Area of Interests:
Robust Adaptive Control of Constrained Nonlinear Systems
Extremum Seeking
Infinite-dimensional control system (PDE)
Neural Network
Summary of the selected research works:
In this work, a novel robust adaptive barrier Lyapunov function (BLF)-based backstepping controller has been proposed for a class of interconnected, multi-input–multi-output (MIMO) unknown nonaffine nonlinear systems with asymmetric time-varying (ATV) state constraints. The design involves a neural-network-based online approximator to cope with uncertain dynamics of the system. To tune its weights, a novel adaptive law is proposed based on the Hadamard product. A theorem has also been proposed to have the bounds on virtual control signals beforehand. This theorem eliminates the need for tedious offline computation for the feasibility condition on the virtual controller in BLF-based controller design. To overcome the problem of unknown control gain in the nonaffine system, Nussbaum gain has been used during the design. A simulation study on the robot manipulator in task space has been performed to illustrate the effectiveness of the proposed methodology.
This work is concerned with a new problem of designing a robust adaptive backstepping controller for nonlinear systems where state constraints are an explicit function of time and state variables, i.e., pure state constraints. Furthermore, a disturbance observer is designed to cope with the disturbances in the systems. Finally, a numerical example is demonstrated to show the efficacy of the proposed theoretical results.
Adaptive control using neural network provides a real-time systematic approach to achieve or maintain a desired level of control system performance when dynamics are unknown. In this work a novel approach for designing an adaptive controller with input as relative pixel density from a fuzzy system for an automated guided vehicle with a vision sensor has been proposed. The fuzzy system computes relative pixel density from vision sensor data while minimizing uncertainties due to illumination, occlusion and obscure images. It provides a methodology to apply an advanced nonlinear intelligent control technique for vision-based path tracking problems. The proposed strategy has been applied for path tracking problems on an indigenously developed vehicle. The results obtained show the efficiency of the proposed approach and ease of applying different control techniques for vision sensor based plants.