arXiv subjects

Shubhendu Bhasin

Publications and source records attributed to Shubhendu Bhasin.

At least 19 recordsLinked to original sources

arXiv2026

Output Feedback MPC with Adaptive Tubes

An output feedback model predictive control (MPC) framework with adaptive tubes is proposed for linear time-invariant systems subject to parametric and additive uncertainties. An adaptive observer provides point estimates of the system state, model parameters, and initial condition, while jointly updating the corresponding sets containing the true parameters and initial state. These estimates parameterize the constrained optimal control problem, enabling constraint tightening, terminal ingredients, and tube geometry to be updated as the estimates evolve. In contrast to standard robust tube-based MPC formulations, the proposed approach does not require a common quadratically stabilizing linear feedback gain across the parametric uncertainty set. As the available uncertainty information improves, the tube geometry evolves accordingly, resulting in an adaptive tube MPC framework with improved performance over time. Recursive feasibility and robust exponential stability are established, and a numerical example is presented.

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arXiv2026

Adaptive Tube MPC: Beyond a Common Quadratically Stabilizing Feedback Gain

This paper proposes an adaptive tube framework for model predictive control (MPC) of discrete-time linear time-invariant systems subject to parametric uncertainty and additive disturbances. In contrast to conventional tube-based MPC schemes that employ fixed tube geometry and constraint tightening designed for worst-case uncertainty, the proposed approach incorporates online parameter learning to progressively refine the parametric uncertainty set and update the parameter estimates. These updates are used to adapt the components of the MPC optimization problem, including the prediction model, feedback gain, terminal set, and tube cross-sections. As the uncertainty set contracts, the required amount of constraint tightening reduces and the tube shrinks accordingly, yielding less conservative control actions. Recursive feasibility, robust constraint satisfaction, and closed-loop stability are formally established. Furthermore, the framework does not require the existence of a common quadratically stabilizing linear feedback gain for the entire parametric uncertainty set, thereby relaxing a standard assumption in existing tube-based MPC formulations. Numerical examples illustrate the effectiveness of the proposed approach.

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arXiv2026

Adaptive Tracking Control of Euler-Lagrange Systems with Time-Varying State and Input Constraints

This paper presents an adaptive control framework for Euler-Lagrange (E-L) systems that enforces user-defined time-varying state and input constraints in the presence of parametric uncertainties and bounded disturbances. The proposed design integrates a time-varying barrier Lyapunov Function (TVBLF) with a saturated control law to guarantee constraint satisfaction without resorting to real-time optimization. A key contribution is the development of an offline, verifiable feasibility condition that certifies the existence of a feasible control policy for any prescribed pair of time-varying state and input envelopes. Additionally, we prove boundedness of all closed-loop signals. Real-time experiments conducted on a 2-DoF helicopter model validate the efficacy and practical viability of the proposed method.

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arXiv2025

Initial Excitation-based Adaptive Observers for Discrete-Time LTI Systems

In practical applications, the efficacy of a control algorithm relies critically on the accurate knowledge of the parameters and states of the underlying system. However, obtaining these quantities in practice is often challenging. Adaptive observers address this issue by performing simultaneous state and parameter estimation using only input-output measurements. While many adaptive observer designs exist for continuous-time systems, their discrete-time counterparts remain relatively unexplored. This paper proposes an initial excitation (IE)-based adaptive observer for discrete-time linear time-invariant systems. In contrast to conventional designs that rely on the persistence of excitation condition, which requires continuous excitation and infinite control effort, the proposed method does not require excitation for infinite time, thus making it more practical for stabilization tasks. We employ a two-layer filtering structure and a normalized gradient descent-based update law for learning the unknown parameters. We also propose modifying the regressors to enhance information extraction, leading to faster convergence. Rigorous theoretical analysis guarantees bounded and exponentially converging estimates of both states and parameters under the IE condition, and simulation results validate the efficacy of the proposed design.

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arXiv2025

State and Input Constrained Model Reference Adaptive Control with Robustness and Feasibility Analysis

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.

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arXiv2025

Model Reference Adaptive Control with Time-Varying State and Input Constraints

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.

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arXiv2025

Balancing Safety and Optimality in Robot Path Planning: Algorithm and Metric

Path planning for autonomous robots faces a fundamental trade-off between path length and obstacle clearance. While existing algorithms typically prioritize a single objective, we introduce the Unified Path Planner (UPP), a graph-search algorithm that dynamically balances safety and optimality via adaptive heuristic weighting. UPP employs a local inverse-distance safety field and auto-tunes its parameters based on real-time search progress, maintaining bounded heuristic behavior while maintaining superior clearance. To enable rigorous evaluation, we introduce the OptiSafe index, a normalized metric that quantifies the trade-off between safety and optimality. Extensive evaluation across 10 environments shows that UPP achieves a 0.94 OptiSafe score in cluttered environments, compared with 0.22-0.85 for existing methods, with only 0.5-1% path-length overhead in simulation and a 100% success rate. Hardware validation on TurtleBot confirms practical advantages despite sim-to-real gaps.

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arXiv2025

State Constrained Model Reference Adaptive Control with Input Amplitude and Rate Limits

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 demonstrate the effectiveness of the proposed algorithm.

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arXiv2025

State and Input Constrained Adaptive Tracking Control of Uncertain Euler-Lagrange Systems with Robustness and Feasibility Analysis

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.

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arXiv2025

Behavioral Inequalities

We introduce behavioral inequalities as a way to model dynamical systems defined by inequalities among their variables of interest. We claim that such a formulation enables the representation of safety-aware dynamical systems, systems with bounds on disturbances, practical design limits and operational boundaries, etc. We develop a necessary and sufficient condition for the existence of solutions to such behavioral inequalities and provide a parametrization of solutions when they exist. Finally, we show the efficacy of the proposed method in two practical examples.

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arXiv2025

Adaptive Output Feedback MPC with Guaranteed Stability and Robustness

This work proposes an adaptive output feedback model predictive control (MPC) framework for uncertain systems subject to external disturbances. In the absence of exact knowledge about the plant parameters and complete state measurements, the MPC optimization problem is reformulated in terms of their estimates derived from a suitably designed robust adaptive observer. The MPC routine returns a homothetic tube for the state estimate trajectory. Sets that characterize the state estimation errors are then added to the homothetic tube sections, resulting in a larger tube containing the true state trajectory. The two-tier tube architecture provides robustness to uncertainties due to imperfect parameter knowledge, external disturbances, and incomplete state information. Additionally, recursive feasibility and robust exponential stability are guaranteed and validated using a numerical example.

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arXiv2024

EEG-based 90-Degree Turn Intention Detection for Brain-Computer Interface

Electroencephalography (EEG)--based turn intention prediction for lower limb movement is important to build an efficient brain-computer interface (BCI) system. This study investigates the feasibility of intention detection of left-turn, right-turn, and straight walk by utilizing EEG signals obtained before the event occurrence. Synchronous data was collected using 31-channel EEG and IMU-based motion capture systems for nine healthy participants while performing left-turn, right-turn, and straight walk movements. EEG data was preprocessed with steps including Artifact Subspace Reconstruction (ASR), re-referencing, and Independent Component Analysis (ICA) to remove data noise. Feature extraction from the preprocessed EEG data involved computing various statistical measures (mean, median, standard deviation, skew, and kurtosis), and Hjorth parameters (activity, mobility, and complexity). Further, the feature selection was performed using the Random forest algorithm for the dimensionality reduction. The feature set obtained was utilized for 3-class classification using XG boost, gradient boosting, and support vector machine (SVM) with RBF kernel classifiers in a five-fold cross-validation scheme. Using the proposed intention detection methodology, the SVM classifier using an EEG window of 1.5 s and 0 s time-lag has the best decoding performance with mean accuracy, precision, and recall of 81.23%, 85.35%, and 83.92%, respectively, across the nine participants. The decoding analysis shows the feasibility of turn intention prediction for lower limb movement using the EEG signal before the event onset.

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arXiv2024

Lagrangian-based online safe reinforcement learning for state-constrained systems

This paper proposes a safe reinforcement learning (RL) algorithm that approximately solves the state-constrained optimal control problem for continuous-time uncertain nonlinear systems. We formulate the safe RL problem as the minimization of a Lagrangian that includes the cost functional and a user-defined barrier Lyapunov function (BLF) encoding the state constraints. We show that the analytical solution obtained by the application of Karush-Kuhn-Tucker (KKT) conditions contains a state-dependent expression for the Lagrange multiplier, which is a function of uncertain terms in the system dynamics. We argue that a naive estimation of the Lagrange multiplier may lead to safety constraint violations. To obviate this challenge, we propose an Actor-Critic-Identifier-Lagrangian (ACIL) algorithm that learns optimal control policies from online data without compromising safety. We provide safety and boundedness guarantees with the proposed algorithm and compare its performance with existing offline/online RL methods via a simulation study.

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arXiv2024

Adaptive Actor-Critic Based Optimal Regulation for Drift-Free Uncertain Nonlinear Systems

In this paper, a continuous-time adaptive actor-critic reinforcement learning (RL) controller is developed for drift-free nonlinear systems. Practical examples of such systems are image-based visual servoing (IBVS) and wheeled mobile robots (WMR), where the system dynamics includes a parametric uncertainty in the control effectiveness matrix with no drift term. The uncertainty in the input term poses a challenge for developing a continuous-time RL controller using existing methods. In this paper, an actor-critic or synchronous policy iteration (PI)-based RL controller is presented with a concurrent learning (CL)-based parameter update law for estimating the unknown parameters of the control effectiveness matrix. An infinite-horizon value function minimization objective is achieved by regulating the current states to the desired with near-optimal control efforts. The proposed controller guarantees closed-loop stability and simulation results validate the proposed theory using IBVS and WMR examples.

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arXiv2024

Computation of Maximal Admissible Robust Positive Invariant Sets for Linear Systems with Parametric and Additive Uncertainties

In this paper, we address the problem of computing the maximal admissible robust positive invariant (MARPI) set for discrete-time linear time-varying systems with parametric uncertainties and additive disturbances. The system state and input are subjected to hard constraints, and the system parameters and the exogenous disturbance are assumed to belong to known convex polytopes. We provide necessary and sufficient conditions for the existence of the non-empty MARPI set, and explore relevant features of the set that lead to an efficient finite-time converging algorithm with a suitable stopping criterion. The analysis hinges on backward reachable sets defined using recursively computed halfspaces and the minimal RPI set. A numerical example is used to validate the theoretical development.

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arXiv2023

BiCurNet: Pre-Movement EEG based Neural Decoder for Biceps Curl Trajectory Estimation

Kinematic parameter (KP) estimation from early electroencephalogram (EEG) signals is essential for positive augmentation using wearable robot. However, work related to early estimation of KPs from surface EEG is sparse. In this work, a deep learning-based model, BiCurNet, is presented for early estimation of biceps curl using collected EEG signal. The model utilizes light-weight architecture with depth-wise separable convolution layers and customized attention module. The feasibility of early estimation of KPs is demonstrated using brain source imaging. Computationally efficient EEG features in spherical and head harmonics domain is utilized for the first time for KP prediction. The best Pearson correlation coefficient (PCC) between estimated and actual trajectory of $0.7$ is achieved when combined EEG features (spatial and harmonics domain) in delta band is utilized. Robustness of the proposed network is demonstrated for subject-dependent and subject-independent training, using EEG signals with artifacts.

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arXiv2023

Modeling and parametric optimization of 3D tendon-sheath actuator system for upper limb soft exosuit

This paper presents an analysis of parametric characterization of a motor driven tendon-sheath actuator system for use in upper limb augmentation for applications such as rehabilitation, therapy, and industrial automation. The double tendon sheath system, which uses two sets of cables (agonist and antagonist side) guided through a sheath, is considered to produce smooth and natural-looking movements of the arm. The exoskeleton is equipped with a single motor capable of controlling both the flexion and extension motions. One of the key challenges in the implementation of a double tendon sheath system is the possibility of slack in the tendon, which can impact the overall performance of the system. To address this issue, a robust mathematical model is developed and a comprehensive parametric study is carried out to determine the most effective strategies for overcoming the problem of slack and improving the transmission. The study suggests that incorporating a series spring into the system's tendon leads to a universally applicable design, eliminating the need for individual customization. The results also show that the slack in the tendon can be effectively controlled by changing the pretension, spring constant, and size and geometry of spool mounted on the axle of motor.

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arXiv2023

Adaptive Gravity Compensation Control of a Cable-Driven Upper-Arm Soft Exosuit

This paper proposes an adaptive gravity compensation (AGC) control strategy for a cable-driven upper-limb exosuit intended to assist the wearer with lifting tasks. Unlike most model-based control techniques used for this human-robot interaction task, the proposed control design does not assume knowledge of the anthropometric parameters of the wearer's arm and the payload. Instead, the uncertainties in human arm parameters, such as mass, length, and payload, are estimated online using an indirect adaptive control law that compensates for the gravity moment about the elbow joint. Additionally, the AGC controller is agnostic to the desired joint trajectory followed by the human arm. For the purpose of controller design, the human arm is modeled using a 1-DOF manipulator model. Further, a cable-driven actuator model is proposed that maps the assistive elbow torque to the actuator torque. The performance of the proposed method is verified through a co-simulation, wherein the control input realized in MATLAB is applied to the human bio-mechanical model in OpenSim under varying payload conditions. Significant reductions in human effort in terms of human muscle torque and metabolic cost are observed with the proposed control strategy. Further, simulation results show that the performance of the AGC controller converges to that of the gravity compensation (GC) controller, demonstrating the efficacy of AGC-based online parameter learning.

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