This study focuses on Physical Human-Robot Interaction (pHRI) tasks that require a close coupling between safety constraints and compliance with human intentions. To address this, the authors propose a novel switched admittance controller for an n-link manipulator to comply with external forces while ensuring safety in the workspace. The controller switches between two reference models to generate a reference trajectory that maintains the safety constraints. Stability analysis of the switched reference model is performed by selecting an appropriate Common Quadratic Lyapunov Function (CQLF), which ensures the asymptotic convergence of the trajectory tracking error. The effectiveness of the proposed controller is demonstrated through simulation on a 4-DOF SCARA (RRPR) robot manipulator.

In this article, we introduce a data-driven feedback linearizing controller for robotic manipulators with uncertain dynamics based on the augmented framework of time delay estimation (TDE) and a residue acceleration observer. While the time-delayed estimation approach in the inner-loop uses immediate past measurements of control effort and state to get an online estimate of the unknown manipulator dynamics, the residue acceleration observer in the outer-loop helps to nullify the estimation error on the fly, thus establishing a linear and decoupled dynamics. Once the linear dynamics is established, a PD controller helps to track the desired output trajectories. Simulation results for a two-link manipulator using the proposed controller, along with a delay-dependent convergence analysis, are shown to validate the proposition.

Time delay analysis is very crucial when it comes to the synchronization of chaotic systems. All practical dynamical systems are described by differential equations, hence the presence of time delay in dynamics becomes a pivotal part of the system analysis. So, in this article, an attempt has been made to study the synchronization problem of two identical same-ordered time-delayed chaotic systems, in strict-feedback form as described in (2) and (3) in master and slave configuration. Unlike other conventional nonlinear control techniques, the back-stepping control design strategy is used to serve the purpose of synchronization. The main advantage of using the back-stepping control technique is that it utilizes only a single scalar controller to attain synchronization among different chaotic systems. A generalized control function$u\in R$is designed for the same. For verification of the theoretical results, the 3D time-delayed Genesio-Tesi chaotic system is considered both a master and slave system. Simulation results are provided to support the proposition.

This article studies the behaviour of switched mechanical systems with respect to periodic perturbations. The switching considered is due to the one-sided stiffness present in many practical engineering systems. Due to one-sided stiffness, the overall system behaves like nonlinear dynamics. One peculiar phenomenon of nonlinear dynamics is the presence of harmonics in the response along with fundamental components when excited with periodic input. Hence, the response of these types of mechanical systems contains k-periodic solutions$(\mathrm{k}=2,3,4)$. Existence of these leads to the cause of unintended vibrations, which is adversarial for the system. In this paper, Contraction theory has been explored to suppress the components of k-periodic solutions in the output. The contractivity of the overall system ensures entrainment property which makes the output frequency the same as that of the input perturbation. A matrix measure is used to verify the Contraction of the overall switched system, which suggests the parameter regime for entrainment. Simulation results are attached to validate the proposition.

A consideration of actuator saturation is an important aspect to study the effectiveness of a designed controller in practice. However, the conventional Lyapunov theory-based design is not always suitable to analyze the quantitative behavior of closed-loop system. This article presents a time-scale redesign-based saturated tracking controller for a class of feedback linearizable multi-input–multi-output (MIMO) nonlinear systems. The proposed controller is built upon the frameworks of contraction and partial contraction theories which ensures that bounded tracking performance as well as quantify the steady-state error bounds in terms of the various control design parameters. Notably, in contrast to the existing Lyapunov-method-based designs, the proposed approach allows to tune the controller performance without arbitrary reduction of singular perturbation parameters. Therefore, the vulnerability of the controller toward actuator saturation and noise, due to the ill-effects of high-gains stemming from the conventional high-gain controllers, are reduced. The extensive experimental results using a wheeled mobile robot are provided to demonstrate the effectiveness of the proposed controller.

This paper proposes to design a robust controller for a class of nonlinear networked control systems using aperiodic feedback information. The parameter variation and system nonlinearity are considered as sources of uncertainty. To tackle uncertainty in system dynamics, a linear robust control law is derived using optimal control theory. Two different architectures of closed-loop systems are considered. In the first one, system and controller are not collocated; instead they are interconnected by means of a shared communication network. In the second architecture, however, sensors and controller are connected through a shared communication channel. In both architectures, the feedback loop is closed through the network. To save network bandwidth, the state and input information are transmitted aperiodically through the feedback loop. To this aim, the paper adopts an event-triggered control approach to reduce the transmission overhead. We show that the designed event-triggered controller achieves a trade-off between control performance and saving network bandwidth in the presence of uncertainty. The developed control algorithm is implemented and validated numerically on a classical nonlinear system.

Computational resources are vital especially for systems with limited computational capabilities. We propose a computationally efficient formulation for Nonlinear Model Predictive Control (NMPC) algorithm for a discrete time nonlinear system affected by additive and multiplicative disturbances. Factors which contribute to the computationally efficient formulation are: avoiding stability related terminal cost and constraints in the optimization problem formulation, varying prediction horizon online according to the cost value and use of event triggering. Simulations and analysis were carried out in nonlinear models of mechanical system and the Quadruple Tank Process (QTP). The effect of disturbance on the control algorithm was considered and solutions suggested.

Time delay is an inevitable aspect to study the behavior of a designed controller in practice. Furthermore, in practical systems, uncertainties broadly exist because of the modeling errors. Thus, the robust stabilization of time delay systems (TDS) with uncertainty is investigated in this paper via sliding mode control (SMC) approach. The novelty comes from the exploitation of the Lambert W function for obtaining the parameters of the sliding manifold. Sufficient stability conditions are derived for designing of a robust sliding manifold along with the reaching and the switching control law. An algorithm is presented to elaborate the proposed design procedure. The thus designed controller guarantees asymptotic stability for the nominal system as well as when the system is subjected to the matched uncertainty. Further, the proposed approach guarantees uniformly ultimately bounded (UUB) stability of all motions of the TDS in the presence of mismatched uncertainty as well. Lastly, computer simulations are included to verify the efficacy of the presented controller and the sliding manifold design methodology.

A new adaptive robust control strategy, christened as Adaptive Time-delayed Robust Control (ATRC) is presented in this paper for trajectory tracking control of a class of Euler-Lagrange systems subjected to uncertainties with unknown bounds. The adaptive law to compute the switching gain of the conventional adaptive-robust controllers require either complete nominal modelling of the system or uncertainty bound. The proposed control framework amalgamates the best features of the switching control logic and time-delayed logic. The proposed control strategy approximates the unknown dynamics through time-delayed logic, and the switching logic provides robustness against the approximation error. A novel adaptive law for the switching control is developed which does not require uncertainty modelling or the knowledge of its bound and the switching gain adapts itself according to the tracking error incurred by the system. Moreover, a new design methodology and stability criterion for time-delayed control is proposed. Experimental results of the proposed methodology using a nonholonomic wheeled mobile robot (WMR) is presented and improved tracking accuracy of the proposed control law is noted compared to the conventional time-delayed control and time-delayed control with gradient estimator.

This paper proposes a framework to design an event-triggered based robust control law for nonlinear uncertain robot manipulator. Load variations and unmodeled system dynamics of manipulator are the primary sources of both system and input uncertainties. A static event-triggering rule is employed to realize the proposed robust control law. Derivation of static event-triggering rule with a positive inter-event time and corresponding stability criteria for uncertain manipulator dynamics are the key contribution of this paper. Validation of proposed control technique is carried out numerically on a two-link SCARA type robot manipulator. Simulation results show that measurement error norm is always bounded by the state dependent threshold and also ensures that asymptotic convergence of manipulator states in the presence of both system and input uncertainty.

This paper addresses the problem of synchronization analysis of Takagi-Sugeno (T-S) fuzzy complex dynamical network in the presence of interval time-varying delay. A novel synchronization criteria is obtained in terms of linear matrix inequalities (LMIs) by defining a Lyapunov-Krasovskii functional (LKF). The delay-range is divided into two segments for stability analysis. A tighter bounding technique for integral terms arising in the derivative of LKF is developed. The Wirtinger inequality and a bounding technique developed by authors are employed to develop the stability criteria. Further, the proposed approach has also been adopted to develop stability criteria for a single T-S fuzzy system with interval time-varying delay. It is shown with the help of numerical examples that the proposed results are less conservative as compared to the other recently reported results.

This study investigates the design and analysis of an estimator for unknown frequencies of a sinusoid in the presence of additive noise. A dynamic stochastic estimator is proposed to ensure simultaneous globally convergent estimation of the state and the frequencies of a sinusoid comprising multiple frequencies. Approach given in this study exploits the results of stochastic contraction theory and the observers. The concept of contraction theory related to semi‐contracting systems is used to show the asymptotic convergence of the proposed non‐linear estimator. The boundedness and convergence of the state and frequencies estimates for all initial conditions and frequency values has been shown analytically. The proposed estimator is generalised to estimate n ‐unknown frequencies of a given noisy sinusoid. Numerical simulations of estimator are presented for different combinations of frequencies to justify the claim.

In this paper, a Hamilton-Jacobi-Bellman (HJB) equation based optimal control algorithm is proposed for a bilinear system. Utilizing the Lyapunov direct method, the controller is shown to be optimal with respect to a cost functional, which includes penalty on the control effort and the system states. In the proposed algorithm, Neural Network (NN) is used to find approximate solution of HJB equation using least squares method. Proposed algorithm has been applied on bilinear systems. Necessary theoretical and simulation results are presented to validate proposed algorithm.

This paper aims to demonstrate application of type-2 fuzzy logic systems (FLS) to predict a critical parameter of gas turbine in a power plant viz., the Turbine Inlet Temperature (TIT). Maintaining higher TIT than allowed severely affects the life of the components whereas operating at lower TIT may cause low efficiency and low load. Nonavailability of TIT, which cannot be measured directly, puts great limitations on efficient gas turbine operation. Accurate estimation of this parameter requires significant computing power and the time required places limitations on the conventional modeling methods for use in real time applications. It is also demonstrated here by way of comparison, that a type-2 FLS is more robust in the presence of noise uncertainties than a type-1 conventional FLS for this application. Results are verified through the practical plant data obtained from an 88 MW gas turbine power plant.

This paper presents a successful demonstration of application of type-2 fuzzy logic systems (FLS) to predict a critical parameter of Gas Turbine in a power plant viz., the compressor discharge pressure. The loss of Compressor Discharge Pressure (CDP) measurement leads to loss of few megawatts electricity generation. It is also demonstrated here by way of comparison, that a type-2 FLS is more robust in the presence of noise uncertainties than a type-1 conventional FLS for this application. Results are verified through the practical plant data obtained from a 110 MW gas turbine power plant.

In this paper, associated Hermite (AH) series based learning control (AH-ILC) scheme with experience inclusion has been proposed for the tracking control of robot manipulators including actuator dynamics. Approximation of system function has been done by orthonormal projections of Hermite polynomials. AH basis functions are orthogonal in euclidean space in both time, frequency domains, since each one is isomorphic to its Fourier transform, thus the Hermite coefficients are independent to each other. AH series is used to approximate the desired and actual trajectories of the system into finite number of Hermite coefficients. AH-ILC is designed in such a way that forces the Hermite coefficients of actual output approach to the corresponding coefficients of desired trajectory which are known constants, such that the tracking control of robot manipulator is achieved. Instead of zero initial input assumption as in most of the ILC algorithms, this paper includes the idea of using past trajectory tracking experiences on initial input selection using locally weighted learning approach for new trajectory tasks. The learning controller is based on the local input-output information. A priori structure or parameters of the system model are not required. The learning controller improves tracking performance as iteration progress and experience inclusion concept reduces initial iteration errors as well as improves the convergence of bounded error. Proposed ILC algorithm has been verified through detailed simulation studies.

In this paper, a stable neuro adaptive controller for trajectory tracking is developed for robot manipulators without velocity measurements, taking into account the actuator constraints. The controller is based on structural knowledge of the dynamic equations of the robot and measurements of joint positions only. The gravity torque which may include payload variation and disturbances etc represent system uncertainty, which is estimated by a single layer Chebyshev neural network (CNN). The adaptive controller represents an amalgamation of a filtering technique to eliminate velocity measurements and the theory of function approximation using CNN to estimate the gravity torque. The proposed controller ensures the local asymptotic stability and the convergence of the position error to zero. The proposed controller is robust not only to structured uncertainty such as payload parameter variation but also to unstructured one such as disturbances. The validity of the control scheme is shown by simulation studies on a two link robot manipulator.

The stable fuzzy adaptive controller for trajectory tracking is developed for robot manipulators without velocity measurements, taking into account the actuator constraints. The controller is based on structural knowledge of the dynamics of the robot and measurements of link positions only. The gravity torque which may include payload variation etc. represents system uncertainty, which is estimated by a fuzzy logic system (FLS). The adaptive controller represents an amalgamation of a filtering technique to eliminate velocity measurements and the theory of function approximation using FLS to estimate the gravity torque. The proposed controller ensures the local asymptotic stability and the convergence of the position error to zero. The proposed controller is robust not only to structured uncertainty such as payload parameter variation but also to unstructured one such as disturbances. The validity of the control scheme is shown by simulations of a two link robot manipulator.