Fuzzy Control Policy Research Articles

Adaptive Fuzzy Fault-Tolerant Tracking Control for Partially Unknown Systems With Actuator Faults via Integral Reinforcement Learning Method

In this paper, a fuzzy reinforcement learning (RL)-based tracking control algorithm is first proposed for partially unknown systems with actuator faults. Based on Takagi–Sugeno fuzzy model, a novel fuzzy-augmented tracking dynamic is developed and the overall fuzzy control policy with corresponding performance index is designed, where four kinds of actuator faults, including actuator loss of effectiveness and bias fault, are considered. Combining the RL technique and fuzzy-augmented model, the new fuzzy integral RL-based fault-tolerant control algorithm is designed, and it runs in real time for the system with actuator faults. The dynamic matrices can be partially unknown and the online algorithm requires less information transmissions or computational load along with the learning process. Under the overall fuzzy fault-tolerant policy, the tracking objective is achieved and the stability is proven by Lyapunov theory. Finally, the applications in the single-link robot arm system and the complex pitch-rate control problem of F-16 fighter aircraft demonstrate the effectiveness of the proposed method.

Tracking control optimization scheme for a class of partially unknown fuzzy systems by using integral reinforcement learning architecture

In this paper, a novel fuzzy integral reinforcement learning (RL) based tracking control algorithm is first proposed for partially unknown fuzzy systems. Firstly, by using the precompensation and augmentation techniques, a new augmented fuzzy tracking system is constructed by combining the fuzzy logic model and desired reference trajectory, where the solution of actual working feedback control policy is converted into a virtual optimal control problem. Secondly, to overcome the requirements of exact original system information, the integral RL technique is utilized to learn the fuzzy control solution, which relaxes the repeatedly transmissions of system matrices during the solving process. Thirdly, compared with the existing standard solution, some crucial and strict aforementioned assumptions are removed and the system can be partially unknown by using the designed algorithm. Besides, under the novel fuzzy control policy, the tracking objective is achieved and the stability is guaranteed by Lyapunov theory. Finally, the developed integral RL tracking control algorithm for partially unknown systems is applied in a mechanical system and the simulation results demonstrate the effectiveness of the proposed new method.

Fuzzy Disturbance Rejection Control of Two Link Robot Manipulator

It is well known that robotic manipulators are nonlinear coupling dynamic systems. The control strategy is fuzzy logic technique to cope with the model parameter disturbances. The simulation is implemented to verify the effectiveness of the proposed fuzzy control policy. From the simulation results, strong robustness, fast response, good disturbance rejection capability and good tracking capability can be obtained. It is also illustrated from simulation results that the proposed control technique is valid for the two-link robot manipulator.

Chaos suppression in a fractional order financial system using intelligent regrouping PSO based fractional fuzzy control policy in the presence of fractional Gaussian noise

Financial systems are known to have irregular and erratic fluctuations due to diverse influences and often result in economic crisis and huge financial losses. Recent models of financial systems show that they behave chaotically and have long range memory dependence. Mitigating these undesirable chaotic natures of financial systems by appropriate control policies is important in order to reduce investment risks and improve economic performance. In this paper, a fractional order fuzzy control policy is employed to suppress the chaotic dynamics of a representative chaotic fractional order financial system. An intelligent Regrouping Particle Swarm Optimization (Reg-PSO) is used to design the numeric weights of the control policy and the methodology is demonstrated by credible simulations. The designed fractional fuzzy control policies are shown to work well with respect to conventional fuzzy control policies in the presence of persistent and anti-persistent noise, which can be due to additional extraneous influences on the system.