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This paper presents a techno-economic analysis of an absorption-based CO2capture process using Monoethanolamine (MEA) as the solvent for pulp and paper mill limekiln. The flue gas specifications were obtained from published limekiln data of a theoretical pulp and paper mill. The process was simulated in Aspen Plus and linked to CAPCOST using a python script for the cost calculations. The CO2capture cost estimates were compared to the only CO2capture costs data available in the literature for limekiln flue gas. Comparing the cost breakdown between the published data and this study, the capital cost difference was found to be highest for the stripper and the compression and dehydration sections. Further, the capture cost sensitivity analysis, evaluating the impacts of key parameters, including flue gas CO2mol%, MEA, electricity, and steam, showed that the capture costs varied from $70 to $82 per tonne of CO2captured.

Intrinsically, many real control problems are mathematically challenging. Thus, we often relax the original problem to obtain a tractable problem. In this paper, we consider a computational technique to solving a nonlinear stochastic optimal control problem in a direct manner. Our computational approach employs the idea of the recently developed technique, called deep unfolding. Deep unfolding is a deep learning method that is model-based, used to accelerate iterative algorithms. For the finite-horizon optimal control problem, we regard each state transition of the discrete-time dynamical system with control inputs and disturbances as an iteration step. Then, we unfold those state transitions into the layers to construct a deep neural network so that each of those layers contains a trainable parameter of the control input. This produces a computational graph. Once the computational graph is fixed, the control inputs are computed by training the deep neural network. This open-loop method is then integrated into model predictive control to close the loop. The advantages of our computational technique are not only the ability of handling nonlinear state transitions and non-Gaussian disturbances, but also its simplicity. The feasibility and benefit of the proposed technique are demonstrated by numerical experiments using a continuous stirred tank reactor model, which is highly nonlinear.

In this paper, adaptive radial basis function neural network based sliding mode control of a fast acting superconducting magnetic energy storage (SMES) is reported. With the converter interface SMES is installed and connected with the wind-diesel micro grid to carry the required power exchange to improve the system frequency. With sliding surface design and neural network using a radial basis function, a sliding mode controller action is used to control the converter, and achieve the desired operation of SMES. Computer simulations are performed and presented to show the superiority of the proposed methodology with the system subjected to load and wind power variations.

Graph Neural networks have shown the potential to solve large-scale combinatorial optimisation problems. The following work demonstrates how the graph neural networks can be utilised to solve the sensor placement problem, a combinatorial optimisation problem. This paper focuses on the problem of placing pressure sensors optimally in a Water Distribution Network (WDN). The problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) or Ising model, a combinatorial optimisation problem. The paper outlines the QUBO and Ising formulations for the sensor placement problem, starting from the network topology and other relevant features. A detailed procedure is presented for solving the problem by minimising its Hamiltonian using PyQUBO, an open-source Python Library. Finally, the proposed methods are applied to a real Water Distribution Network for evaluation.

This paper tackles the challenges of tracking control pertaining to the pitch and yaw motions of an autonomous underwater vehicle (AUV) designed in a torpedo-like configuration. To capture pitch and yaw motion dynamics, a two-degrees-of-freedom (2-DOF) nonlinear system is employed, and the devised control strategy is rooted in the Immersion and Invariance (I&I) methodology. Achieving the desired depth and steering capabilities in the underwater domain requires an effective control strategy for both pitch and yaw angles, ensuring the vehicle's stability and minimizing the influence of roll angles. The control objective is to achieve precise tracking by incorporating a stable target (first-order) dynamic system into the second-order closed-loop pitch and yaw motion dynamics. The proposed control law, based on I&I, asymptotically stabilizes the tracking position of the AUV's pitch and yaw motions. In a companion paper, the stability has been demonstrated within the framework of Lyapunov theory, and the robustness of the proposed control approach has been rigorously evaluated against parametric uncertainties and underwater disturbances in the simulation studies.

This note gives an alternate derivation of generalized Ackermann's formula (GAF). Proposed derivation extends the existing Ackermann's gain formula to multi-input systems. Final result of the paper is obtained by the application of Cayley-Hamilton theorem. We propose two additional variations of Ackermann's gain formula. These formulae contain additional degrees of freedom which can be utilized to meet additional requirements along with placing all the poles at the desired locations. Paper also suggests different ways to overcome numerical difficulties while using the proposed formulae for gain computation.

Additive Manufacturing (AM) has revolutionized manufacturing with its ability to manufacture complex shapes in a layer-by-layer manner. Materials used for AM include metal, polymer, ceramics, composites, etc. Parts produced using metal AM suffer from large thermal gradients resulting in residual stresses, distortions, and defects. This paper focuses on minimizing the thermal gradients through the optimal selection of scan pattern. Currently, scan pattern is selected based on heuristics, whereas this paper proposes an approach based on the optimization of a temperature uniformity metric using a thermal model based on conduction and convection. The proposed method uses a physics-based model and optimization. The layer to be scanned is discretized using the finite difference method. The approach starts with the selection of the best node that minimizes the temperature uniformity metric. In the next step, the best neighbouring node is selected to minimize the temperature uniformity metric in a greedy sense. If there are no neighbouring nodes, in a procedure similar to the first step, the best-unscanned node is selected, and the greedy approach is continued. This process continues till the entire layer is scanned. Simulations show a 3.54 times improvement in temperature uniformity using the proposed approach as compared to approaches used in the literature.

Formal verification uses mathematically rigorous techniques to establish the correctness of an algorithm or model. While traditional testing shows the presence of defects, it cannot guarantee the absence of defects in a design. Formal verification, on the other hand, can guarantee the absence of defects concerning a set of desirable properties, or provide counter-examples where the properties do not hold. Despite its value, it is not commonly used due to various reasons. This paper discusses two major reasons and proposes solutions for them. The first reason is the difficulty in deriving the proof obligations, the properties to be proved, from the textual requirements. The second hindrance is the additional effort in developing the infrastructure for formal verification. The paper proposes a Natural Language Processing (NLP) based approach to automatically suggest the proof obligations from the textual requirements to remove the first hindrance. They are expressed in propositional, Linear-time Temporal Logic (LTL), and a few customized expressions. The paper also provides methods for converting these obligations into verification subsystems which enable model checking, a method of formal verification to be invoked on the design model, thereby alleviating the second hindrance. The approach and methods are explained in the context of a flight control system's fault handling and safety requirements.

Behavior of Radio Frequency (RF) cavity as a dynamical system has been well explained by its state space model especially in case of normal conducting cavity. In case of superconducting cavity, one needs to also model Time Varying Detuning (TVD) of cavity by a additional non-linear equations. The TVD forms one of parameter of system matrix of cavity state space model and is function of number of mechanical modes of cavities. Often one misses out or fails to model one or more of these modes. Given, that system designer has modeled system to maximum possible extent, there is a need to detect if any dynamics are unmodeled. Kalman filter (KF) and its many variants have been at forefront as estimators for states and parameters of system governed by dynamical equation in presence of uncertainty. It estimates states of system by running in parallel as a system with model similar to that of plant and with its inputs and outputs fetched from plant/system. Under such a situation, KF, while its operation, outputs an innovation sequence. If all dynamics of process are well captured by KF model, the innovation sequence is white noise and zero mean. Any model mismatch due to unmodeled or wrongly modeled dynamics are indicated by innovation sequence. In this work, authors attempt to detect such dynamics using superconducting RF cavity model as a plant in a case study. This paper must be viewed as a novel usage of KF. The proposed method may find potential use in field of dynamics detection or system identification. This paper describes RF cavity model and a discrete KF followed by a mechanism to detect unmodelled dynamics by modeling and simulations studies.

This paper presents a novel design based on disturbance estimation and rejection for robust balance and walking control of humanoid robot. A state-feedback control (SFC) law is designed for trajectory tracking of the center of mass (CoM) of a 3-D linear inverted pendulum model (3D-LIPM) of humanoid robot. To achieve robustness i.e. to cancle the effects of disturbance, the control law is augmented with the Uncertainty and Disturbance Estimator (UDE) based estimate of the disturbance. Next, stability of the close loop system is analysed. The effectiveness of the UDE augmented state feedback controller in estimating the disturbances as well as tracking is validated through the simulations.

Since the past few years, the commercial unmanned aerial vehicle (UAV) sector has grown significantly, thus making easier access of UAVs to public. Because these gadgets have the potential to inflict major risks intentionally or accidentally, this development has quickly aroused security concerns. Since then, many researchers have been working to develop a counter-drone system using different options such as acoustics, RF analyzers, optics, etc. This paper presents an idea on developing and implementing detection model of uncooperative quadcopter drones and track them simultaneously by using a 2-degree-of-freedom robotic manipulator with an onboard camera. First, the detection model is discussed which is developed by training a modified You look only once (YOLO v2) object detector with a large dataset of labeled images of quadcopter. Further, the target's depth is estimated via the stereo camera integrated with RGB. Tracking of target drone is done by controlling the manipulator in Pulse Width Modulation (PWM) mode in which signal is sent as soon as the target is detected.

Sustainable development goals and industry 4.0 push for a holistic plan of action for smart water infrastructure enabling advance digital technologies such as Digital Twins for water networks through an integrated use of machine and physical counterparts. This paper proposes a Digital Twin framework for leakage detection applications in large scale water distribution systems. The framework elucidates digital map generation of the network, hydraulics modelling, calibration and leakage detection model in an integrated manner using python interface. The hydraulic model accounting for spatial and temporal variations of network hydraulics and an optimization formulation for calibration and graph neural networks for leakage identification has been developed. The framework is applied, and results have been demonstrated on a real-life case study of IIT Jodhpur campus water distribution system.

Bounded Sum (BS) was hardly used as an aggregation operator in the modeling of fuzzy controllers. In this work, the utilization of the Bounded Sum (BS) as an aggregation operator has been explored in the modeling of the simplest fuzzy PD controller. Mathematical models of two simplest fuzzy PD controllers utilizing Mamdani Minimum (MM) inference and Center of Area (CoA) defuzzification have been derived in this paper. In addition to modeling, the properties of newly developed controllers are examined. A simulation study is carried out on a single link manipulator system to show the effectiveness of the proposed controllers over the linear PD controller. An improvement in numerical accuracy of results is clearly shown. These newly designed fuzzy PD controllers have the potential to be beneficial for a wide range of control applications where most traditional control algorithms might not yield satisfactory results.

This paper presents two different mathematical models (also called analytical structures) of the simplest Mamdani type Interval Type-2 (IT2) Fuzzy Proportional Integral Derivative (FPID) controller by considering Average and Nie-Tan (NT) defuzzification methods. IT2 Fuzzy Sets (FSs) with triangular Membership Functions (MFs) on both input and output variables, Mamdani Minimum (MM) inference, and One-Dimensional (1D) input space are used to obtain the analytical structures of the controller. The models of the proposed controller are thoroughly analysed for their properties. Validity and applicability of the proposed controller models are demonstrated through simulation study

Precision medicine and personalized treatment approaches are imperative for effectively managing neurological disorders such as Epilepsy, Parkinson's disease, and Mental Depression. Deep brain stimulation is one of the important treatment techniques to suppress epilepsy for drug-resistant epilepsy. Advances in computational modeling and control schemes have received more attention for the development of patient-specific treatment of these diseases. For the effective treatment of these diseases, the design of smart deep brain stimulators (SDBS) with patient-specific models based on individual pathological conditions becomes essential. This paper focuses on developing learning-based control schemes for SDBS to suppress epilepsy using the Hodgkin–Huxley (HH) conductance-based single-neuron model of the brain and real-time validation of the control algorithms using Controller Hardware-in-the-Loop (CHIL). Firstly, patient-specific linear models are developed using a single-neuron cellular model. Secondly Reinforcement Learning (RL) based control schemes namely RL, RL-based Proportional Integral (RL-PI) controller, and RL-based Model Predictive Controller (RL-MPC) are developed for SDBS using Deep Deterministic Policy Gradient (DDPG) algorithm for epilepsy suppression. Finally, the developed control schemes are validated using CHIL with OP4200 real-time simulator and Arduino Mega 2560. The results show that RL-MPC outperforms other controllers and suppresses epilepsy effectively by providing optimal stimulation based on individualized pathological conditions.

Distributed model predictive control (DMPC) has emerged as a powerful approach for large scale integrated systems due to its ability to provide a balance between performance, computational time, and real-world implementability. The decomposition of the overall control problem into the distributed subsystems is an important aspect of the design of a DMPC system. This paper aims at establishing a connection between structural properties of the distributed architecture with the corresponding closed-loop performance. Specifically, the goodness of decomposition is quantified using a well-known concept of modularity from graph theory. For an octuple tank system, four different distributed control structures varying in the number of subsystems and decoupling objectives are proposed. The closed-loop performance of these controllers are compared and subsequently correlated with the corresponding modularity of the structure. Thus the proposed work provides a tool to screen candidate distributed architectures.

Soft exosuits are wearable devices that can provide assistance to individuals with impaired arm movement due to injuries or disabilities. The control of cable-driven soft exosuits has several challenges due to the complex nature of the system interaction and inherent nonlinearities. In this paper, we consider the problem of robust controller design for a soft exosuit considering actuator nonlinearities. This presented work discusses the design of a two-loop sliding mode controller for upper limb exosuits to compensate the effect of nonlinearities in the actuator, i.e., the DC motor and the cable-driven actuation unit. Our aim is to achieve the desired trajectory tracking in the presence of friction in the DC motor and backlash hysteresis in the Bowden cable, for which a two-loop control scheme is introduced. The outer loop generates a desired trajectory by compensating for the backlash in the Bowden cable. The inner loop controller is a robust position-tracking control that tackles the friction in the DC motor. Using Lyapunov stability analysis, we guarantee the convergence of tracking error to zero in the presence of disturbance. Simulation results show that the proposed control law can guarantee accurate tracking performance in the presence of unknown non-linearities.

A hyper-chaotic system with an improved degree of hyper-chaos is preferable for chaos-based encryption. In this paper, Lorenz's hyper-chaotic system is chosen to maximize the degree of hyper-chaos. To perform the maximization process, a competitive swarm optimization technique is used. Four parameters and four initial conditions of Lorenz's hyper-chaotic system are considered the control variables of the optimization process. As a result, the maximum value of the degree of hyper-chaos is obtained as 0.02245. This reported maximum value is 33.63% higher than the existing value (0.0168) in the current literature. The impact of the higher degree of hyper-chaos on the enhanced security of the encryption process is tested. Substantial simulations and analyses fnd that the optimized Lorenz hyper-chaotic system with a higher degree of hyper-chaos provides better security than the original Lorenz hyper-chaotic system.

This paper addresses the challenge of swinging up a 2-DOF rotary inverted pendulum while controlling both, the position and speed of the horizontal link, at the precise time of controller switching. A nonlinear controller based on an appropriately chosen Lyapunov function is proposed for the purpose of achieving the desired states of the system. Once the pendulum is in the upright position, the system transitions to a linear controller, utilizing full state feedback, to stabilize it at its final desired position. The efficacy of the controller is tested experimentally on a setup built in-house and the results are found to be satisfactory.

This paper presents the adaptive terminal sliding mode control (ATSMC) for robotic manipulators using input quantization in the presence of uncertainties and external disturbance. The concept of adaptive quantized control for robot manipulator systems is explored in this study, which involves the introduction of a novel approach for creating input quantization with hysteretic quantizer. Initially, the approach involves suggesting adaptive terminal sliding mode, followed by the application of a hysteretic quantizer incorporating quantization error bounded within specific sectors for controlling the input signal. The proposed adaptive quantized control technique eliminates the chattering and provides convergence to origin in finite-time. The proposed control algorithm's effectiveness is shown through simulation results, illustrating its successful application in controlling a two-link robotic manipulator.

Haptic feedback enables humans to perceive physical sensations through a variety of haptic qualities. Within the realm of computer haptics technology, the pivotal component is haptic rendering, which involves computing forces/torques and/or tactile sensations to provide a more realistic immersive experience. This paper attempts to develop a virtual reality-based application for rendering interactions between tools and fluids, using an affordable commercial haptic device and an open-source software framework. Our aim is to create efficient models and assess their suitability for imparting training in medical tasks such as needle or catheter insertion and/or rehabilitation. The study encompasses the evaluation of both rigid and deformable virtual models to gauge their quality in providing a realistic haptic feedback, discernible variations in perceived stiffness, and their efficacy in enhancing motor skills. The responses from participants indicate that deformable models exhibit significant potential, with performance metrics such as quality, effectiveness, and distinctiveness, scoring 7.93 ± 1.32, 8.12 ± 0.91, and 8.2 ± 0.95, respectively, on a scale of 0 to 10. These results underscore the impact of virtual haptic training on motor skill development and highlight the enhanced proficiency in trajectory recollection.

This paper compares backstepping controller and fast terminal super twisting sliding mode controller (FTSTSMC) approaches for position and altitude control of quadrotor in the presence of constrained disturbances and uncertainties. As conventional SMC which ensures only asymptotic convergence, a new sliding surface is made that allows the tracking error to quickly go to zero in a set amount of time. For achieving above condition based on the super-twisting algorithm, a control law is made that both removes the chattering and handles the quick convergence. The stability in terms of convergence to zero of the entire closed loop system in finite time is examined using the mathematical formulas and the Lyapunov stability theory. Using MATLAB simulation, the proposed controller's robustness and effectiveness are compared against backstepping control technique.

In this paper (with its IP protected contents) we revisit the current literature's Transformation Compliant (TC) methods such as the Routh-Hurwitz Criterion, Cayley Hamilton Theorem and the Lyapunov Matrix Equation method (which are all equivalent to each other) and demonstrate that eigenvalues, which are used as the sole measures of stability (real state variable convergence in Linear Time Invariant State Space (LTISS) systems) are highly unsuitable for assessing the All Real State Variable Convergence, i.e. Convex Stability, of LTISS systems since they are sign pattern blind. For this purpose, we borrow the Qualitative Sign Stability (QLSS) concepts being used in the fields of ecology and economics and by juxtaposing the TC methods with QLSS philosophy, prove that eigenvalue based Hurwitz stability criterion is erroneous and leads to misleading conclusions about the actual ARSVC (Convex stability) property of any LTISS system. An illustrative counterexample to the Cayley-Hamilton Theorem's statement is presented to prove this important observation. Further research results on the TA approach are explained and documented in the RES developed TA Approach Control Systems Design Toolbox in the website of RES.

The efficient operation of modern Electric Vehicles (EVs) is crucial for ensuring safety, energy efficiency, and a wide speed range. Conventional controllers, such as Field-Oriented Control (FOC) and Direct Torque Control (DTC), have limitations in terms of operational bandwidth and efficiency, especially at low-speed and low-torque operations of micro EVs. This paper presents the development and implementation of a Robust and Fast Linear Model Predictive Control (LMPC) for controlling the Brushless Direct Current (BLDC) motors in micro EVs. The computational complexity of the state-of-the-art LMPC algorithms is systematically analyzed, leading to the design of a fast and robust LMPC algorithm. The LMPC is then tailored and customized for implementation on a Renesas ARM Cortex-M automotive-grade microcontroller, ensuring the successful implementation with the desired sample time. The performance of the proposed LMPC is evaluated through experimental tests conducted on a 3kW/48V BLDC motor. The results demonstrate that the proposed LMPC offers robustness, efficiency, and an exceptional disturbance rejection capabilities as well as outperforms the conventional controllers in some cases.

While operation of radio frequency (RF) cavity in a particle accelerator, there is need to stabilize electric field (E) within RF cavity by control system. In pulse mode of RF cavity operation, a particle beam injected into it perturbs amplitude and phase of E, commonly termed as beam loading disturbance problem. For this disturbance compensation, Iterative Learning Controller (ILC) algorithm using proportional integral derivative (PID) type formulation have been reported for Tesla cavity. However, better ILCs based on optimization of errors and control inputs as used in Norm Optimal ILC (NOILC) have not been explored much for beam loading disturbance compensation. This paper elaborates on RF cavity model, Fast NOILC (FNOILC) algorithm and its application to beam loading disturbance problem. Modeling and simulations are explained to highlight strengths of FNOILC. More importantly for best performance, FNOILC needs tuning of weight parameters. A method to tune FNOILC is a novel contribution of this work.

Since the capacitor current can reflect load variations faster than the peak inductor current, incorporating the capacitor current into control loops helps to improve the transient response of the dc-dc converter. In this paper, a coupled inductor single input dual output (CI-SIDO) buck converter is investigated under capacitor current ripple (CCR) control. A precise small-signal model for a CCR-controlled CI-SIDO buck converter operating in continuous conduction mode (CCM) is developed. The accurate small-signal model is obtained by substituting the derived CCR controller expressions in the CI-SIDO buck converter state-space model. The CCR controller equations specify the duty ratios as functions of the circuit variables namely the capacitor currents and the output voltages. It is observed that the CI-SIDO buck converter with the CCR controller exhibits instability when both or either of the duty ratios are greater than 0.7 or their sum is greater than 1. The results of the PLECS STANDALONE simulation validate the theoretical propositions as regards the switching instability of the controlled converter.

The LLC resonant converter is commonly used in onboard charger applications because of its high efficiency and simple circuitry. However, the traditional LLC resonant converter with variable frequency control, possesses a wide switching frequency range between constant current (CC) and constant voltage (CV) charging mode and it also lacks zero voltage switching (ZVS) in constant current charging mode. As an alternative solution to this problem, a unique capacitor inductor capacitor inductor resonant converter (CLCL-RC) with built-in constant current (CC) and constant voltage (CV) output characteristics is provided. This converter uses the inherent properties of the CLCL resonant converter as a current source and a voltage source to achieve CC and CV charging. The paper also presents a design technique for the CLCL resonant converter to achieve the desired output voltage range in both CC and CV modes. The proposed converter can achieve ZVS for all primary-side inverter switches in both CC and CV charging modes, resulting in high efficiency. A 3.36-kilowatt model, operating with a 250-volt input and producing an output ranging from 126.3 to 168 volts, was designed and simulated. The simulated outcomes confirm the effectiveness of the proposed converter for CC-CV charging, particularly in the ZVS operating region.

Vision based runway identification using ‘marked or unmarked terrain’ image sequences captured from a fixed wing unmanned aerial vehicle through onboard stereovision sensor is presented in this paper. An innovative convolutional neural network (CNN) based YOLO-V8 object detection algorithm is used to detect the runway during approach segment of UAV. This deep learning algorithm detects the region of interest in real time and in a computationally efficient manner. The captured unknown road segment or runway image frames are processed and examined for width, length, level and smoothness aspects to qualify as a suitable runway for UAV landings. Also, it is ensured that there are no obstacles, patches or holes on the detected road or runway. Runway start and end threshold lines and regions, touchdown point and runway edge lines are considered as the region of interest. Image processing algorithms are applied on the captured runway or road images to detect strong features in the region of interest. Feature detector based image processing algorithm with stereo vision constraint is used to establish the relation between unmanned aerial vehicle's center of gravity and detected runway feature points. Image processing algorithms like hough line detection, RANSAC, Oriented FAST and Rotated BRIEF (ORB), median filters, morphological methods are applied to extract terrain features. Based on the detected runway orientation and position with respect to UAV position. An automatic landing manoeuvre is performed by UAV autopilot to land the UAV on intended touchdown point on runway computed through detected feature points.