The Experts below are selected from a list of 194178 Experts worldwide ranked by ideXlab platform
Chih Chiang Chen - One of the best experts on this subject based on the ideXlab platform.
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second order sliding mode controller design with output Constraint
Automatica, 2020Co-Authors: Shihong Ding, Ju H Park, Chih Chiang ChenAbstract:Abstract The output Constraints widely exist in the physical systems and severely affect the performance of the closed-loop system. This paper considers the second-order sliding mode controller design subject to an output Constraint. By constructing a new barrier Lyapunov function and applying the technique of adding a power integrator, a novel second-order sliding mode control algorithm, which can be used to deal with the output Constraint Problem, has been developed. The proposed sliding mode algorithm enables the output variable not to violate the boundary of the Constraint region. Meanwhile, it has been shown that under the output Constraint the sliding variable can still be stabilized to zero in a finite time. Finally, an academic example is given to verify the feasibility of the proposed second-order sliding mode algorithm.
Jiuxiang Dong - One of the best experts on this subject based on the ideXlab platform.
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adaptive neural network based control of uncertain nonlinear systems with time varying full state Constraints and input Constraint
Neurocomputing, 2019Co-Authors: Jiuxiang DongAbstract:Abstract This paper investigates the Problem of state-Constraint adaptive neural network-based tracking control for a class of nonlinear systems with input saturation Constraint. The considered systems are with uncertain nonlinearities which are not required to be globally Lipschitz or be with a prior knowledge of the structure. To facilitate the stability analysis, radial basis function neural networks (RBFNNs) are first utilized to approximate the unknown nonlinear terms. The Constraint Problem of input saturation often appears in the control system. To solve above issue, a novel adaptive control scheme is proposed with the help of an augmented function with auxiliary control signal, which ensures that all the closed-loop signals are semi-globally uniformly ultimately bounded. On the other hand, to guarantee better transient performance under input saturation, an improved barrier Lyapunov function with time-varying barriers is developed, which makes the tracking errors preserve within the specified Constraint bounds. Simulation results are given to demonstrate the effectiveness of the proposed approach.
Shihong Ding - One of the best experts on this subject based on the ideXlab platform.
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second order sliding mode control design subject to an asymmetric output Constraint
IEEE Transactions on Circuits and Systems Ii-express Briefs, 2021Co-Authors: Lu Liu, Shihong DingAbstract:In this brief, a novel second-order sliding mode (SOSM) control method is developed to solve the asymmetric output Constraint Problem by using a power integrator and barrier Lyapunov function (BLF). The new BLF is first constructed based on the asymmetric Constraint condition. A novel SOSM algorithm is then constructed for the nonlinear systems with an asymmetric output Constraint. Under the proposed SOSM algorithm, the output of the resulting closed-loop system will never escape from the asymmetric Constraint. Finally, a pendulum system is adopted to verify the validity of the theoretical results.
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second order sliding mode controller design with output Constraint
Automatica, 2020Co-Authors: Shihong Ding, Ju H Park, Chih Chiang ChenAbstract:Abstract The output Constraints widely exist in the physical systems and severely affect the performance of the closed-loop system. This paper considers the second-order sliding mode controller design subject to an output Constraint. By constructing a new barrier Lyapunov function and applying the technique of adding a power integrator, a novel second-order sliding mode control algorithm, which can be used to deal with the output Constraint Problem, has been developed. The proposed sliding mode algorithm enables the output variable not to violate the boundary of the Constraint region. Meanwhile, it has been shown that under the output Constraint the sliding variable can still be stabilized to zero in a finite time. Finally, an academic example is given to verify the feasibility of the proposed second-order sliding mode algorithm.
Yuan Zhou - One of the best experts on this subject based on the ideXlab platform.
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parameterized algorithms for Constraint satisfaction Problems above average with global cardinality Constraints
Symposium on Discrete Algorithms, 2017Co-Authors: Xue Chen, Yuan ZhouAbstract:Given a Constraint satisfaction Problem (CSP) on n variables, x1, x2, . . . , xn ∈ {±1}, and m Constraints, a global cardinality Constraint has the form of Σni = 1 xi = (1 − 2p)n, where p ∈ (Ω(1), 1 − Ω(1)) and pn is an integer. Let AV G be the expected number of Constraints satisfied by randomly choosing an assignment to x1, x2, . . . , xn, complying with the global cardinality Constraint. The CSP above average with the global cardinality Constraint Problem asks whether there is an assignment (complying with the cardinality Constraint) that satisfies more than (AV G + t) Constraints, where t is an input parameter. In this paper, we present an algorithm that finds a valid assignment satisfying more than (AV G + t) Constraints (if there exists one) in time (2o(t2) + no(d)). Therefore, the CSP above average with the global cardinality Constraint Problem is fixed-parameter tractable.
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parameterized algorithms for Constraint satisfaction Problems above average with global cardinality Constraints
arXiv: Data Structures and Algorithms, 2015Co-Authors: Xue Chen, Yuan ZhouAbstract:Given a Constraint satisfaction Problem (CSP) on $n$ variables, $x_1, x_2, \dots, x_n \in \{\pm 1\}$, and $m$ Constraints, a global cardinality Constraint has the form of $\sum_{i = 1}^{n} x_i = (1-2p)n$, where $p \in (\Omega(1), 1 - \Omega(1))$ and $pn$ is an integer. Let $AVG$ be the expected number of Constraints satisfied by randomly choosing an assignment to $x_1, x_2, \dots, x_n$, complying with the global cardinality Constraint. The CSP above average with the global cardinality Constraint Problem asks whether there is an assignment (complying with the cardinality Constraint) that satisfies more than $(AVG+t)$ Constraints, where $t$ is an input parameter. In this paper, we present an algorithm that finds a valid assignment satisfying more than $(AVG+t)$ Constraints (if there exists one) in time $(2^{O(t^2)} + n^{O(d)})$. Therefore, the CSP above average with the global cardinality Constraint Problem is fixed-parameter tractable.
Ju H Park - One of the best experts on this subject based on the ideXlab platform.
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second order sliding mode controller design with output Constraint
Automatica, 2020Co-Authors: Shihong Ding, Ju H Park, Chih Chiang ChenAbstract:Abstract The output Constraints widely exist in the physical systems and severely affect the performance of the closed-loop system. This paper considers the second-order sliding mode controller design subject to an output Constraint. By constructing a new barrier Lyapunov function and applying the technique of adding a power integrator, a novel second-order sliding mode control algorithm, which can be used to deal with the output Constraint Problem, has been developed. The proposed sliding mode algorithm enables the output variable not to violate the boundary of the Constraint region. Meanwhile, it has been shown that under the output Constraint the sliding variable can still be stabilized to zero in a finite time. Finally, an academic example is given to verify the feasibility of the proposed second-order sliding mode algorithm.
