A sliding mode estimation method for fluid flow fields using a differential inclusions-based analysis

2020 ◽  
pp. 1-10 ◽  
Author(s):  
Krishna Bhavithavya Kidambi ◽  
William MacKunis ◽  
Sergey V. Drakunov ◽  
Vladimir Golubev
Keyword(s):  
Fluid Flow ◽  
Differential Inclusions ◽  
Sliding Mode ◽  
Estimation Method ◽  
Flow Fields ◽  
Mode Estimation

A closed-loop nonlinear control and sliding mode estimation strategy for fluid flow regulation

2018 ◽  
Vol 29 (3) ◽  
pp. 779-792 ◽  
Author(s):  
Krishna Bhavithavya Kidambi ◽  
Natalie Ramos-Pedroza ◽  
William MacKunis ◽  
Sergey V. Drakunov
Keyword(s):  
Fluid Flow ◽  
Nonlinear Control ◽  
Closed Loop ◽  
Sliding Mode ◽  
Flow Regulation ◽  
Mode Estimation ◽  
Estimation Strategy

A Quaternion-Based Sliding Mode Observer for Gyro-Bias Estimation and Attitude Reconstruction

2019 ◽  
Author(s):  
Yuan Tian ◽  
Marc Compere ◽  
Sergey Drakunov
Keyword(s):  
Sliding Mode ◽  
Estimation Method ◽  
The Body ◽  
Sliding Mode Observer ◽  
Measurement Unit ◽  
Bias Estimation ◽  
Simple Formulation ◽  
The Real ◽  
Observer Error ◽  
Gyro Bias

Abstract Localization accuracy is one of the most important parts of Unmanned Vehicle Systems, Automated Vehicles, Robotics and Navigation. The 6-DOF Inertial Measurement Unit (IMU) is a commonly used device for inertial navigation and is composed of a 3-axis accelerometer and 3-axis gyroscope. The body-fixed IMU measurements are combined with initial values to produce a position and orientation estimate in the inertial frame with every new measurement. However, IMU performance is greatly degraded by bias, scale-factor, non-orthogonality, temperature, and noise. This paper develops a sliding mode observer specifically focused on gyroscope bias estimation to improve gyro measurement results. The work presented here improves the performance of tilt sensors equipped in a commercially available smartphones with accelerometers and gyroscopes. The algorithm uses quaternions to avoid the well-known Euler angle singularities also known as gimbal lock. The observed gyro-bias can be used to reconstruct an improved estimation of the real attitude. A sliding-mode observer was constructed, and A* Matrix stability criterion were used to guarantee observer error convergence in finite time. The algorithm was verified using both a simulated IMU model and experimental tests with a custom designed rotational platform. Simulation tests used a predefined gyros-bias to ensure the algorithm-estimated results converged to the correct value. Simulation results show the observer error quickly converges to zero and the gyro-bias estimation converged to the expected values. The results also show that the proposed method is very effective for reconstructing the real attitude using the observed gyro-bias. This study presents a fast, simple gyro-bias estimation method that can help reconstruct the real attitude with a simple formulation that eliminates complicated constraints.

scholarly journals Advanced Adaptive Fault Diagnosis and Tolerant Control for Robot Manipulators

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1281 ◽  
Author(s):  
Farzin Piltan ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Fault Tolerant Control ◽  
Sliding Surface ◽  
Surface Slope ◽  
Extended State ◽  
Mode Estimation ◽  
Fuzzy Sliding Mode

In this paper, an adaptive Takagi–Sugeno (T–S) fuzzy sliding mode extended autoregressive exogenous input (ARX)–Laguerre proportional integral (PI) observer is proposed. The proposed T–S fuzzy sliding mode extended-state ARX–Laguerre PI observer adaptively improves the reliability, robustness, estimation accuracy, and convergence of fault detection, estimation, and identification. For fault-tolerant control in the presence of uncertainties and unknown conditions, an adaptive fuzzy sliding mode estimation technique is introduced. The sliding surface slope gain is significant to improve the system’s stability, but the sliding mode technique increases high-frequency oscillation (chattering), which reduces the precision of the fault diagnosis and tolerant control. A fuzzy procedure using a sliding surface and actual output position as inputs can adaptively tune the sliding surface slope gain of the sliding mode fault-tolerant control technique. The proposed robust adaptive T–S fuzzy sliding mode estimation extended-state ARX–Laguerre PI observer was verified with six degrees of freedom (DOF) programmable universal manipulation arm (PUMA) 560 robot manipulator, proving qualified efficiency in detecting, isolating, identifying, and tolerant control for faults inherent in sensors and actuators. Experimental results showed that the proposed technique improves the reliability of the fault detection, estimation, identification, and tolerant control.

Robust Sensors-Fault-Tolerance With Sliding Mode Estimation and Control for PMSM Drives

2018 ◽  
Vol 23 (1) ◽  
pp. 17-28 ◽  
Author(s):  
Suneel Kumar Kommuri ◽  
Sang Bin Lee ◽  
Kalyana Chakravarthy Veluvolu
Keyword(s):  
Fault Tolerance ◽  
Sliding Mode ◽  
Mode Estimation ◽  
Estimation And Control ◽  
And Control

Maneuvering Car Tracking Using the Interacting Multiple Model and the Dynamic 2nd-Order SVSF Method

2015 ◽  
Author(s):  
Mina Attari ◽  
Hamed Hossein Afshari ◽  
Saeid Habibi
Keyword(s):  
State Estimation ◽  
Sliding Mode ◽  
Estimation Method ◽  
Variable Structure ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Structure Control ◽  
Position Data ◽  
Car Driving ◽  
Tracking Strategy

Car tracking algorithms have recently found a major role in intelligent automotive applications. They are mainly based on the state estimation techniques to solve the maneuvering car tracking problems. The dynamic 2nd-order SVSF method is a novel robust state estimation method that is based on the variable structure control theory. It benefits from the accuracy, robustness, and chattering suppression properties of second-order sliding mode systems for robust state estimation. The main contribution of this paper is to present and implement a new tracking strategy that is a combination of the dynamic 2nd-order SVSF with the IMM filter. It benefits from the robust performance of the dynamic 2nd-order SVSF and the switching property of the IMM filter. This strategy is simulated and examined under several car driving patterns and experimental position data that are captured by a GPS device. The robustness and efficiency of this strategy is then compared with the Kalman filter-based counterparts.

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