Hybrid Adaptive Fuzzy Control Research Articles

A hybrid adaptive fuzzy control for a class of nonlinear MIMO systems

A hybrid indirect and direct adaptive fuzzy output tracking control schemes are developed for a class of nonlinear multiple-input-multiple-output (MIMO) systems. This hybrid control system consists of observer and other different control components. Using the state observer, it does not require the system states to be available for measurement. Assisted by observer-based state feedback control component, the adaptive fuzzy system plays a dominant role to maintain the closed-loop stability. Being the auxiliary compensation, H/sup /spl infin// control and sliding mode control are designed to suppress the influence of external disturbance and remove fuzzy approximation error, respectively. Thus, the system performance can be greatly improved. The simulation results demonstrate that the proposed hybrid fuzzy control system can guarantee the system stability and also maintain a good tracking performance.

Hybrid adaptive fuzzy controllers with application to robotic systems

This paper presents a hybrid adaptive fuzzy control (HAFC) design methodology, where the nonlinear system is controlled by a state feedback controller and an adaptive fuzzy controller. In the adaptive fuzzy controller, an adaptive law is developed to tune a robust gain of the sliding-mode controller (SMC) so as to cope with the uncertainties and model errors. The proposed design method is applied to investigate the position and tracking control of a two-link robot. A comparison between the proportional-derivative control, SMC, adaptive control and the proposed HAFC for a two-link robot is made. The simulation results demonstrate that, by using the HAFC, the system performances is improved and the system also exhibits stability and robustness.

Hybrid adaptive fuzzy identification and control of nonlinear systems

We present a combined direct and indirect adaptive control scheme for adjusting an adaptive fuzzy controller, and adaptive fuzzy identification model parameters. First, using adaptive fuzzy building blocks, with a common set of parameters, we design and study an adaptive controller and an adaptive identification model that have been proposed for a general class of uncertain structure nonlinear dynamic systems. We then propose a hybrid adaptive (HA) law for adjusting the parameters. The HA law utilizes two types of errors in the adaptive system, the tracking error and the modeling error. Performance analysis using a Lyapunov synthesis approach proves the superiority of the HA law over the direct adaptive (DA) method in terms of faster and improved tracking and parameter convergence. Furthermore, this is achieved at negligible increased implementation cost or computational complexity. We prove a theorem that shows the properties of this hybrid adaptive fuzzy control system, i.e., bounds for the integral of the squared errors, and the conditions under which these errors converge asymptotically to zero are obtained. Finally, we apply the hybrid adaptive fuzzy controller to control a chaotic system, and the inverted pendulum system.

Real-time hybrid adaptive fuzzy control of a SCARA robot

This paper presents the design, development and implementation of a Hybrid Adaptive Fuzzy Controller (HAFC) suitable for real-time industrial applications. The developed HAFC consists of a weighted combination of the Direct Adaptive Fuzzy Controller (DAFC) and Indirect Adaptive Fuzzy Controller (IAFC) and a gradually activated supervisory controller. The unique feature of the HAFC is that no mathematical models of the plant are required and the proposed controller is able to adaptively estimate the bound functions on-line, which are required for the determination of the supervisory controller. The supervisor controller in the sense that all signals are bounded guarantees global stability of the closed-loop system. The approach of rapid prototyping is employed to implement the HAFC so as to control a Selectively Compliance Assembly Robot Arm (SCARA) Robot in real time. Simulink, an interactive graphical software for simulating dynamic systems, is used to model, simulate and analyse the dynamic system. Once encouraging system performances are achieved, the HAFC is implemented in real-time through Real-Time Workshop (RTW). RTW generates C-codes from the Simulink block diagrams and in turn, the generated codes (object codes) are downloaded to the Texas Instruments TMS320C31 floating-point processor, together with the supporting files, for execution. The performance of the HAFC was found to be superior and it matches favourably the simulation results.

Hybrid adaptive fuzzy controllers of robot manipulators with bounds estimation

A hybrid adaptive fuzzy controller comprises a weighted combination of a direct and an indirect adaptive fuzzy controller, with a continuously switched supervisory controller. The direct and indirect adaptive fuzzy controllers allow fuzzy control rules and fuzzy descriptions to be incorporated respectively, to achieve better adaptation speed. In the design of the supervisory controller, the bounds of the unknown robot dynamics are not required; they are adaptively estimated in the Lyapunov sense. Experimental evaluation conducted on a SEIKO TT-3000 SCARA robot shows that the proposed hybrid adaptive fuzzy controller is robust and stable. It also outperforms both the direct and indirect adaptive fuzzy controllers designed by the authors in their previous work in terms of tracking accuracy and the magnitude of control torque required.