Mixed sliding mode fuzzy control for discrete‐time non‐linear stochastic systems subject to variance and passivity constraints

Chang, Wen‐Jer; Chen, Po-Hsun; Ku, Cheung-Chieh

doi:10.1049/iet-cta.2015.0221

Cited by 12 publications

(8 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, the signal of external disturbance is considered as a stochastic behavior. Solving the control problem of stochastic external disturbance, several papers have already developed the control theory by combining the covariance control theory and passivity theory successfully [19,20]. In addition to suppressing the effect of stochastic behaviors by the variance constraint, the passivity theory is also applied to dissipate its energy.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to suppressing the effect of stochastic behaviors by the variance constraint, the passivity theory is also applied to dissipate its energy. In [19,20], it can be known that the controller designed by the combined theory presents a better performance when controlling the nonlinear stochastic system than the method only applying the passivity theory. Due to this reason, the covariance control theory and passivity theory are both applied in this paper.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pole Location and Input Constrained Robust Fuzzy Control for T-S Fuzzy Models Subject to Passivity and Variance Requirements

Qiao¹,

Chang

Lin

et al. 2021

Processes

View full text Add to dashboard Cite

In this paper, a robust fuzzy controller for stochastic nonlinear systems subject to multiple performance constraints is discussed. To solve the problem of stochastic behaviors in nonlinear systems, the covariance control theory and passive control theory are applied based on the concept of energy. Additionally, the pole placement method is considered for the better transient behaviors of the system responses. However, it is known that the control inputs and maximum overshoot of the system responses may become bigger at the same time when the settling time or converge rate of the system is required to be faster. Due to this reason, the input constraint is also considered in the fuzzy controller design method to limit the value of the control gain. Moreover, an effective robust control method is applied to deal with the perturbation of the nonlinear systems. Based on the above performance constraints, the sufficient conditions can be obtained to achieve the stability in the sense of mean square and the multi-performance requirements. Finally, simulation results of the nonlinear synchronous generator system are presented to verify the feasibility and efficiency of the proposed control method.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pole Location and Input Constrained Robust Fuzzy Control for T-S Fuzzy Models Subject to Passivity and Variance Requirements

Qiao¹,

Chang

Lin

et al. 2021

Processes

View full text Add to dashboard Cite

show abstract

“…Chang et al [18] presented a novel multiobjective sliding mode fuzzy control technique for a class of discrete-time nonlinear stochastic systems, such that the closed-loop system achieves passivity constraint and individual state variance constraints, simultaneously. Also, the problem of steering a linear dynamical system with complete state observation from an initial Gaussian distribution in state space to a final one with minimum energy control was addressed in Chen et al [19].…”

Section: Introductionmentioning

confidence: 99%

A Covariance Feedback Approach to Covariance Control of Nonlinear Stochastic Systems

2020

View full text Add to dashboard Cite

In this paper, the covariance control algorithm for nonlinear stochastic systems using covariance feedback is studied. Covariance control of nonlinear systems scenario involves the theory of covariance control based on the idea of the covariance feedback. Therefore, the proposed covariance control algorithm is derived for our case, firstly by applying the covariance control method and linear approximation of nonlinear systems, and then it is achieved by adopting this method for a class of nonlinear stochastic systems by using feedback linearization idea and a covariance feedback controller. The effectiveness of the proposed covariance feedback algorithm is studied using numerous simulations concerning different nonlinear case studies.

show abstract

“…Meanwhile, fuzzy control was added to the proposed algorithm. Fuzzy control [26,27,28] is fast and highly stable and can adjust the gain of the control algorithm according to the needs of the ILC to improve the convergence speed and tracking the accuracy of the ILC. The proposed scheme combines the PID-ILC with fuzzy control, and fuzzy control optimizes the parameters of the ILC law to find the optimal gain so that the algorithm can learn faster, and the system can accurately converge to the desired path with fewer iterations.…”

Section: Introductionmentioning

confidence: 99%

Novel Fuzzy PID-Type Iterative Learning Control for Quadrotor UAV

Dong

2018

Sensors

View full text Add to dashboard Cite

Due to the under-actuated and strong coupling characteristics of quadrotor aircraft, traditional trajectory tracking methods have low control precision, and poor anti-interference ability. A novel fuzzy proportional-interactive-derivative (PID)-type iterative learning control (ILC) was designed for a quadrotor unmanned aerial vehicle (UAV). The control method combined PID-ILC control and fuzzy control, so it inherited the robustness to disturbances and system model uncertainties of the ILC control. A new control law based on the PID-ILC algorithm was introduced to solve the problem of chattering caused by an external disturbance in the ILC control alone. Fuzzy control was used to set the PID parameters of three learning gain matrices to restrain the influence of uncertain factors on the system and improve the control precision. The system stability with the new design was verified using Lyapunov stability theory. The Gazebo simulation showed that the proposed design method creates effective ILC controllers for quadrotor aircraft.

show abstract

Mixed sliding mode fuzzy control for discrete‐time non‐linear stochastic systems subject to variance and passivity constraints

Cited by 12 publications

References 39 publications

Pole Location and Input Constrained Robust Fuzzy Control for T-S Fuzzy Models Subject to Passivity and Variance Requirements

Pole Location and Input Constrained Robust Fuzzy Control for T-S Fuzzy Models Subject to Passivity and Variance Requirements

A Covariance Feedback Approach to Covariance Control of Nonlinear Stochastic Systems

Novel Fuzzy PID-Type Iterative Learning Control for Quadrotor UAV

Contact Info

Product

Resources

About