Presence Of Inputs Research Articles

Autonomous Lunar rendezvous trajectory planning and control using nonlinear MPC and Pontryagin’s principle

This paper explores the application of Nonlinear Model Predictive Control (NMPC) techniques, based on the Pontryagin Minimum Principle, for a minimum-propellant autonomous rendezvous maneuver in non-Keplerian Lunar orbits. The relative motion between the chaser and the target is described by the nonlinear dynamics of the circular restricted three body-problem, posing unique challenges due to the complex and unstable dynamics of near-rectilinear halo orbits. Key aspects of the proposed NMPC include trajectory optimization, maneuver planning, and real-time control, leveraging on its ability to satisfy complex mission requirements while ensuring safe and efficient spacecraft operations and in the presence of input and nonlinear/non-convex state constraints. The proposed formulation allows the design of a minimum-propellant controller, whose optimal control signal results to be bang–bang in time. A case study based on the Artemis III mission – where the docking of the Orion spacecraft to the Gateway station is planned – is illustrated in order to demonstrate the efficiency of the proposed approach, showcasing its potential for enhancing target tracking accuracy, while reducing propellant consumption.

Inner loop model predictive control and outer loop PI reference governor for PMSMs with input and state saturation for torque control

This contribution considers a torque control scheme consisting of model predictive control (MPC) in the inner control loop together with PI reference governor in the outer control loop and a decoupling feedforward control for an isotropic permanent magnet synchronous machine (PMSM). This innovative approach is known in literature as PI-MPC dual loop control. A particular emphasis is given to the control governor strategy which is the outer loop PI reference governor and allows to regulate the machine in the flux weakening region and is therefore only active for field weakening. In this context the analysis of the stability based on Lyapunov’ approach of the control loop in flux weakening region is shown. The desired currents represent the reference currents for the MPC, which forms the inner control loop. The MPC is adapted using an extended Kalman filter (EKF), which estimates inductance of the electrical system in dq coordinates by using a bivariate polynomial. Compared measurements with a hardware-in-the-loop (HIL) system show the effectiveness of the proposed control scheme with respect to a standard PI controller in inner loop (PI-PI scheme) in the presence of saturated inputs and state of a PMSM. The proposed MPC uses just an optimal, proportional control and thus avoids windup effects. Measurement results in the presence of input and state saturations show that MPC is working without overshoot in the currents which leads to less needed power in input.

Observer-Based Adaptive Fuzzy Quantized Fault-Tolerant Control of Nonstrict-Feedback Nonlinear Systems With Sensor Fault

This paper focuses on the problem of adaptive quantized fault tolerant tracking control for a class of uncertain nonstrict-feedback nonlinear systems in the presence of input and output quantization and sensor fault. The main technical challenges come from how to deal with the input and output quantization errors and the partial loss of sensors' effectiveness. Firstly, a new observer including the quantized input and output signals and the estimation of fault parameter is designed to reconstruct the unavailable states, and some special techniques are developed to deal with the sector-bounded quantization errors. Furthermore, based on the cubic absolute-value Lyapunov function, the adaptive fault compensation strategy is presented to make up for the influence from sensor fault upon the control performance. Then, with the aid of fuzzy logic system (FLS) approximation method and backstepping technique, a novel output feedback adaptive fuzzy fault tolerant control (FTC) scheme is proposed for this class of systems, such that all signals in the closed-loop are semi-globally uniformly ultimately bounded (SGUUB) while the tracking error remains in a small vicinity of the origin. Finally, the effectiveness and applicability of the proposed control scheme are validated by conducting two simulation examples.

Finite‐time robust performance improvement for non‐linear systems in the presence of input non‐linearity and external disturbance

AbstractThe main focus of this research is on improving the performance of dynamic systems with actuator non‐linearities and time‐varying disturbances. To this end, using the concept of finite‐time stability, a novel observer is presented to estimate the disturbances in systems with input non‐linearities. Then, introducing an innovative sliding manifold, a robust observer‐based technique is derived to guarantee the finite‐time stability of the sliding surface. Hence, the closed‐loop system would be stable in the presence of hard non‐linearities and time‐varying disturbances. The performance of the proposed approach was assessed in using a simulation study of two numerical examples. The obtained results and comparison with a conventional method confirmed the benefits of the suggested approach.

Control for plasma glucose regulation in type 1 diabetes mellitus patients with unknown input delays

This paper presents a control strategy for plasma glucose regulation in the presence of input with unknown time delay (constant or variable) and meal disturbances in the system. The control scheme proposed is an observer-based state feedback controller. The observer achieves to estimate the state vector even if the control input in the system presents an unknown time delay in the input. The main advantage of the controller is that the gains are selected so the control law will provide positive values that ensure an implementable case. The observer-based control scheme is validated considering different disturbance and unknown delays scenarios, in both cases, glucose regulation is achieved, rejecting meal disturbances in 4 hours and avoiding hypoglycemic episodes.

State estimation using a network of distributed observers with unknown inputs

State estimation for a class of linear time-invariant systems with distributed output measurements (distributed sensors) and unknown inputs is addressed in this paper. The objective is to design a network of observers such that the state vector of the entire system can be estimated, while each observer has access to only local output measurements that may not be sufficient on their own to reconstruct the entire system’s state. Existing results in the literature on distributed state estimation assume either that the system does not have inputs, or that all the system’s inputs are globally known to all the observers. Accordingly, we address this gap by proposing a distributed observer capable of estimating the overall system’s state in the presence of inputs, while each observer only has limited local information on inputs and outputs. We provide a design method that guarantees convergence of the estimation errors to zero under joint detectability conditions. This design suits undirected communication graphs that may have switching topologies and also applies to strongly connected directed graphs. We also give existence conditions that are consistent with existing results on unknown input observers. Finally, simulation results verify the effectiveness of the proposed estimation scheme for various scenarios.

State observer-based model reference adaptive balance control for one-leg stance

PurposeHumanoid robots have been utilized in many fields such as medical, construction, and disaster response. While humanoid robots nowadays can achieve great capabilities, the one-leg balancing task still poses a challenging problem. This paper aims to propose a novel approach to solve the problem.Design/methodology/approachTo aid the balance of one leg in humanoid robot, an external balance mechanism is inserted to the back of the humanoid robot. First, a dynamic model of the humanoid robot with balance mechanism and its simplified model are introduced. Second, a backstepping-based control method is utilized to build the proposed controller for one-leg stance system through two steps. For the first step, a minimum observer-based controller with a virtual control input is used to control the first sub-system reaching the desired reference input. For the second step, a virtual control input is considered as a reference input of a second sub-system, then a model reference adaptive controller (MRAC) is employed to control the second sub-system reaching the virtual control input in presence of uncertainties. By using the external balance mechanism, the sideway balancing task is separated from normal walking function. Furthermore, the utilization of the balance mechanism ensures the humanoid robot's hip adduction does not exceed the threshold of a human when walking. Finally, a simulation study is carried out to evaluate the effectiveness of the proposed method.FindingsThis paper proposes a model reference adaptive control using state observer for balancing one leg of humanoid robot in stance phase that extends our previous research (Tran et al., 2021).Originality/valueThe main research contents have been introduced.

Enhancing deep learning algorithm accuracy and stability using multicriteria optimization: an application to distributed learning with MNIST digits

The training phase is the most crucial stage during the machine learning process. In the case of labeled data and supervised learning, machine learning entails minimizing the loss function under various constraints. We provide an innovative model for learning with numerous data sets, resulting from the application of multicriteria optimization techniques to existing deep learning algorithms. Data fitting is formulated as a multicriteria model in which each criterion measures the data fitting error on a specific data set. This is an optimization model involving a vector-valued function, and it has to be analyzed using the notion of Pareto efficiency. We present stability results for efficient solutions in the presence of input and output data perturbations. The multiple data set environment comes into play to eliminate the bias caused by the selection of a specific training set. To apply this concept, we present a scalarization strategy as well as numerical experiments in digit classification using MNIST data.

Consensus Stability of a Large-Scale Robotic Network Under Input and Transmission Delays

A large scale network of differential-wheeled robots seeking consensus is considered in the presence of input and transmission delays. Consensus stability of this nonlinear network dynamics holds if its linear part can be shown to achieve consensus stability despite the delays. We present theoretical and computational results on this linear large scale system. From a theoretical point of view, conditions on the amount of input delays are revealed such that stability is guaranteed independent of any transmission delay, and in the case this is not possible, a computationally-efficient algorithm is proposed to reveal the largest transmission delay that the network can accommodate without losing stability.

Numerical study of the single-phase environment flow in turns in the presence of input and output sections

Numerical study of the single-phase environment flow in turns in the presence of input and output sections

Assessing the role of initial conditions in the local structural identifiability of large dynamic models

Structural identifiability is a binary property that determines whether or not unique parameter values can, in principle, be estimated from error-free input–output data. The many papers that have been written on this topic collectively stress the importance of this a priori analysis in the model development process. The story however, often ends with a structurally unidentifiable model. This may leave a model developer with no plan of action on how to address this potential issue. We continue this model exploration journey by identifying one of the possible sources of a model’s unidentifiability: problematic initial conditions. It is well-known that certain initial values may result in the loss of local structural identifiability. Nevertheless, literature on this topic has been limited to the analysis of small toy models. Here, we present a systematic approach to detect problematic initial conditions of real-world systems biology models, that are usually not small. A model’s identifiability can often be reinstated by changing the value of such problematic initial conditions. This provides modellers an option to resolve the “unidentifiable model” problem. Additionally, a good understanding of which initial values should rather be avoided can be very useful during experimental design. We show how our approach works in practice by applying it to five models. First, two small benchmark models are studied to get the reader acquainted with the method. The first one shows the effect of a zero-valued problematic initial condition. The second one illustrates that the approach also yields correct results in the presence of input signals and that problematic initial conditions need not be zero-values. For the remaining three examples, we set out to identify key initial values which may result in the structural unidentifiability. The third and fourth examples involve a systems biology Epo receptor model and a JAK/STAT model, respectively. In the final Pharmacokinetics model, of which its global structural identifiability has only recently been confirmed, we indicate that there are still sets of initial values for which this property does not hold.

Investment, insurance and weather shocks: Evidence from Cambodia

The livelihoods of poor people in developing countries are increasingly dependent on weather shocks whose effects are exacerbated by the lack of access to adequate insurance markets allowing risk hedging. Index-based insurance underwrites a weather risk as a proxy for economic loss: when the index falls below a certain level, farmers automatically get a payment. The aim of this paper is to study the impact of an Index-based insurance on investment decisions in profitable but risky inputs in presence of weather shocks by means of an incentivized lab-in-the-field experiment conducted in Cambodia. The protocol is designed so as to study the extent to which investment decisions change under risk or ambiguity, for different levels of initial wealth, under contract nonperformance (i.e., when claims are not repaid by the insurer) and when the insurance is fully subsidized. The findings indicate that, while the mere presence of a market for insurance increases investment, the strength of the effect crucially depends upon the level of initial wealth and upon the subjects' ability to correctly assess the probability of a shock.

Suboptimal sliding manifold For nonlinear supply chain with time delay

In this paper, a basic dynamic model of the supply chain system is constructed in which the oscillation problems caused by the time delay in remanufacturing, ordering, the disturbance of the system parameters are considered along with the customers’ demand estimation. Time delay in production systems affects the efficiency of supply chain and lead to the bullwhip effect. This paper proposes a new method according to the model structure for controlling the bullwhip effect based on state dependent Riccati equation (ESDRE) and designing suboptimal sliding manifolds for a nonlinear supply chain in the presence of input and state delays. A switching control scheme is obtained based on the designed suboptimal sliding manifold. It is proved that this control scheme can guarantee that the nonlinear supply chain system is asymptotically stable and understand soft switching among subsystems of the nonlinear supply chain to mitigate fluctuations in the system variables. The efficiency of suboptimal sliding manifold method was examined by comparing LQR method in the presence of various time delays. Also, simulation investigation in real supply chain proved this efficiency.

Measuring congestion in sustainable supply chain based on data envelopment analysis

Sustainable Supply Chain Management (SSCM) involves the integrating of environmental, social and economic concerns into supply chain management activities with emphasis on the managers' efforts in the context of reducing the negative social and environmental impacts. Evaluating sustainable supply chain performance and efficiency is a significant topic for many researchers and scholars. Presence of input and intermediate product congestion is one of the key issues that results in lower efficiency and performance in a sustainable supply chain. Therefore, determination of congestion is of prime importance and removing it improves performance of the sustainable supply chain. One of the most appropriate methods for detecting congestion is Data Envelopment Analysis (DEA). Some studies have been conducted to detect the intermediate product congestion via solving Network DEA (NDEA) models without considering the role of intermediate products. In this study, a sustainable supply chain with two-stage structure was considered. Then, the congestion status according to the role of intermediate products was found out for the first time. Towards this aim, different scenarios which congestion can occur in intermediate products were identified. Then, in each scenario, the dominant cone definition was developed in network structure and NDEA models were proposed. Finally 20 Iranian sustainable supply chains of Resin manufacturing companies have been used to demonstrate applicability of the proposed models.

Long-Term Electricity Demand Forecasting for Malaysia Using Artificial Neural Networks in the Presence of Input and Model Uncertainties

Electricity demand is also known as load in electric power system. This article presents a Long-Term Load Forecasting (LTLF) approach for Malaysia. An Artificial Neural Network (ANN) of 5-layer Multi-Layered Perceptron (MLP) ... | Find, read and cite all the research you need on Tech Science Press

Computation of Discrete Medial Axis Using Local Search in Domain Delaunay Triangulation of a Solid

AbstractA new method is proposed to determine the points on the medial axis transform (MAT) of an object from its surface mesh representation. Current art typically uses a Voronoi diagram-based approach to generate the medial axis of a given point cloud on the boundary of the object or a surface mesh representation as input. This approach defines the MAT points as a subset of the Voronoi vertices close to the medial axis, where the accuracy and density of the points on the medial axis depend on the sampling density of the input point cloud representation. Therefore, the set of medial axis points is incomplete and may lack various topological features of the MAT and its reconstruction property. Instead of filtering the Voronoi vertices that are not medial points, the method proposed in this paper searches for the correct MAT point in the vicinity of such Voronoi vertices and finds the pair of corresponding footpoints using the properties of the MAT point. Hence, the algorithm can determine points on the medial axis without being dependent on the given sampling density and even in the presence of inputs having non-manifold entities. As the MAT points are generated based on the definition of medial axis (MA), the result obtained is accurate to within a specified tolerance.

Observer-based adaptive consensus control for nonlinear multi-agent systems with time-delay

In this paper, we consider the observer-based adaptive consensus trackingproblem for a class of nonlinear time-delay multi-agent systems in the presence ofinput saturation. Under the assumption that the communication topology is directedand connected, a distributed adaptive consensus controller is developed based on thedynamic surface control technique. By constructing the nonlinear observer, theunmeasurable agents dynamics can be estimated. Input saturation problem issolved by a smooth function combined with an auxiliary variable. With the helpof prescribed performance functions, the synchronization errors converge tothe prescribed sets, which are characterized as a neighborhood of zero.According to Lyapunov stability theory, it is shown that with the proposeddistributed consensus tracking approach, the consensus errors are cooperativelysemi-globally uniformly ultimately bounded. Finally, a simulation example is provided to show theeffectiveness of the proposed algorithm.

Efficient Calibration of Embedded MPC

Model Predictive Control (MPC) is a powerful and flexible design tool of high-performance controllers for physical systems in the presence of input and output constraints. A challenge for the practitioner applying MPC is the need of tuning a large number of parameters such as prediction and control horizons, weight matrices of the MPC cost function, and observer gains, according to different trade-offs. The MPC design task is even more involved when the control law has to be deployed to an embedded hardware unit endowed with limited computational resources. In this case, real-time implementation requirements limit the complexity of the applicable MPC configuration, giving rise to additional design tradeoffs and requiring to tune further parameters, such as the sampling time and the tolerances of the on-line numerical solver. To take into account closed-loop performance and real-time requirements, in this paper we tackle the embedded MPC design problem using a global, data-driven optimization approach. We showcase the potential of this approach by tuning an MPC controller on two hardware platforms characterized by largely different computational capabilities.

Feedback Control in the Presence of Input and Output Disturbances

A novel control law is proposed to attenuate the influence of input and output disturbances for systems with vector output and sector bounded nonlinearities. The control law is based on estimation of the disturbance in the output. Differently from the existing results, the ultimate bound of the closed-loop system depends on only one component of the output disturbance vector (as well as, on the input disturbance). The results are formulated in terms of LMIs. The efficiency and advantages of the results over the existing methods are demonstrated by numerical examples.

ЕЛЕМЕНТИ МАРКЕТИНГОВОГО МЕХАНІЗМУ

UDC 339.138; JEL Classification: М380Purpose. The aim of the article is to identify the key elements of the marketing mechanism by summarizing scientific approaches to the analysis of the structure of the marketing mechanism based on the content analysis of scientific works. Methodology of research. The content analysis method was used to systematize the existing approaches in the scientific literature to determine the key elements of the marketing mechanism as a system. The comparison method was used to identify similar and different views of researchers on determining the characteristic elements of the marketing mechanism. The generalization method is used to substantiate the results of the study. Methods of analysis and synthesis are applied to determine the individual structural marketing mechanism and consider them in unity. Findings. In the course of the study, the results were obtained: it was determined that the marketing mechanism can be used as an effective tool for solving pressing problems of society; proved the possibility of considering the marketing mechanism as an open system with the presence of input, output and internal structure; elements of the marketing mechanism are identified; it is proved that the marketing nature of the mechanism is determined by the tools used by marketing entities, the presence and nature of the target result; the expediency of consciously creating an effective marketing mechanism for managing socially significant areas in order to prevent the occurrence of spontaneous mechanisms is substantiated. Originality. For the first time, an approach has been substantiated to determine the key elements of the marketing mechanism, which, unlike existing approaches, provides for determining the nature of the target result, which allows us to outline the expediency of the functioning of the marketing mechanism in various fields, the feasibility of transforming the object of influence and the use of appropriate levers by marketing entities. Practical value. The definition of the elements of the marketing mechanism forms the scientific basis for understanding the principles and patterns of applying the marketing mechanism to solve a wide range of problems of society, enshrined in the principles of sustainable development, application in order to increase the efficiency of marketing activities of enterprises and use in the social sphere.