• Using the robust features of SM theory to compute estimates of states, disturbances, parameters, and uncertainties.

• Application to constrained state estimation and control, fault diagnosis and reconfiguration control, and state/ output feedback control tasks for mechatronic systems.

• Focus on higher order SMC techniques- Linear STA, Terminal SMC, Recursive NSTSM, Composite SMC, adaptive SMC etc.

Representative Works: IJACSP-2016 TIE-2017 MSSP-2019

Current Focus : Interval SMC, TS-Fuzzy SMC

• Design of a central intelligent controller which takes in information of robust estimators/ classifiers of the environment and vehicle states and provide adaptive authority allocation to complete a driving task

• The influence of actuators dynamics, mechanical limitations, balancing issues of safety versus comfort and environmental disturbances would be addressed for problems such as integrated .

• Focus on friction, road level, bank angle, hydraulic actuator for suspensions, path-following controllers etc.

Representative Works: TIE-2020 T-ITS-2020 TVT-2014 T-ITS-2016

Current Focus : Friction-Road Level Classifier, Intelligent nonlinearity & disturbance estimation

• Multiple driver attributes such as workload, skill, behavior, style among others to be identified for several objectives such as determining assistance, authority transition, balancing safety and fuel economy, and behavior mapping .

• Information from sensors such as GPS, Lidar, odometers, and using naturalistic driver data sets, simulator data sets, real-time driving data sets the challenge is to develop novel learning algorithms- unsupervised, supervised, combination of supervised-unsupervised to model driver .

• Focus on the challenge of integrating intention, environment, vehicle response, weather conditions, and traffic scenarios into such detection problems

Representative Works: ITSC-2018 ACC-2019 SMC-2020

Current Focus : Learning based driver model adaptation, enviornmental influence on intent.

• Shared control (direct/indirect/haptic) to support graded and gradual transitions of control authority and enable the human driver to monitor automation actions.

• Specific challenges to be addressed are Necessary driver characteristic to be adapted, influence of external factors (weather, traffic, disturbances) to be accounted for, influence of assistance on trust, skill, experience, maneuver specific or generic design, how to asses performance, how to ensure consensus control, and personalize assistance.

Representative Works: IETCTA-2019 IET-ITS 2019

Current Focus : Game theoritic human centered design, learning based shared control.