiveness of the proposed methodology.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Mathematics, Applied
Mathias Oster, Leon Sallandt, Reinhold Schneider
Summary: The paper discusses the common occurrence of controlling systems of ordinary differential equations in science and engineering. It focuses on the local optimal control problems in finite horizon control systems, with two methods being applied to solve these problems - policy iteration and open-loop control methods inspired by model predictive control. The use of low-rank hierarchical tensor product approximation and high-dimensional quadrature is also explored for high-dimensional systems, with linear error propagation demonstrated with numerical evidence on diffusion and Allen-Cahn equations.
SIAM JOURNAL ON SCIENTIFIC COMPUTING
(2022)
Article
Mathematics, Applied
Mathias Oster, Leon Sallandt, Reinhold Schneider
Summary: This paper discusses the finite horizon control problem in ordinary differential equation systems, and presents two different methods for solving it: policy iteration and model predictive control. For high-dimensional systems, low-rank tensor approximation and high-dimensional quadrature methods are used for numerical solution, and the effectiveness of the methods is verified through examples.
SIAM JOURNAL ON SCIENTIFIC COMPUTING
(2022)
Article
Automation & Control Systems
Rong Zhou, Caifen Fu, Wen Tan
Summary: LADRC treats the controlled plant as a cascaded integral model and uses an ESO to estimate the generalized disturbance for quick rejection. The structure can implement any linear finite-dimensional controller, making it a general-purposed control structure.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Automation & Control Systems
Zhen-Guo Liu, Hongli Dong, Weixing Chen, Weidong Zhang
Summary: This article investigates the adaptive regulation problem of uncertain delayed nonlinear systems and presents two unified adaptive control methods to achieve global asymptotic stability by introducing dynamic gain transformation and using homogeneous domination method.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Automation & Control Systems
Zhong-Xin Fan, Zhaoyi Li, Avizit Chandra Adhikary, Shihua Li, Rongjie Liu
Summary: This paper proposes a decentralized control method using inverse optimal strategy for interconnected systems with disturbances. By using disturbance observers and inverse optimal control, the system achieves robustness and optimality. The results are extended to n-dimensional interconnected systems with rigorous proof.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Automation & Control Systems
Yihang Kong, Xinghui Zhang, Enyong Liu, Ancai Zhang, Jianlong Qiu
Summary: This research addresses the problem of finite-time tracking error constrained control for a class of non-strict stochastic nonlinear systems with unknown time-varying powers and multiple power terms. Based on the conversion from constrained tracking error to an unconstrained signal with the same effect, by adopting the backstepping technique together with adaptive neural network control, a controller with upper and lower time-varying power bounds is designed to meet the prescribed performance control scheme in finite-time. Finally, two simulation examples are shown to verify the effectiveness of the commendatory control method.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Automation & Control Systems
Liang Liu, Jiaming Lu, Mengru Kong
Summary: This paper discusses exponentially stable problem for a class of stochastic strict feedforward nonlinear systems, presenting a parameter-dependent controller to handle nonlinearities. Through coordinate transformation and parameter selection, the proposed controller ensures stability of the closed-loop system as demonstrated by stochastic Lyapunov stability theory. Simulation results validate the efficiency of the proposed controller.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Automation & Control Systems
Yingzhe Jia, Biao Wang, Jun-E Feng, Daizhan Cheng
Summary: This article comprehensively investigates the set stabilization problem of Boolean control networks using output-feedback controllers. A novel method based on the state dynamics matrix is proposed, and the time-invariant output-feedback (TIOF) and time-variant output-feedback (TVOF) laws are derived. Necessary and sufficient conditions for both TIOF and TVOF stabilization are given, along with corresponding algorithms for designing stabilizers. Illustrative examples are provided to validate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
