Type-2 Fuzzy Control for a Flexible- joint Robot Using Voltage Control Strategy

Abstract

Type-1 fuzzy sets cannot fully handle the uncertainties. To overcome the problem, type-2 fuzzy sets have been proposed. The novelty of this paper is using interval type-2 fuzzy logic controller (IT2FLC) to control a flexible-joint robot with voltage control strategy. In order to take into account the whole robotic system including the dynamics of actuators and the robot manipulator, the voltages of motors are used as inputs of the system. To highlight the capabilities of the control system, a flexible joint robot which is highly nonlinear, heavily coupled and uncertain is used. In addition, to improve the control performance, the parameters of the primary membership functions of IT2FLC are optimized using particle swarm optimization (PSO). A comparative study between the proposed IT2FLC and type-1 fuzzy logic controller (T1FLC) is presented to better assess their respective performance in presence of external disturbance and unmodelled dynamics. Stability analysis is presented and the effectiveness of the proposed control approach is demonstrated by simulations using a two-link flexible-joint robot driven by permanent magnet direct current motors. Simulation results show the superiority of the IT2FLC over the T1FLC in terms of accuracy, robustness and interpretability.

Interval Type-2 Fuzzy PD Tracking Control of Flexible-Joint Robots

“…In addition, it spends less control effort than the type-1 in order to deal with disturbance. Note that in the present paper a novel control approach has been proposed whereas in [29] type-2 fuzzy system has been directly used as a controller and Particle Swarm Optimization (PSO) used to optimize the control structure. …”

Robust Full Tracking Control Design of Disturbed Quadrotor UAVs with Unknown Dynamics

“…Using the IT2-AFSs defined in (11) and (29), the adaptive systems defined in (12), the adaptation laws given by (20) and (32), the global control law (31) developed for the underactuated quadrotor (8) is stable in the sense of Lyapunov, and the asymptotic convergence of the tracking error is established despite the unknown dynamics, unknown physical parameters, and all of the unknown and unpredictable disturbances that affect quadrotor dynamics.…”

“…However, type-1 (T1)-FSs (T1-FSs) have a drawback when the linguistic information used to describe the system dynamics contains uncertainties. To cope with this constraint, another kind of FS called type-2 (T2)-FS (T2-FS) was introduced into the modeling and design of robust controllers for complex systems with uncertainties [26][27][28][29][30]. The capability of T2-FSs to handle the problem of inaccurate linguistic information much better than T1-FSs is due to the uncertainty property footprint incorporated into the membership functions (MFs) of T2 fuzzy sets [31][32][33].…”

Real-time compliance control of an assistive joint using QNX operating system

“…The dynamic model of a manipulator provides a description of the relationship between the joint actuator torques and the motion of the structure [31,32] . There are two methods for derivation of the equations of motion of a manipulator in the joint space.…”