Pramod D. Shendge

Bio: Pramod D. Shendge is an academic researcher from College of Engineering, Pune. The author has contributed to research in topics: Sliding mode control & Control theory. The author has an hindex of 16, co-authored 51 publications receiving 1080 citations.

Sliding Mode Control for Mismatched Uncertain Systems Using an Extended Disturbance Observer

Abstract: This paper extends a recent result on sliding mode control for general n th order systems with mismatched uncertainties. In this paper, a control is proposed to handle a larger class of mismatched uncertainties by extending the disturbance observer and modifying and generalizing the sliding surface. The practical stability of the overall system is proved and the results are verified by simulation of an illustrative example.

Uncertainty-Estimation-Based Approach to Antilock Braking Systems

Abstract: Two new strategies based on uncertainty estimation are proposed for antilock braking systems (ABSs). In one of the strategies, the uncertainties and the disturbances are estimated using inertial delay control (IDC), and the estimates are used in a backstepping-control-based braking system. In the other strategy, in addition to uncertainties, the states are also estimated using an inertial delay observer (IDO) for a sliding-mode-control (SMC)-based braking system. No knowledge of uncertainties, disturbances, and the road adhesion friction coefficient or their bounds is assumed. The stability of the overall system is proven, and the schemes are validated by simulation and experimentation in the laboratory.

Adaptive Neural Output Feedback Control of Uncertain Nonlinear Systems With Unknown Hysteresis Using Disturbance Observer

Abstract: In this paper, an adaptive neural output feedback control scheme is proposed for uncertain nonlinear systems that are subject to unknown hysteresis, external disturbances, and unmeasured states. To deal with the unknown nonlinear function term in the uncertain nonlinear system, the approximation capability of the radial basis function neural network (RBFNN) is employed. Using the approximation output of the RBFNN, the state observer and the nonlinear disturbance observer (NDO) are developed to estimate unmeasured states and unknown compounded disturbances, respectively. Based on the RBFNN, the developed NDO, and the state observer, the adaptive neural output feedback control is proposed for uncertain nonlinear systems using the backstepping technique. The first-order sliding-mode differentiator is employed to avoid the tedious analytic computation and the problem of “explosion of complexity” in the conventional backstepping method. The stability of the whole closed-loop system is rigorously proved via the Lyapunov analysis method, and the satisfactory tracking performance is guaranteed under the integrated effect of unknown hysteresis, unmeasured states, and unknown external disturbances. Simulation results of an example are presented to illustrate the effectiveness of the proposed adaptive neural output feedback control scheme for uncertain nonlinear systems.

Adaptive Sliding Mode Control for Interval Type-2 Fuzzy Systems

Abstract: This paper is concerned with the adaptive sliding mode control problem of uncertain nonlinear systems. Interval type-2 Takagi–Sugeno (T–S) fuzzy model is employed to represent uncertain nonlinear systems. The input matrices of the nonlinear systems are allowed to be different for the sliding mode controller design. The uncertain parameters are described by the lower and upper membership functions. An integral sliding mode surface is designed for analysis of sliding motion. Based on the sliding mode surface, a novel sliding mode controller is designed to guarantee that the closed-loop system is uniformly ultimately bounded. Some simulation results are given to illustrate the effectiveness of the presented control scheme.

Extended state observer-based sliding mode control for PWM-based DC–DC buck power converter systems with mismatched disturbances

Abstract: This study investigates an extended state observer (ESO)-based sliding mode control (SMC) approach for pulse-width modulation-based DC-DC buck converter systems subject to mismatched disturbances. By designing a novel sliding-mode manifold incorporated with a disturbance estimation technique, an ESO-based SMC method is designed to deal with mismatched disturbances. A rigorous stability analysis is also presented. As compared with the nominal SMC method, the proposed method obtains a better disturbance rejection ability even the disturbances do not satisfy the so-called matching condition. Simulations and experimental comparison results are implemented to verify the effectiveness of the proposed control method.

Disturbance Observer-Based Integral Sliding-Mode Control for Systems With Mismatched Disturbances

Abstract: This paper develops the disturbance observer-based integral sliding-mode control approach for continuous-time linear systems with mismatched disturbances or uncertainties. The disturbance observer is proposed to generate the disturbance estimate, which can be incorporated in the controller to counteract the disturbance. With the help of the proposed disturbance observer, both the memoryless and memory-based integral sliding surfaces and integral sliding-mode controllers are developed, respectively, and two approaches, i.e., $H_\infty$ control and steady-state output-based approaches, are proposed to design the controller and disturbance rejection gains. Finally, the effectiveness and applicability of the proposed technique are illustrated by a numerical example and a real-time experiment.