Poulomee Ghosh
PUBLICATIONS
State and Input Constrained Model Reference Adaptive Control with Robustness and Feasibility Analysis
P. Ghosh and S. Bhasin
in IEEE Transactions on Automatic Control (TAC), 2026. (To appear)
We propose a model reference adaptive controller (MRAC) for uncertain linear time-invariant (LTI) plants with user-defined state and input constraints in the presence of unmatched bounded disturbances. Unlike popular optimization-based approaches for constrained control, such as model predictive control (MPC) and control barrier function (CBF) that solve a constrained optimization problem at each step using the system model, our approach is optimization-free and adaptive; it combines a saturated adaptive controller with a barrier Lyapunov function (BLF)-based design to ensure that the plant state and input always stay within pre-specified bounds despite the presence of unmatched disturbances. To the best of our knowledge, this is the first result that considers both state and input constraints for control of uncertain systems with disturbances and provides sufficient feasibility conditions to check for the existence of an admissible control policy. Simulation results, including a comparison with a robust MRAC, demonstrate the effectiveness of the proposed algorithm.
P. Ghosh and S. Bhasin
International Journal of Systems Science, pp.1-13, 2025.
This paper proposes a robust model reference adaptive controller (MRAC) for uncertain multi-input multi-output (MIMO) linear time-invariant (LTI) plants with user-defined constraints on the plant states, input amplitude, and input rate. The proposed two-layer barrier Lyapunov function (BLF)-based control design considers the input and the input rate as states that are constrained using two BLFs in the first layer, while another BLF in the second layer constrains the plant states. The adaptive control law ensures that the plant states, input amplitude, and input rate remain within the user-defined safe sets despite unmatched bounded disturbances. Sufficient conditions for the existence of a feasible control policy are also provided. To the best of the authors' knowledge, this is the first optimization-free method that imposes user-defined constraints on the state, input, and input rate and also provides verifiable feasibility conditions in the presence of parametric uncertainties and disturbances. Simulation results on an aircraft model demonstrate the effectiveness of the proposed algorithm.​
P. Ghosh and S. Bhasin
American Control Conference (ACC), CA, USA, 2023.
This paper proposes a novel control architecture for state and input constrained Euler-Lagrange systems with parametric uncertainties. A simple saturated controller, designed to satisfy the constraints on the control input is strategically coupled with Barrier Lyapunov Function (BLF) based controller which ensures state constraint satisfaction. Without any restrictive assumption, the proposed controller guarantees both the state and input remain within pre-specified safe regions while simultaneously ensuring asymptotic tracking. The controller also verifies that all the closed-loop signals remain bounded and the trajectory tracking error converges to zero asymptotically. Efficacy of the proposed controller in terms of constraint satisfaction and tracking performance are verified by the simulation results.
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P. Ghosh and S. Bhasin
IEEE Conference on Decision and Control (CDC), Mexico, 2022.
​Satisfaction of state and input constraints is one of the most critical requirements in control engineering applications. In classical model reference adaptive control (MRAC) formulation, although the states and the input remain bounded, the bound is neither user-defined nor known a-priori. In this paper, an MRAC is developed for multivariable linear time-invariant (LTI) plant with user-defined state and input constraints using a simple saturated control design coupled with a barrier Lyapunov function (BLF). Without any restrictive assumptions that may limit practical implementation, the proposed controller guarantees that both the plant state and the control input remain within a user-defined safe set for all time while simultaneously ensuring that the plant state trajectory tracks the reference model trajectory. The controller ensures that all the closed-loop signals remain bounded and the trajectory tracking error converges to zero asymptotically.
COMMUNICATED PAPERS
P. Ghosh and S. Bhasin
This paper proposes an adaptive tracking controller for uncertain Euler-Lagrange (E-L) systems with user-defined state and input constraints in presence of bounded external disturbances. A barrier Lyapunov function (BLF) is employed for state constraint satisfaction, integrated with a saturated controller that ensures the control input remains within pre-specified bounds. To the best of the authors' knowledge, this is the first result on tracking control of state and input-constrained uncertain E-L systems that provides verifiable conditions for the existence of a feasible control policy. The efficacy of the proposed controller in terms of constraint satisfaction and tracking performance is demonstrated through simulation on a robotic manipulator system.
P. Ghosh and S. Bhasin
This paper presents a model reference adaptive control (MRAC) framework for uncertain linear time-invariant (LTI) systems subject to user-defined, time-varying state and input constraints. The proposed design seamlessly integrates a time-varying barrier Lyapunov function (TVBLF) to enforce state constraints with a time-varying saturation function to handle input limits. These time-varying constraints can be designed as performance functions to shape transient and steady-state behaviors for both state and input. A key contribution is the derivation of a verifiable, offline feasibility condition to check the existence of a valid control policy for a given set of constraints. To the best of our knowledge, this is the first adaptive control methodology to simultaneously handle both time-varying state and input constraints without resorting to online optimization. Simulation results validate the efficacy of the proposed constrained MRAC scheme.​