An adaptive type-2 fuzzy sliding mode tracking controller for a robotic manipulator

With the wide application of intelligent manufacturing and the development of diversified functions of industrial manipulator, the requirements for the control accuracy and stability of the manipulator servo system are also increasing. The control of industrial manipulator is a time-varying system with nonlinear and strong coupling, which is often affected by uncertain factors, including parameter changing, environmental interference, joint friction and so on. Aiming at the problem of the poor control accuracy of the manipulator. Under the complex disturbance environment, control accuracy of the manipulator will be greatly affected, so this paper proposes an adaptive type-2 fuzzy sliding mode control (AT2FSMC) method applied to the servo control of the industrial manipulator, which realizes the adaptive adjustment of the boundary layer thickness to suppress the trajectory error caused by the external disturbance and weakens the chattering problem of the sliding mode control. The simulation results on a two-axis manipulator indicate that, with the presence of external disturbances, the proposed control method can help the manipulator maintain control signal stability and improve tracking accuracy. It also suppressed chattering produced by sliding mode control (SMC) and strengthening the robustness of the system. Compared with other conventional trajectory tracking control methods, the effectiveness of the proposed method can be reflected. Finally, the proposed method is tested in an actual manipulator to complete a practical trajectory to prove its feasibility

Input and State Estimation for Discrete-Time Linear Systems with Application to Target Tracking and Fault Detection

This dissertation first presents a deterministic treatment of discrete-time input reconstruction and state estimation without assuming the existence of a full-rank Markov parameter. Algorithms based on the generalized inverse of a block-Toeplitz matrix are given for 1) input reconstruction in the case where the initial state is known; 2) state estimation in the case where the initial state is unknown, the system has no invariant zeros, and the input is unknown; and 3) input reconstruction and state estimation in the case where the initial state is unknown and the system has no invariant zeros. In all cases, the unknown input is an arbitrary deterministic or stochastic signal. In addition, the reconstruction/estimation algorithm is deadbeat, which means that, in the absence of sensor noise, exact input reconstruction and state estimation are achieved in a finite number of steps. Next, asymptotic input and state estimation for systems with invariant zeros is considered. Although this problem has been widely studied, existing techniques are confined to the case where the system is minimum phase. This dissertation presents retrospective cost input estimation (RCIE), which is based on retrospective cost optimization. It is shown that RCIE automatically develops an internal model of the unknown input. This internal model provides an asymptotic estimate of the unknown input regardless of the location of the zeros of the plant, including the case of nonminimum-phase dynamics. The input and state estimation method developed in this dissertation provides a novel approach to a longstanding problem in target tracking, namely, estimation of the inertial acceleration of a body using only position measurements. It turns out that, for this problem, the discretized kinematics have invariant zeros on the unit circle, and thus the dynamics is nonminimum-phase. Using optical position data for a UAV, RCIE estimates the inertial acceleration, which is modeled as an unknown input. The acceleration estimates are compared to IMU data from onboard sensors. Finally, based on exact kinematic models for input and state estimation, this dissertation presents a method for detecting sensor faults. A numerical investigation using the NASA Generic Transport Model shows that the method can detect stuck, bias, drift, and deadzone sensor faults. Furthermore, a laboratory experiment shows that RCIE can estimate the inertial acceleration (3-axis accelerometer measurements) and angular velocity (3-axis rate-gyro measurements) of a quadrotor using vision data; comparing these estimates to the actual accelerometer and rate-gyro measurements provide the means for assessing the health of the accelerometer and rate gyro.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145813/1/ansahmad_1.pd