Kalyana C. Veluvolu

High-Gain Observers With Sliding Mode for State and Unknown Input Estimations

To handle the state estimation of a nonlinear system perturbed by a scalar disturbance distributed by a known nonlinear vector, a sliding-mode term is incorporated into the nonlinear high-gain observer (HGO) to realize a robust HGO. By imposing a structural assumption on the unknown input distribution vector, the observability of the disturbance with respect to the output is safeguarded, and the disturbance can be estimated from the sliding surface. Under a Lipschitz condition for the nonlinear part, the nonlinear observers are designed under the structural assumption that the system is observable with respect to any input. In the sliding mode, the disturbance under an equivalent control becomes an increment of Lipschitzian function, and the convergence of the estimation error dynamics can be proven similar to the analysis of HGOs. The proposed technique can be applied for fault detection and isolation. The simulation results for the bioreactor application demonstrate the effectiveness of the proposed method.

Nonlinear sliding mode high-gain observers for fault estimation

A robust high gain observer for state and unknown inputs/faults estimations for a special class of nonlinear systems is developed in this article. Ensuring the observability of the faults/unknown inputs with respect to the outputs, the faults can be estimated from the sliding surface. Under a Lipschitz condition for the nonlinear part, the high gain observers are designed under some regularity assumptions. In the sliding mode, the convergence of the estimation error dynamics is proven similar to the analysis of high-gain observers.

Estimation of Physiological Tremor from Accelerometers for Real-Time Applications

Accurate filtering of physiological tremor is extremely important in robotics assisted surgical instruments and procedures. This paper focuses on developing single stage robust algorithms for accurate tremor filtering with accelerometers for real-time applications. Existing methods rely on estimating the tremor under the assumption that it has a single dominant frequency. Our time-frequency analysis on physiological tremor data revealed that tremor contains multiple dominant frequencies over the entire duration rather than a single dominant frequency. In this paper, the existing methods for tremor filtering are reviewed and two improved algorithms are presented. A comparative study is conducted on all the estimation methods with tremor data from microsurgeons and novice subjects under different conditions. Our results showed that the new improved algorithms performed better than the existing algorithms for tremor estimation. A procedure to separate the intended motion/drift from the tremor component is formulated.

A Robust Observer-Based Sensor Fault-Tolerant Control for PMSM in Electric Vehicles

This paper investigates the problem of automatic speed tracking control of an electric vehicle (EV) that is powered by a permanent-magnet synchronous motor (PMSM). A reconfiguration scheme, based on higher order sliding mode (HOSM) observer, is proposed in the event of sensor faults/failures to maintain a good control performance. The corresponding controlled motor output torque drives EVs to track the desired vehicle reference speed for providing uninterrupted vehicle safe operation. The effectiveness of the overall sensor fault-tolerant speed tracking control is highlighted when an EV is subjected to disturbances like aerodynamic load force and road roughness using high-fidelity software package CarSim. Experiments with a 26-W, three-phase PMSM are presented to demonstrate the validity of the proposed fault-detection scheme.

Estimating Displacement of Periodic Motion With Inertial Sensors

Inertial sensors, like accelerometers and gyroscopes, are rarely used by themselves to measure displacement. Accuracy of inertial sensors is greatly handicapped by the notorious integration drift, which arises due to numerical integration of the sensors zero bias error. A solution is proposed in this paper to provide drift free estimation of displacement from inertial sensors.

Bandlimited Multiple Fourier Linear Combiner for Real-time Tremor Compensation

Surgical accuracy of the hand-held instruments depends on the active compensation of disturbance and tremor. Physiological tremor is one of the main causes for imprecision in micro-surgery procedures. One of the popular tremor compensation methods is based on weighted-frequency Fourier linear combiner (WFLC) algorithm, that can adapt to the changes in frequency as well as amplitude of the tremor signal. WLFC estimates the dominant frequency and the amplitude. For the case of tremor with frequency variation or comprising of two or three frequencies close in spectral domain, the WFLC performance is degraded. In this paper, we present a bandlimited multiple Fourier linear combiner that can track the modulated signals with multiple frequency components. We also discuss the tremor sensing with accelerometers. Using the proposed algorithm the drift caused by the accelerometers is also eliminated. The proposed filter is tested in real-time for 1-DOF cancellation of tremor.

High-gain observer with sliding mode for nonlinear state estimation and fault reconstruction

Abstract This paper develops a high gain observer with multiple sliding modes for simultaneous state and fault estimations for MIMO nonlinear systems. The novelty lies in the observer design that employs the combination of high-gain observer and sliding mode observer. The proposed observer does not impose the small-Lipschitz-constant condition on the system nonlinearity. By imposing a structural assumption on the nonlinear fault distribution matrix, the observability of the faults/unknown inputs w.r.t. the outputs is safeguarded and sliding modes are utilized for their reconstruction. The reconstruction of the faults from the sliding mode only relies on the output estimation error and thus can be implemented online together with the state estimation. Finally, an application to flexible joint robotic arm is used to illustrate the proposed method.

Estimation and filtering of physiological tremor for real‐time compensation in surgical robotics applications

Physiological tremor is the main cause of imprecision in microsurgical procedures/robotics applications. Existing methods, such as weighted‐frequency Fourier linear combiner (WFLC), rely on estimating the tremor under the assumption that it has a single dominant frequency. This paper focuses on developing a new algorithm for accurate tremor filtering in real time.

Double adaptive bandlimited multiple Fourier linear combiner for real-time estimation/filtering of physiological tremor

Tremor is the root cause for human imprecision during microsurgery. Accurate filtering of physiological tremor is extremely important for compensation in robotics assisted microsurgical instruments/ procedures. A study on several surgeons tremor is conducted and the characteristics of the tremor are analyzed. A double adaptive bandlimited multiple Fourier linear combiner is designed to estimate the modulated signals with multiple frequency components for filtering and compensation of tremor in realtime. A separation procedure to separate the intended motion/drift from the tremor portion is developed. The proposed methods are compared with the existing weighted-frequency Fourier linear combiner (WFLC) algorithm on the tremor data of surgeons/subjects. Critical validation of the algorithm is performed, experiments are conducted for 1-degree of freedom (DOF) cancellation of tremor. Our experiments showed that our newly developed algorithm has a tremor compensation of at least 65% compared to 46% for the WFLC algorithm.

Discrete-Time Sliding-Mode State and Unknown Input Estimations for Nonlinear Systems

This paper addresses the problems of discrete-time state and unknown input/fault estimation for continuous-time nonlinear systems with multiple unknown inputs. Taylor series expansion and a nonlinear transformation are used to convert the nonlinear continuous-time system into a discrete-time model. The conditions for the observability of unknown inputs w.r.t. outputs are discussed. The novelty lies in the formulation of multiple sliding-mode estimator for the states that are directly influenced by unknown inputs, which cannot be decoupled by nonlinear transformation. This framework allows for the estimation of unknown inputs from the multiple sliding modes. The existence of discrete-time sliding mode is guaranteed, and the relation between the boundary layer thickness and the sliding-mode gain design that will eliminate chattering and the boundedness conditions is obtained. The proposed technique can be applied for fault detection and isolation. Simulation results with application to three-phase motor are given to demonstrate the effectiveness of the proposed method.