Bo Yu's research while affiliated with University of Michigan and other places

This paper investigates robust H2 and H∞ step tracking control methods for networked control systems subject to random time delays modeled by Markov chains. To make full use of the delay information, the proposed two-mode dependent output feedback controller depends on both sensor-to-controller and controller-to-actuator delays. To actively compensate for the controller-to-actuator delays, we propose the “send all, apply one” scheme: Sending a sequence of control signals, then at the actuator/plant node, applying the appropriate control signal according to the actual controller-to-actuator delay. Using the augmentation method, the resulting closed-loop system can be formulated as a discrete-time Markovian jump linear system. The H2 and H∞ step tracking problems are tackled by solving a set of linear matrix inequalities with nonconvex constraints. Both numerical simulations and experiments on a networked dc motor system are conducted to illustrate the effectiveness of the proposed methods.

This paper considers the discrete-time ${\cal H}_2$ **image** output tracking control of wireless networked control systems (NCSs) where the time delays are modeled as Markov chains. Output tracking control can find many applications in industry. In order to reduce the conservativeness and achieve better performance, the designed state feedback controller is dependent on available sensor-to-controller and controller-to-actuator delays. Then, the formulated closed-loop system is a special jump linear system governed by interdependent parameters of Markov chains and the condition for stochastic stability is proposed. By generalization of the ${\cal H}_2$ **image** norm definition, new relation of the ${\cal H}_2$ **image** norm for the special system is derived in terms of state space form. The condition of a set of linear matrix inequalities (LMIs) with nonconvex constraints is given to solve the ${\cal H}_2$ **image** output tracking control problem. Simulation examples are provided to illustrate the effectiveness of the method. Copyright © 2009 John Wiley & Sons, Ltd.

This paper is concerned with the design of networked control systems using the modified generalized predictive control (M-GPC) method. Both sensor-to-controller (S-C) and controller-to-actuator (C-A) network-induced delays are modeled by two Markov chains. M-GPC uses the available output and prediction control information at the controller node to obtain the future control sequences. Different from the conventional generalized predictive control in which only the first element in control sequences is used, M-GPC employs the whole control sequences to compensate for the time delays in S-C and C-A links. The closed-loop system is further formulated as a special jump linear system. The sufficient and necessary condition to guarantee the stochastic stability is derived. Simulation studies and experimental tests for an experimental hydraulic position control system are presented to verify the effectiveness of the proposed method. [DOI: 10.1115/1.4003385]

This paper is concerned with the two-mode-dependent robust control synthesis of networked control systems where random delays existing in both forward controller-to-actuator (C–A) and feedback sensor-to-controller (S–C) communication links are modeled as Markov chains. The output feedback controller is designed to depend on the current S–C delay and the previous C–A delay. Then, the closed-loop system is formulated as a special jump linear system. The generalized definitions of the H2H2 and H∞H∞ norms for such underlying special systems are proposed. Further, the two-mode-dependent robust H2H2 and robust mixed H2/H∞H2/H∞ control design methods for NCSs are developed. The design examples illustrate the effectiveness of the proposed methods.

This paper presents the robust ¿₂ and ¿_¿ step tracking control methods for networked control systems subject to random time delays modeled by Markov chains. To make full use of the delay information, the static output feedback controller to be designed is dependent on both sensor-to-controller and controller-to-actuator delays. Using the augmentation method, the resulting closed-loop system belongs to the discrete-time Markovian jump linear systems (MJLSs). The ¿₂ and ¿_¿ step tracking problems are formulated in the form of a set of linear matrix inequalities (LMIs) with nonconvex constraints, which can be solved by iterative convex optimization methods. Design examples on a VTOL helicopter are provided to illustrate the effectiveness of the proposed methods.

This paper studies the parameter identification problem of Hammerstein output-error systems with two-segment nonlinearities. New switching sequences are proposed to reduce the number of parameters to be estimated. A stochastic gradient identification algorithm together with the convergence analysis is further developed. A varying forgetting factor scheme is used to make a tradeoff between the convergence rate and estimation accuracy. Both numerical simulation results and applications to distillation columns are provided to show the effectiveness of the proposed method.

This note investigates the output feedback stabilization of networked control systems (NCSs). The sensor-to-controller (S-C) and controller-to-actuator (C-A) random network-induced delays are modeled as Markov chains. The focus is on the design of a two-mode-dependent controller that depends on not only the current S-C delay but also the most recent available C-A delay at the controller node. The resulting closed-loop system is transformed to a special discrete-time jump linear system. Then, the sufficient and necessary conditions for the stochastic stability are established. Further, the output feedback controller is designed via the iterative linear matrix inequality (LMI) approach. Simulation examples illustrate the effectiveness of the proposed method.

This paper considers the mixed H₂/H_infin control of networked control systems where random time delays existing in sensor-to-controller (S-C) and controller-to-actuator (C-A) links are modeled by Markov chains. The designed output feedback controller is two-mode-dependent, which depends on the available S-C and C-A delay information. The closed-loop system is formulated as a special jump linear system. The definitions of the H₂ and H_infin norms are further proposed to reflect the special characteristics of the system. The H₂ and mixed H₂/H_infin control problems are solved via the linear matrix inequality (LMI) optimization approaches. Simulation examples illustrate the effectiveness of the proposed methods.