State Estimation and Sliding-Mode Control of Markovian Jump Singular Systems

By Ligang Wu, Peng Shi, Huijun Gao. IEEE Transactions on Automatic Control, 2010

647 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/e01da39b7632acaaf694ddae61b20a16731a74f0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Peng Shi
2. Shaocheng Tong
3. Hongyi Li
4. Yong-ming Li
5. Huijun Gao
6. Fanbiao Li
7. Ligang Wu
8. Hao Shen

Citations in the corpus

• Adaptive Fuzzy Control Design for Stochastic Nonlinear Switched Systems With Arbitrary Switchings and Unmodeled Dynamics (2017, background): “Consequently, the control design of nonlinear stochastic systems has become an active topic and many results in this field have been reported, including sliding-model control [1], [2], backstepping control [3], [4], Takagi–Sugeno fuzzy control [5], [6], and so on.”
• Dissipativity-Based Sliding Mode Control of Switched Stochastic Systems (2013, background): “Being an effective robust control strategy, sliding mode control (SMC) has found widespread applications in various complex dynamical systems, such as time-delay systems [7], [11], [20], [22], stochastic systems [10], and Markovian jumping systems [13], [18].”
• A survey on Markovian jump systems: Modeling and design (2015, methodology): “SMC of MJSs with actuator nonlinearities was considered in [100]; Ma and Boukas proposed a singular system approach to robust SMC for uncertain MJSs [102]; SMC problem for Markovian jump singular stochastic hybrid systems was solved in [76]; the problems of state estimation and SMC of Markovian jump”
• Observer‐based H ∞  control on nonhomogeneous Markov jump systems with nonlinear input (2014, background): “It is a fact that transition probability plays an important role in the performance of such systems, under the assumption that the transition probabilities of MJSs are time invariant, some problems have been studied, such as system analysis [2, 3], stochastic stability and stabilization [4], control”
• A novel adaptive high-order sliding mode control based on integral sliding mode (2014, background): “Subsequently, ISMC has also been investigated in [19,20] for the Markovian jump singular systems without and with the time-delay, respectively.”
• Fault-Tolerant Control for Nonlinear Markovian Jump Systems via Proportional and Derivative Sliding Mode Observer Technique (2011, background): “Over the past few decades, a variety of SMC schemes have been developed for various types of dynamical systems, such as Markovian jump systems [21], [12], hybrid systems [27], piecewise systems [30], singular systems [22], [21], and so on.”
• Stabilisation of hybrid stochastic differential equations by feedback control based on discrete-time observations of state and mode (2017, background): “[5, 11, 13, 19, 20]), Mao proposed in [10] to design a feedback control u(x(δ(t, t0, τ)), r(t), t) based on the discrete-time observations of the state in order to make the controlled system”
• SMC Design for Robust Stabilization of Nonlinear Markovian Jump Singular Systems (2018, methodology): “Proof: Choose a stochastic Lyapunov function as V (t) = x (t)(E p Pp + Sp Q T p Rp )Ep x(t), and follow the similar line of the proofs in [24]–[26], we can readily obtain the result in Theorem 1.”
• H ∞  sliding mode observer design for a class of nonlinear discrete time‐delay systems: A delay‐fractioning approach (2012, neutral): “[1,6,7,12,14–17,24,26–30,35,40].”