Predictive control strategy is employed to improve the performance of sliding mode control method in this paper. A multi-step sliding mode prediction model (MSMPM) is constructed at first. The MSMPM includes the funct...
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Predictive control strategy is employed to improve the performance of sliding mode control method in this paper. A multi-step sliding mode prediction model (MSMPM) is constructed at first. The MSMPM includes the function of compensating for system parameter perturbation and external disturbance. Based on the MSMPM and applied receding horizon optimization, a novel sliding mode control law for a class of discrete-time uncertain systems is obtained. Under the control law, closed-loop systems have strong robustness to matched or unmatched uncertainty, and chattering has been eliminated. The results of a inverted pendulum system verify the efficacy of the proposed algorithm.
Based on a creative sliding mode prediction model, a novel sliding mode observer (SMO) design method for discrete-time nonlinear uncertain systems is presented in this paper. By combining feedback correction approach,...
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Based on a creative sliding mode prediction model, a novel sliding mode observer (SMO) design method for discrete-time nonlinear uncertain systems is presented in this paper. By combining feedback correction approach, the observer states can close to system states as exactly as possible, and chattering is eliminate simultaneously. Because of the employing of receding horizon optimization technique, the observer compensatory term can be optimized continuously and on-line. The proposed method guarantees the SMO is robustly stable. The satisfying properties of the SMO are verified by a numerical example.
A kind of output-feedback networked control system was addressed to overcome the defect of feedback lag introduced by the network usage in the networked control system. By introducing a plant model and a buffer into t...
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A kind of output-feedback networked control system was addressed to overcome the defect of feedback lag introduced by the network usage in the networked control system. By introducing a plant model and a buffer into this system, when feedback was available, this system could compute the control output and update the plant model with the plant output, otherwise, compute the control output based on the plant model output instead of plant output. On the condition that the plant was SISO and the error between plant and model existed, the necessary and sufficient condition for closed-loop stability of this system was derived. Simulation indicates that the range of model error within which this system is stable is easy to be derived by using this necessary and sufficient condition.
An Online Kernel Learning based Adaptive control (OKL-AC) framework for discrete-time affine nonlinear systems is presented in this paper. A sparsity strategy is proposed to control the complexity of OKL identificatio...
An Online Kernel Learning based Adaptive control (OKL-AC) framework for discrete-time affine nonlinear systems is presented in this paper. A sparsity strategy is proposed to control the complexity of OKL identification model, meanwhile to make a trade-off between the demanded tracking precision and the complexity of the control law. The forward increasing and backward decreasing learning stages are performed, both incorporating efficient recursive updating algorithms. Owing to these advantages, the adaptive control law based on the OKL identification model is easily obtained and can be efficiently updated. Numerical simulations show that the proposed simple OKL-AC strategy has satisfactory performance, including good tracking performance and fast learning ability, in both deterministic and stochastic environments.
A novel online identification method is developed for nonlinear multi-input multi-output process modeling issue, which is based on kernel learning framework and named as online kernel learning (OKL) algorithm in this ...
A novel online identification method is developed for nonlinear multi-input multi-output process modeling issue, which is based on kernel learning framework and named as online kernel learning (OKL) algorithm in this paper. This proposed approach can adaptively control its complexity and thus acquire controlled generalization ability. The OKL algorithm performs first a forward increasing for incorporating a “new” online sample and then a backward decreasing for pruning an “old” one, both in a recursive manner. Furthermore, the prior knowledge about process can be easily integrated into the OKL scheme to improve its performance. Numerical simulations on a fed-batch penicillin fermentation process show that the proposed OKL algorithm can learn adaptively the dynamics of the process using relatively small samples, indicating the OKL is an attractive online modeling method for fermentation process.
This note concerns the delay-dependent robust stability analysis for uncertain singular time-delay systems. The parameter uncertainty is assumed to be norm-bounded and possibly time-varying, while the time delay consi...
This note concerns the delay-dependent robust stability analysis for uncertain singular time-delay systems. The parameter uncertainty is assumed to be norm-bounded and possibly time-varying, while the time delay considered here is assumed to be constant but unknown. By using a new Lyapunov-krasovskii functional which splits the whole delay interval into two subintervals and defines a different energy function on each subinterval, some delay-dependent conditions are presented for the singular time-delay system to be regular, impulse free and robustly stable. The obtained delay-dependent criteria are effective and less conservative than previous ones, which are illustrated by numerical examples.
This note is concerned with the absolute stability analysis for time-delay Lurie control systems with nonlinearity located in an infinite sector and finite one. By using a new Lyapunov-Krasovskii functional that split...
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This note is concerned with the absolute stability analysis for time-delay Lurie control systems with nonlinearity located in an infinite sector and finite one. By using a new Lyapunov-Krasovskii functional that splits the whole delay interval into two subintervals and defines a different energy function on each subinterval and introducing some free-weighting matrices, some new delay-dependent robustly absolute stability criteria are presented in terms of strict linear matrix inequalities (LMIs). The obtained delay-dependent criteria are less conservative than previous ones, as are illustrated by numerical examples.
A novel mixed integer linear programming (NMILP) model for detection of gross errors is presented in this paper. Yamamura et al.(1988) designed a model for detection of gross errors and data reconciliation based on Ak...
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A novel mixed integer linear programming (NMILP) model for detection of gross errors is presented in this paper. Yamamura et al.(1988) designed a model for detection of gross errors and data reconciliation based on Akaike information cri- terion (AIC). But much computational cost is needed due to its combinational nature. A mixed integer linear programming (MILP) approach was performed to reduce the computational cost and enhance the robustness. But it loses the super performance of maximum likelihood estimation. To reduce the computational cost and have the merit of maximum likelihood estimation, the simultaneous data reconciliation method in an MILP framework is decomposed and replaced by an NMILP subproblem and a quadratic programming (QP) or a least squares estimation (LSE) subproblem. Simulation result of an industrial case shows the high efficiency of the method.
A new design approach to delay-dependent robust stabilization and robust H ∞ control for a class of uncertain time-delay systems is provided in this paper. The sufficient conditions for delay-dependent robust stabili...
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A new design approach to delay-dependent robust stabilization and robust H ∞ control for a class of uncertain time-delay systems is provided in this paper. The sufficient conditions for delay-dependent robust stabilization and robust H ∞ control are derived based on a new state transformation and given in terms of linear matrix inequalities (LMI). Numerical examples are presented to show that the proposed results can be less conservative and can be used to deal with not only small but also large delay systems.
The problem of robust stability analysis for uncertain discrete singular time-delay systems is investigated in this paper. By decomposing the nominal system into slow and fast subsystems, a linear matrix inequality (L...
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The problem of robust stability analysis for uncertain discrete singular time-delay systems is investigated in this paper. By decomposing the nominal system into slow and fast subsystems, a linear matrix inequality (LMI) condition is proposed for a discrete singular time-delay system to be regular, causal and stable. Based on this, an LMI criterion is obtained for robust stability of an uncertain discrete singular time-delay system. Two numerical examples are provided to demonstrate the feasibility of the proposed approach.
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