Spandan Roy

Adaptive-Robust Control of a Class of Nonlinear Systems with Unknown Input Delay

In this paper, the tracking control problem of a class of uncertain Euler–Lagrange systems subjected to time-varying input delay and bounded disturbances is addressed. To this front, a novel delay-dependent control law referred as adaptive robust outer loop control (AROLC) is proposed. Compared to the conventional predictor-based approaches, the proposed controller is capable of negotiating any input delay, within a stipulated range, without knowing the delay variation. The maximum allowable input delay is computed through Razumikhin-type stability analysis. AROLC also provides robustness against the disturbances due to the input delay, parametric variations and unmodelled dynamics through switching control law. The novel adaptive law allows the switching gain to modify itself online in accordance with the tracking error without any prerequisite of the uncertainties. The uncertain system, employing AROLC, is shown to be uniformly ultimately bounded. As a proof of concept, experimentation is carried out on a nonholonomic wheeled mobile robot with various time varying as well as fixed input delay, and better tracking accuracy of the proposed controller is noted compared to predictor-based methodology.

Robust position control of an autonomous underwater vehicle: A comparative study

The highly non-linear and coupled dynamics of Autonomous Underwater Vehicles (AUVs), added with modeling errors, parametric uncertainties and payload variations pose a major challenge towards autonomous control of AUVs for various application requirements. Environmental hazards such as ocean currents sometimes dominate and make the control of underwater systems even more complicated. The proposed control technique addresses the design of a robust controller for reasonably accurate path tracking of AUVs incorporating the effects of above uncertain paradigms within some known bounds. It is well-known that measurement noise, which is associated with the navigational sensors, degrades the performance of the controller leading to substantial deviation from the reference path. Incorporation of sensor fusion technique, which is driven by sensors error characteristics, is necessary to improve the controller performance. Performance of the controller is verified using the real-life parameters an AUV, developed at CSIR-CMERI, Durgapur, India considering a few uncertainties.

Adaptive-Robust Time-Delay Control for a Class of Uncertain Euler–Lagrange Systems

This paper proposes a new adaptive-robust control (ARC) strategy for a tracking control problem of a class of uncertain Euler–Lagrange systems. The proposed adaptive-robust time-delay control (ARTDC) amalgamates the ARC strategy with the time-delay control (TDC). It comprises three parts: a time-delay estimation part, a desired dynamics injection part, and an adaptive-robust part. The main feature of the proposed ARTDC is that it does not involve any threshold value in its adaptive law; thus, it allows the switching gain to increase or decrease whenever the error trajectories move away or close to the switching surface, respectively. Thus, compared with the existing ARC schemes, ARTDC is able to alleviate the over- and underestimation problems of the switching gain. Moreover, the stability analysis of ARTDC provides an upper bound for the selection of sampling interval and its relation with controller gains. The proposed ARTDC shows improved tracking performance compared with the TDC and the existing adaptive sliding-mode control in simulations as well as in experiments with a multiple-degree-of-freedom system.

Robust control of nonholonomic wheeled mobile robot with past information: Theory and experiment

In this article, a robust hybrid control method is presented for efficient path tracking control of a nonholonomic wheeled mobile robotic system under parametric and nonparametric variations. The present control law is a paradigm shift to control a wheeled mobile robot over a predefined trajectory by fusing the best features of the switching control logic as well as time-delayed control logic. The proposed hybrid control strategy aims at reducing the effort required for modeling the complex wheeled mobile robotic systems by approximating the unknown dynamics using input and feedback information of past time instances. Furthermore, the proposed methodology significantly reduces the approximation error arising from finite time-delay through the switching logic without any prior knowledge of the uncertainty bound. A new stability analysis for the time-delayed control is proposed which establishes an analytical relation between the controller performance and the approximation error. Performance of the proposed hybrid controller is tested with a real-life wheeled mobile robot and improved tracking performance is observed compared to conventional robust control strategies even with the incorporation of dynamic parametric uncertainties.

Adaptive sliding mode control of a class of nonlinear systems with artificial delay

Abstract In this paper, an adaptive-robust control (ARC) strategy, christened as Adaptive Time-delayed Sliding Mode Control (ATSMC) is presented for trajectory tracking control of a class of uncertain Euler-Lagrange systems. The proposed control framework brings together the best features of the switching control logic and time-delayed logic. ATSMC uses artificial time delay to approximate the unknown dynamics through time-delayed logic, and the switching logic provides robustness against the approximation error. The adaptation law for the switching gain of the conventional ARC methodologies suffer from over- and under-estimation problems. The novel adaptive law of ATSMC alleviates the over- and under-estimation problems of switching gain. Moreover, a new design methodology and stability criterion for time-delayed control is proposed which provides an upper bound on the allowable delay time. Experimental results of the proposed methodology using a nonholonomic wheeled mobile robot (WMR) is presented and improved tracking accuracy of the proposed control law is noted compared to time-delayed control and conventional adaptive sliding mode control.

Adaptive-Robust Control of uncertain Euler-Lagrange systems with past data: A time-delayed approach

A new adaptive robust control strategy, christened as Adaptive Time-delayed Robust Control (ATRC) is presented in this paper for trajectory tracking control of a class of Euler-Lagrange systems subjected to uncertainties with unknown bounds. The adaptive law to compute the switching gain of the conventional adaptive-robust controllers require either complete nominal modelling of the system or uncertainty bound. The proposed control framework amalgamates the best features of the switching control logic and time-delayed logic. The proposed control strategy approximates the unknown dynamics through time-delayed logic, and the switching logic provides robustness against the approximation error. A novel adaptive law for the switching control is developed which does not require uncertainty modelling or the knowledge of its bound and the switching gain adapts itself according to the tracking error incurred by the system. Moreover, a new design methodology and stability criterion for time-delayed control is proposed. Experimental results of the proposed methodology using a nonholonomic wheeled mobile robot (WMR) is presented and improved tracking accuracy of the proposed control law is noted compared to the conventional time-delayed control and time-delayed control with gradient estimator.

Adaptive–Robust Control of Euler–Lagrange Systems With Linearly Parametrizable Uncertainty Bound

This brief proposes a new adaptive–robust control (ARC) architecture for a class of uncertain Euler–Lagrange (EL) systems where the upper bound of the uncertainty satisfies linear in the parameters structure. Conventional ARC strategies either require structural knowledge of the system or presume that the overall uncertainties or its time derivative are norm bounded by a constant. Due to the unmodeled dynamics and modeling imperfection, true structural knowledge of the system is not always available. Furthermore, for the class of systems under consideration, prior assumption, regarding the uncertainties (or its time derivative) being upper bounded by a constant, puts a restriction on the states beforehand. Conventional ARC laws invite overestimation–underestimation problem of switching gain. Toward this front, adaptive switching-gain-based robust control (ASRC) is proposed, which alleviates the overestimation–underestimation problem of switching gain. Moreover, ASRC avoids any presumption of constant upper bound on the overall uncertainties and can negotiate uncertainties regardless of being linear or nonlinear in parameters. Experimental results of ASRC using a wheeled mobile robot note improved control performance in comparison with the adaptive sliding mode control.

Toward Position-Only Time-Delayed Control for Uncertain Euler–Lagrange Systems: Experiments on Wheeled Mobile Robots

This letter addresses the various practical design issues of a continuous-time time-delayed control (TDC) and proposes a new controller to make the TDC more suitable and applicable for real-life systems. While TDC has been renowned for its robust performance and simplicity in form, it requires state-derivatives feedback which may not be available explicitly in practice; and multiple numerical differentiations of the noisy state data deteriorate performance by invoking measurement error. In this letter, position-only time-delayed control (POTDC) has been proposed for a class of Euler–Lagrange systems which encompasses a large class of practical systems such as robotic manipulators and unmanned mobile robots. Contrary to the conventional TDC, the proposed POTDC eliminates explicit requirement of velocity and acceleration feedbacks and uses only position information of present and past instances to estimate the velocity and acceleration terms. It thus alleviates the measurement error arising from a numerical computation of state-derivatives. Moreover, based on the Razumikhin theorem, continuous-time stability is rigorously analyzed with consideration of the time-delay element in POTDC, which indeed establishes a selection criterion for the sampling interval and provides the designer a range of sampling intervals for same choice of controller gains. This allows POTDC to be suitable for systems which specifically operate with high sampling intervals due to application requirement. Accordingly, experimental validations of POTDC are provided in comparison with TDC under various sampling intervals, using a wheeled mobile robot.

Adaptive-Robust Control of a Class of EL Systems With Parametric Variations Using Artificially Delayed Input and Position Feedback

In this paper, the tracking control problem of an Euler-Lagrange system is addressed with regard to parametric uncertainties, and an adaptive-robust control strategy, christened Time-Delayed Adaptive Robust Control (TARC), is presented. TARC approximates the unknown dynamics through the time-delayed estimation, and the adaptive-robust control provides robustness against the approximation error. The novel adaptation law of TARC, in contrast to the conventional adaptive-robust control methodologies, requires neither complete model of the system nor any knowledge of predefined uncertainty bounds to compute the switching gain, and circumvents the over- and underestimation problems of the switching gain. Moreover, TARC only utilizes position feedback and approximates the velocity and acceleration terms from the past position data. The adopted state-derivatives estimation method in TARC avoids any explicit requirement of external low pass filters for the removal of measurement noise. A new stability notion in continuous-time domain is proposed considering the time delay, adaptive law, and state-derivatives estimation which in turn provides a selection criterion for gains and sampling interval of the controller.In this paper, the tracking control problem of an Euler–Lagrange system is addressed with regard to parametric uncertainties, and an adaptive-robust control (ARC) strategy, christened time-delayed ARC (TARC), is presented. TARC approximates the unknown dynamics through the time-delayed estimation, and the ARC provides robustness against the approximation error. The novel adaptation law of TARC, in contrast to the conventional ARC methodologies, requires neither complete model of the system nor any knowledge of predefined uncertainty bounds to compute the switching gain, and circumvents the overestimation and underestimation problems of the switching gain. Moreover, TARC only utilizes position feedback and approximates the velocity and acceleration terms from the past position data. The adopted state-derivatives estimation method in TARC avoids any explicit requirement of external low-pass filters for the removal of measurement noise. A new stability notion in the continuous-time domain is proposed considering the time delay, adaptive law, and state-derivatives estimation, which in turn provides a selection criterion for gains and sampling interval of the controller. Experimental results of the proposed methodology using a multiple degrees-of-freedom robot are presented, and improved tracking accuracy of the proposed control law is demonstrated compared with the conventional adaptive sliding mode control.

Artificial Delay Based ARC of a Class of Uncertain EL Systems with Only Position Feedback.

In this paper, the tracking control problem of an Euler-Lagrange system is addressed with regard to parametric uncertainties, and an adaptive-robust control strategy, christened Time-Delayed Adaptive Robust Control (TARC), is presented. TARC approximates the unknown dynamics through the time-delayed estimation, and the adaptive-robust control provides robustness against the approximation error. The novel adaptation law of TARC, in contrast to the conventional adaptive-robust control methodologies, requires neither complete model of the system nor any knowledge of predefined uncertainty bounds to compute the switching gain, and circumvents the over- and underestimation problems of the switching gain. Moreover, TARC only utilizes position feedback and approximates the velocity and acceleration terms from the past position data. The adopted state-derivatives estimation method in TARC avoids any explicit requirement of external low pass filters for the removal of measurement noise. A new stability notion in continuous-time domain is proposed considering the time delay, adaptive law, and state-derivatives estimation which in turn provides a selection criterion for gains and sampling interval of the controller.In this paper, the tracking control problem of an Euler–Lagrange system is addressed with regard to parametric uncertainties, and an adaptive-robust control (ARC) strategy, christened time-delayed ARC (TARC), is presented. TARC approximates the unknown dynamics through the time-delayed estimation, and the ARC provides robustness against the approximation error. The novel adaptation law of TARC, in contrast to the conventional ARC methodologies, requires neither complete model of the system nor any knowledge of predefined uncertainty bounds to compute the switching gain, and circumvents the overestimation and underestimation problems of the switching gain. Moreover, TARC only utilizes position feedback and approximates the velocity and acceleration terms from the past position data. The adopted state-derivatives estimation method in TARC avoids any explicit requirement of external low-pass filters for the removal of measurement noise. A new stability notion in the continuous-time domain is proposed considering the time delay, adaptive law, and state-derivatives estimation, which in turn provides a selection criterion for gains and sampling interval of the controller. Experimental results of the proposed methodology using a multiple degrees-of-freedom robot are presented, and improved tracking accuracy of the proposed control law is demonstrated compared with the conventional adaptive sliding mode control.