Indra Narayan Kar

Design and implementation of an adaptive fuzzy logic-based controller for wheeled mobile robots

In this paper, a control structure that makes possible the integration of a kinematic controller and an adaptive fuzzy controller for trajectory tracking is developed for nonholonomic mobile robots. The system uncertainty, which includes mobile robot parameter variation and unknown nonlinearities, is estimated by a fuzzy logic system (FLS). The proposed adaptive controller structure represents an amalgamation of nonlinear processing elements and the theory of function approximation using FLS. The real-time control of mobile robots is achieved through the online tuning of FLS parameters. The system stability and the convergence of tracking errors are proved using the Lyapunov stability theory. Computer simulations are presented which confirm the effectiveness of the proposed tracking control law. The efficacy of the proposed control law is tested experimentally by a differentially driven mobile robot. Both simulation and results are described in detail.

On-line system identification of complex systems using Chebyshev neural networks

This paper proposes a computationally efficient artificial neural network (ANN) model for system identification of unknown dynamic nonlinear discrete time systems. A single layer functional link ANN is used for the model where the need of hidden layer is eliminated by expanding the input pattern by Chebyshev polynomials. Thus, creation of nonlinear decision boundaries in the multidimensional input space and approximation of complex nonlinear systems becomes easier. These models are linear in their parameters and nonlinear in the inputs. The recursive least squares method with forgetting factor is used as on-line learning algorithm for parameter updation. The good behaviour of the identification method is tested on Box and Jenkins Gas furnace benchmark identification problem, single input single output (SISO) and multi input multi output (MIMO) discrete time plants. Stability of the identification scheme is also addressed.

Simple neuron-based adaptive controller for a nonholonomic mobile robot including actuator dynamics

Abstract In this paper, a simple neuron-based adaptive controller for trajectory tracking is developed for nonholonomic mobile robots without velocity measurements. The controller is based on structural knowledge of the dynamics of the robot and the odometric calculation of robot position only. The wheel actuator dynamics is also taken into account. An approximation network approximates a nonlinear function involving robot dynamic parameters so that no knowledge of those parameters is required. The proposed controller is robust not only to structured uncertainty such as mass variation but also to unstructured one such as disturbances. The real-time control of mobile robot is achieved through the online learning of the approximation network. The system stability and the boundness of tracking errors are proved using Lyapunov stability theory. Computer simulations with circular as well as square reference trajectories are presented that confirm the simplicity and effectiveness of the proposed tracking control law. The efficacy of the proposed control law is tested experimentally on a differentially driven mobile robot. Both simulation and experimental results are described in detail.

Bending and torsional vibration control of a flexible plate structure using H/sub /spl infin//-based robust control law

This paper presents a method of controlling the bending and torsional vibration modes of a flexible plate structure (theoretically which has infinite number of vibration modes) using H/sub /spl infin//-based robust control. For this purpose, a three degree of freedom (DOF) reduced order lumped mass model of a plate structure is derived by considering first three vibration modes and neglecting the all other high-frequency modes. These neglected modes constitute the unstructured uncertainties of the system and taken care of in the time of design process. An idea is proposed to reduce the unmodeled system uncertainties by placing actuators in the node points of a neglected mode. As a result, it is possible to avoid the spillover instability (avoid the influence of a neglected mode) without affecting the control of lower order modes. Then a static state feedback controller is designed based on the reduced order model and the approximate knowledge of unmodeled uncertainties. The efficacy of feedback controller is shown through simulation and experimental studies.

Adaptive output feedback tracking control of robot manipulators using position measurements only

In this paper, a new adaptive neuro controller for trajectory tracking is developed for robot manipulators without velocity measurements, taking into account the actuator constraints. The controller is based on structural knowledge of the dynamics of the robot and measurements of joint positions only. The system uncertainty, which may include payload variation, unknown nonlinearities and torque disturbances is estimated by a Chebyshev neural network (CNN). The adaptive controller represents an amalgamation of a filtering technique to generate pseudo filtered tracking error signals (for the elimination of velocity measurements) and the theory of function approximation using CNN. The proposed controller ensures the local asymptotic stability and the convergence of the position error to zero. The proposed controller is robust not only to structured uncertainty such as payload variation but also to unstructured one such as disturbances. Moreover the computational complexity of the proposed controller is reduced as compared to the multilayered neural network controller. The validity of the control scheme is shown by simulation results of a two-link robot manipulator. Simulation results are also provided to compare the proposed controller with a controller where velocity is estimated by finite difference methods using position measurements only.

Multimode vibration control of a flexible structure using H/sub /spl infin//-based robust control

This paper presents the experimental results of a robust control scheme to suppress the vibration of a flexible structure. The feedback controller is designed using the H/sub /spl infin//-based robust control theory. For this purpose, a flexible bridge tower connected with a crane structure is considered to control its first five vibration modes using a static state feedback controller. A five-degrees-of-freedom reduced-order lumped parameter mass model is derived by neglecting high-frequency vibration modes. The neglected vibration modes constitute the unstructured system uncertainties. An attempt has been made to reduce the unmodeled uncertainties by placing actuators and/or sensors at the node points of a neglected mode. The H/sub /spl infin//-based control law is able to suppress the low-order vibration modes without any spillover instability due to neglected modes. The proposed control scheme is also shown to be robust against parameter variations. The performance of the control scheme is verified both by simulation and experimental studies.

Adaptive control of robot manipulators using fuzzy logic systems under actuator constraints

In this paper, a stable fuzzy adaptive controller for trajectory tracking is developed for robot manipulators without velocity measurements, taking into account the actuator constraints. The controller is based on structural knowledge of the dynamics of the robot and measurements of link positions only. The gravity torque including system uncertainty like payload variation, etc., is estimated by a fuzzy logic system (FLS). The adaptive controller represents an amalgamation of a filtering technique to eliminate velocity measurements and the theory of function approximation using FLS to estimate the gravity torque. The proposed controller ensures the local asymptotic stability and the convergence of the position error to zero. The proposed controller is robust not only to structured uncertainty such as payload parameter variation, but also to unstructured one such as disturbances. The validity of the control scheme is shown by simulations on a two-link robot manipulator.

Bounded robust control of nonlinear systems using neural network–based HJB solution

In this paper, a Hamilton–Jacobi–Bellman (HJB) equation–based optimal control algorithm for robust controller design is proposed for nonlinear systems. The HJB equation is formulated using a suitable nonquadratic term in the performance functional to tackle constraints on the control input. Utilizing the direct method of Lyapunov stability, the controller is shown to be optimal with respect to a cost functional, which includes penalty on the control effort and the maximum bound on system uncertainty. The bounded controller requires the knowledge of the upper bound of system uncertainty. In the proposed algorithm, neural network is used to approximate the solution of HJB equation using least squares method. Proposed algorithm has been applied on the nonlinear system with matched and unmatched type system uncertainties and uncertainties in the input matrix. Necessary theoretical and simulation results are presented to validate proposed algorithm.

Adaptive stick-slip friction and backlash compensation using dynamic fuzzy logic system

A dynamic fuzzy logic-based adaptive algorithm is proposed for reducing the effect of stick-slip friction and for the compensation of backlash. The control scheme proposed is an online identification and indirect adaptive control, in which the control input is adjusted adaptively to compensate the effect of these non-linearities. A tuning algorithm for fuzzy logic parameters is used to ensure stable performance. The efficacy of the proposed algorithm is verified on a one degree of freedom (1-DOF) mechanical mass system with stick-slip friction and on a one-link robot manipulator with backlash.

Contraction based adaptive control of a class of nonlinear systems

Adaptive control problem of nonlinear systems having dynamics in parametric strict feedback form is addressed here. Effort is made to derive adaptive methodology for controller design in contraction framework. General results and conditions for stabilization are derived using backstepping. At each step of recursive design, system is made contracting by suitable selection of control inputs. As contraction property is not intrinsic to the systems, so proposed strategy helps in identifying a coordinate transformation along with controller to establish contracting nature of the system. Contracting dynamics ensures exponential convergence of state trajectories to each other. Results are further extended to address control problem of systems having uncertain parameters. Tracking control problem of single link manipulator with actuator dynamics is addressed using the proposed scheme. Numerical simulations justify the effectiveness of the proposed methodology.