**Current Research work**

##### 1. Guidance and Control of Missiles

In our lab, optimal guidance algorithms are designed to meet the terminal and path constraints. The technique used to develop these guidance algorithms is ** Model Predictive Static Programming (MPSP)**: MPSP is inspired from the predictive nature of Model Predictive Control and takes the advantage of computational efficiency of Approximate Dynamic programming. MPSP satisfies the terminal constraints by correcting the control history at each grid point iteratively by a close form update formula.

A revised version of MPSP is also formulated where the control variable is represented as weighted function of some basis functions. This variety is known as ** ‘Quasi-Spectral MPSP’ (QS-MPSP)**. It reduces the optimization dimension and thereby reduces computation time of MPSP. Path constraints are satisfied by another variety of Quasi-Spectral MPSP known as

**. In this formulation the path constraints (look angle and lateral acceleration) are presented as inequality constraints on control variable only. The solution is obtained by minimizing a quadratic cost subjected to terminal equality and path inequality constraints using standard nonlinear optimization techniques.**

*‘Constrained QS-MPSP’*##### 2. Deciphering Control Architecture of Voluntary Movements Using Movement Variability

##### 3. Integrated Guidance and Control of a Lunar Module for Optimal Soft-landing

In addition, the algorithm needs to have the capability to re-designate the landing to an alternate site in the precision phase in case any hazard is detected at the designated landing site. Also the terminal orientation of the lander needs to be ensured vertical with respect to local ground.

Additional requirements include that the algorithm should be computationally very efficient to be amenable for onboard implementation and it should preferably have sufficient algorithmic generality to be extensible to future missions. The algorithms to be developed will be based on nonlinear optimal and nonlinear control theory and is expected to lead to substantial amount of fuel saving with enough robustness. Moreover, the general philosophy will be quite useful for other similar missions.

##### 4. Nature Inspired Multiagent Multitarget Interception

Some possible solutions exist for missile interception problems which are based on Lyapunov Theory, variants of Proportional-Navigation (PN) guidance algorithms etc. Multiagent robotics navigation and associated problems like Coverage Control etc. have commended several classical approaches based on Proximity Functions, Navigation Functions etc. But many of them will require all-to-all communication, which is impractical for large number of agents in an unknown environment. No single guidance and control strategy is going to achieve the three phases. Hence, inspiration from nature is sought to emulate the foraging behavior of wolf packs, insects like ants, bees etc., the flocking behavior of birds, fish shoals etc. In our current body of research, the objective is not to mimic the exact respective foraging behaviors which are complex processes and often largely empirical. Rather cues and signals that lead to successful foraging in nature we will be identified and incorporate those functionalities in the robots for successful interception in unknown environment.

Preliminary position based switched control law have been proposed by extending pack hunting behavior of wolves (originally proposed by Escobedo *et al*) to the MA-MT scenario. A combined velocity and position based behavioral control law based on local interaction and vision cone (originally proposed by Borzi *et al*) has been proposed which works in realistic predator-prey scenarios. Current efforts are underway to develop identical laws based on topological distance. Another direction of research underway is to frame the MA-MT problem as a minimization problem and solve as a Dynamic Optimization Problem. Hybrid variants of Evolutionary Algorithms are being explored to be suitable candidates.

##### 5. Robust Adaptive Flight Control Design of Air-breathing Hypersonic Vehicles

The main objective of this research is to develop a “*state-constrained robust nonlinear adaptive controller*” for hypersonic vehicles to constrain the states within the prescribed bounds, and also minimize the possibility of inlet unstart during cruise phase of flight. The developed controller should ensure the stability of the vehicle in presence of modelling inaccuracy of the vehicle dynamics, thereby minimizing the necessity to have a very high fidelity model. The key idea be to quickly learn the model discrepancy function online using a fast disturbance observer and then utilize the updated model in state-constrained nonlinear control theory framework

##### 6. Air Traffic Modelling and Optimal Management

The objective of research to design an algorithm which designs an optimal trajectory for aircraft in consensus with all other aircraft flying at same time. The delays in arrival, departure at airport affects the airport utility which may causes changes in many flight plans.

Ref : https://thepointsguy.com/2017/06/air-traffic-control-privitization/

##### 7. Adaptive Control and Guidance of High Performance Aircrafts and UAVs

_{1}adaptive control and guidance algorithms for auto landing of UAVs with external disturbances, Development of Neuro-adaptive control and guidance with state estimation for Autolanding of UAVs with reactive collision avoidance, Development of Image processing techniques with Extended Kalman filter for runway / Road identification for auto landing applications of UAV.

UAVs are ubiquitous now a days. UAVs are being used in civilian, commercial and military applications. UAVs fly at low altitude and are susceptible to collision with other UAVs flying at similar altitude range. The main objective of this research is to develop a control and guidance algorithm with image processing and state estimation to sense and avoid collision with obstacle UAV while performing auto landing operations.

**Figure **: Autolanding of UAV with reactive collision Avoidance

##### 8. Guidance and Control for Exo-atmospheric Kill Vehicles

- Extremely high closing velocities
- Very small time-to-go (time to intercept target).

Low dynamic pressures at high altitudes results in the lack of effectiveness of aerodynamic control surfaces

Fig 2: Exo-atmospheric missile engagement [1]

The missile however, has constraints on maneuverability due to:

- Thrust constraints on the Divert and Attitude Control System (DACS).
- Limited seeker field of view (FoV)

A guidance and control law is required to ensure that the missile detects the target throughout the engagement and ultimately carries out a successful intercept

Fig 2: THAAD Anti-Ballistic Missile system [2]

Conventional guidance and control techniques use individual loops to run guidance, control and estimation computations and have proved to be useful but introduce time lags between various loops. For high speed missiles in the terminal phase, where engagement times are in the order of a few seconds, a small increase in time lag can make all the difference between mission success and failure.

The project seeks to develop innovative, robust and effective guidance and control architectures for an Exo-atmospheric Kill Vehicle (EKV), that can run on-board the missile, Such as: Optimal control based guidance and control laws, integrated guidance and control (IGC) algorithms, …etc.

Image References:

[1] http://www.erewise.com/sites/default/files/image_article/1018/popsci-missile-defense-infographic.jpg

[2] https://commons.wikimedia.org/wiki/File:Wfm_thaad_diagram.svg

##### 9. Artificial Pancreas For Type-1 Diabetes Patients Of India

At present, a majority of the T1DM patients manually inject exogenous insulin by monitoring their glucose concentrations throughout the day. Our project aims at developing an Artificial Pancreas System in order to automate the exogenous insulin infusion process. The closed loop system would use the glucose concentrations obtained from continuous glucose monitoring sensors as input to calculate the required insulin dosage.

These estimated physiological parameters for Indian subjects will help customize the glucose-insulin model for Indian population. The mathematical model customized for Indian population will then be used for developing a glucose control feedback loop to determine the insulin dosage required. Advanced control strategies such as variants of model predictive control which predicts the glucose trajectory in the future for given insulin input. The mathematical model does not exactly replicates the glucose insulin dynamics as in human body, therefore there are many unaccounted variations which needs to be taken care of, and this will be done using neuro adaptive control. Finally arriving at most suitable algorithm for artificial pancreas system.

The control algorithm will then be embedded on a cost effective insulin pump that is being indigenously developed at IISc. The final closed loop system will comprise of a CGMS that wirelessly transmits glucose reading to the control algorithm embedded on the insulin pump.