Behavior Of Control System Research Articles

Effect of Salesforce Control System and Supervisory Behaviour on Job Satisfaction of Salespeople:A Review of Studies

A salesforce control system is an organization's process to monitor, direct, evaluate and reward salespeople (Anderson and Oliver, 1987). It has an impact on salesforce job satisfaction, job performance as well as on the results achieved by the organization. Due to its importance in affecting salespeople and organizational variables, sales organizations are making efforts to choose the right kind of salesforce control system (either behaviour based or outcome based or hybrid salesforce control system). Moreover, the other factor that is closely related to salesforce control system and which has important influence on salesforce characteristics like job satisfaction is supervisory behaviour of sales managers. This paper contributes by making an attempt to analyze the effect of salesforce control system as well as sales manager supervisory behaviour on job satisfaction of salespeople.The analysis is based on the review of studies taken from the period 1976 to 2014.Based on the findings of the review, research implications are stated.

Asymptotic behavior of an optimal control system with two small singularly excitatory parameters

The subject of research in the article is dynamic control systems with optimal slow motions. The goal of the work is to obtain an asymptotic approximation of the control in the form of feedback, which, not being uniform in the domain of the system definition, forms slow motions of the system uniformly close to optimal ones. The objectives of the study are to conduct an asymptotic analysis of the controller for small values of the parameters. Applied methods: methods of minimization of quadratic functions and methods of matrix algebra. The obtained results: the problem of the optimal equation with two small singularly exciting parameters is considered. Requirements for the characteristics and controllability of the selected system have been introduced. The problem under consideration, in contrast to well-known studies, is connected with a fundamental problem: as the system parameters tend to zero, certain components of the matrix that satisfies the Ricatti equation, due to the limiting condition for it, acquire singularities in certain time intervals. The practical significance of the work lies in the fact that with the use of minimization methods, general methods are obtained for constructing a uniform region of asymptotics for optimal control systems with two small singular-exciting parameters with respect to another small parameter.

Computer Animation Education Online: A Tool to Teach Control Systems Engineering throughout the COVID-19 Pandemic

The world is changing, and university education must be able to adapt to it. New technologies such as artificial intelligence and robotics are requiring tools such as simulation and process control to develop products and services. Thus, control systems engineering schools are adapting to new educational frameworks tailored to deploy promising and feasible new technologies. Herein, we have relied on computer animation-based education and its implementation as an online project-based strategy to attain the objectives and goals of the control systems engineering courses at University of Los Andes, Venezuela. The ControlAnimation library developed in Mathematica program in 2002 has been used as a tool to teach control systems engineering courses since 2008 and with greater prominence since 2020, when the stay-at-home orders due to the COVID-19 pandemic were enacted. Consequently, computer animation-based education has proven its feasibility as an online tool combined with project-based learning techniques, thus allowing students to interact with an animated control system by changing the mathematical model and the design parameters of control laws in a comfortable and somewhat playful way. This enabled new capabilities to study the dynamic behaviors of primordial control systems online. In addition, it allowed students to co-identify and relate in a more intuitive way to the mathematical models and control equations with the physical behavior of the real control systems.

Switched Fractional Order Model Reference Adaptive Control for First Order Plants: A Simulation-Based Study

Abstract This paper presents the results and analysis of an exhaustive simulation study where switched fractional order model reference adaptive control (SFOMRAC) is used for first order plants, along with the analytical proof of boundedness and convergence of the scheme. The analysis is focused on the controlled system behavior through the integral of the time-weighted squared control error (ITSE) and on the control energy through the integral of the squared control signal (ISI). Controller parameters such as fractional order, adaptive gain, and switching time are varied along the simulation studies, as well as plant parameters and reference models. The results show that SFOMRAC controllers can be found for every plant and reference model used such that both system behavior and control energy can be improved, compared to equivalent nonswitched fractional order and integer order control strategies.

Improvement of the comprehensive automation control system for dynamic processes of hot deformation processing on thermoelastic presses

The researches results of the automated control system improvement for a thermoelastic press in the implementation of the processes of isothermal deformation and metallic materials stamping in a state of superplasticity are considered. It is established that the deviation from the optimal temperature-speed deformation regimes leads to the defect formation in the article body in the form of sinks, clamps, oxide films drawn into the article from the surface of the semi-finished product. These defects are stress concentrators that can lead to the destruction of loaded parts. The main problem that prevents the active industrial use of automated control systems for considered equipment is determined — their inertia. Measures to eliminate the inertia of the automated control system for thermocompression plants are proposed. It is shown that the main and most effective method for solving this problem is the use of digital extropolation algorithms for short-term prediction of the measuring and control system behavior. The improved automated control system has been successfully tested at stamping of finned aircraft panels and shells made from aluminum alloys D16, V95, V96ts, 01420. The system provided control of the temperature and speed parameters for metal materials stamping in the state of superplasticity and under isothermal stamping conditions with a heating accuracy of 2... 4 °С and the accuracy of the speed parameter compliance (deformation rate) up to 0.1 mm/s. Keywords isothermal forging, thermal compression, thermal expansion, defects, automated control system, system inertia

Strategic behavior and use of management control systems in agro-industrial cooperatives

Purpose – This study aims to identify the combinations of MCS used in different strategic behaviors adopted by agro-industrial cooperatives.  Theoretical framework – Miles and Snow's strategic behavior model and Simons' management control systems model (1995).  Design/methodology/approach – A survey was carried out with 100 managers of agribusiness cooperatives in the dairy sector in the southern region of Brazil. Fuzzy set qualitative comparative analysis (fsQCA) was applied to the data collected  Findings – The results partially confirmed the propositions established in the study. Different combinations of belief systems, limits, diagnostic control and interactive control were observed depending on the cooperative's strategic, defender or prospective behaviors. Therefore, the control of the cooperative's strategy is achieved by combining the SCG, since its power lies in the joint use and its complementarity.  Research Practical & Social implications – The combined use of rigid and flexible controls allows agro-industrial cooperatives to simultaneously target the efficiency of operations and the pursuit of innovation.  Originality/value – By demonstrating that not all SCGs individually are relevant to different strategic behaviors, with evidence of equifinality in SCG combinations, the study denotes originality and contribution.  Keywords – Strategic behavior. Management control systems. Levers of control

Swing up and stabilization control of rotary inverted pendulum based on energy balance, fuzzy logic, and LQR controllers

Rotary Inverted Pendulum (RIP) mimics the behavior of many practical control systems like crane mechanism, segway, unicycle robot, traction control in vehicles, rocket stabilization, and launching. RIP is a fourth-order nonlinear open-loop unstable dynamical system and is widely used for testing the effectiveness of the newly developed control algorithms. In this paper, a Hybrid Control Scheme (HCS) based on energy balance and fuzzy logic controllers is proposed to implement the swing up and stabilization control of RIP. In the proposed control scheme, the fuzzy logic-based state feedback gains are dynamically tuned in real-time by minimizing the absolute error between the desired and actual states to get robust control performance. The proposed HCS is also compared with the conventional Linear Quadratic Controller (LQR) for this application. The comparative results show that the proposed fuzzy logic-based hybrid control scheme gives the optimal control performance in terms of achieving satisfactory transient, steady-state, and robust responses from a given RIP system, as compared to the conventional LQR based control scheme. The proposed control scheme is also relatively less complex with a low computational cost and provides desired response characteristics as compared to the existing ones in the literature.

Power System Stabilizer as a Part of a Generator MPC Adaptive Predictive Control System

In this paper, a model predictive controller based on a generator model for prediction purposes is proposed to replace a standard generator controller with a stabilizer of a power system. Such a local controller utilizes an input-output model of the system taking into consideration not only a generator voltage Ug but also an additional, auxiliary signal (e.g., α, Pg, or ωg). This additional piece of information allows for taking oscillations into account that occur in the system and minimizing their impact on the overall system performance. Parameters of models used by the controller are obtained on the basis of the introduced black-box models both for a turbine and a synchronous generator, parameters of which are estimated in an on line fashion using a RLS method. The aim of this paper is to compare the behavior of the classical generator control system with a power system stabilizer and a model predictive control with an additional feedback signal. The novelty of the paper is related to the use of the predictive controller instead of the classical controller/stabilizer system and its possibility of stabilizing the power system. Contrary to the solutions found in the literature, which are commonly-based on a fuzzy logic approach, the authors propose the use of an adaptive model predictive controller, which takes advantage of the knowledge concerning the plant in the form of a model and adapts itself to the operating point of the system using the model parameters estimation mechanism. Moreover, the adaptive predictive controller, unlike other solutions, automatically adjusts signal levels to changes in the plant. The proposed solution is able to calculate the best control signal regardless of whether these changes of the plant are caused by a change in the operating point, or resulting from operation, e.g., wear of mechanical parts.

Provably Safe Artificial General Intelligence via Interactive Proofs

Methods are currently lacking to prove artificial general intelligence (AGI) safety. An AGI ‘hard takeoff’ is possible, in which first generation AGI1 rapidly triggers a succession of more powerful AGIn that differ dramatically in their computational capabilities (AGIn << AGIn+1). No proof exists that AGI will benefit humans or of a sound value-alignment method. Numerous paths toward human extinction or subjugation have been identified. We suggest that probabilistic proof methods are the fundamental paradigm for proving safety and value-alignment between disparately powerful autonomous agents. Interactive proof systems (IPS) describe mathematical communication protocols wherein a Verifier queries a computationally more powerful Prover and reduces the probability of the Prover deceiving the Verifier to any specified low probability (e.g., 2−100). IPS procedures can test AGI behavior control systems that incorporate hard-coded ethics or value-learning methods. Mapping the axioms and transformation rules of a behavior control system to a finite set of prime numbers allows validation of ‘safe’ behavior via IPS number-theoretic methods. Many other representations are needed for proving various AGI properties. Multi-prover IPS, program-checking IPS, and probabilistically checkable proofs further extend the paradigm. In toto, IPS provides a way to reduce AGIn ↔ AGIn+1 interaction hazards to an acceptably low level.

THE EXISTENCE OF TRADITIONAL LAW REVIEWING FROM PANCASILA VALUES IN THE DEVELOPMENT OF NATIONAL LAW

The purpose of this study is to examine the existence of customary law in terms of Pancasila values in the development of national law and what factors influence the existence of customary law in terms of Pancasila values in the development of national law. The research method used is normative juridical. The Indonesian people have succeeded in carrying out legal reforms to the constitution. Legal reform must continue with the formation and renewal of laws replacing Dutch legacy laws. Having its own laws for the Indonesian people can reveal national identity. Legal development simply implies efforts to make improvements from unfavorable conditions to better ones. According to this understanding, development can be meaningful with renewal. Reform is an effort to reorient and reform something that will be pursued through policy. The development of national law is the process of building a legal system and its instruments. The development of national law must be able to replace all Dutch Colonial product laws replaced with their own product laws. Legal development cannot be separated from legal politics, because it is a direction in making and enforcing laws in order to achieve national goals and objectives. Law as mentioned in the Oxford English Dictionary is a collection of rules either as a result of formal legislation or custom, in which a certain state or society claims to be bound as members or as subjects. Law is a behavioral control system (ethical control). The legal form is a norm which is the product of a center of power that has the authority to create and apply the law. Law as a unidirectional control system carried out by a central organ that has power. Unidirectional control implies that control only takes place from a certain organ that is given the capacity and function for it. Unidirectional control is also automatic-mechanical that guides behavior.

Short-circuit modeling of three-phase 4-wire unbalanced networks in presence of single-phase photovoltaic systems

Due to the growing diffusion of single-phase Photovoltaic Systems (PVSs), the analysis of active unbalanced three-phase 4-wire distribution networks in short-circuit operating conditions is nowadays of great interest. However, this analysis is not trivial due to various factors (e.g., environmental conditions, inverter control system, limits of the inverter current, location and type of the fault, PVS self-protection systems) influencing the contribution of PVSs to the short-circuit conditions. Among these factors, the greatest challenge is to model the complex behavior of the inverter control system that must comply with actual and future grid requirements, or with national and international standards, in both normal and abnormal operating conditions.An analytical model based on the phase-coordinates approach is proposed in this paper to evaluate the short-circuit contributions of single-phase PVSs connected to unbalanced three-phase 4-wire LV distribution networks. The model of the PVSs takes into account the main factors that influence the PVS contributions, with particular attention to the model of the control system which injects reactive power to support grid voltage in normal operating conditions and to perform low-voltage ride-through (LVRT) dynamic grid support during the fault.In this paper, control systems for next-generation single-phase PVSs able to satisfy grid code requirements are explored; they include constant average active power control, constant active current control and constant peak current control. Extensive numerical experiments are presented with reference to the Cigrè European LV Distribution Network Benchmark, considering different fault types, effects of various control strategies and the evaluation of the impact of the PVSs in LVRT conditions w.r.t. the passive network, i.e., the network under fault with no installed PVSs.

Neuroevolutionary Control for Autonomous Soaring

The energy efficiency and flight endurance of small unmanned aerial vehicles (SUAVs) can be improved through the implementation of autonomous soaring strategies. Biologically inspired flight techniques such as dynamic and thermal soaring offer significant energy savings through the exploitation of naturally occurring wind phenomena for thrustless flight. Recent interest in the application of artificial intelligence algorithms for autonomous soaring has been motivated by the pursuit of instilling generalized behavior in control systems, centered around the use of neural networks. However, the topology of such networks is usually predetermined, restricting the search space of potential solutions, while often resulting in complex neural networks that can pose implementation challenges for the limited hardware onboard small-scale autonomous vehicles. In exploring a novel method of generating neurocontrollers, this paper presents a neural network-based soaring strategy to extend flight times and advance the potential operational capability of SUAVs. In this study, the Neuroevolution of Augmenting Topologies (NEAT) algorithm is used to train efficient and effective neurocontrollers that can control a simulated aircraft along sustained dynamic and thermal soaring trajectories. The proposed approach evolves interpretable neural networks in a way that preserves simplicity while maximizing performance without requiring extensive training datasets. As a result, the combined trajectory planning and aircraft control strategy is suitable for real-time implementation on SUAV platforms.

Evaluation of ML Algorithms for System Dynamics Identification of Aircraft Pressure Control System

In this work we investigate the behaviour of aircraft air conditioning and internal pressure control system. This system is critical for aircraft operations, because detected faults lead to flight cancellation and maintenance, while in-flight faults can substantially worsen crew or passenger flight conditions. Our goal is to investigate the quality of fault detection and identification as well as computational complexity of novel data-driven algorithms for system identification. The main algorithm studied is Sparse Identification of Nonlinear Dynamics, which identifies nonlinear dynamical systems from data promoting sparsity in the solution. Developing a SINDy-based prototype of the health monitoring system is carried out as follows. We are using simulation data from an industrial ground facility, which is capable of high altitude flight simulation. We generate time series data of system's normal operation and different types of faults, such as wear of the most sensitive elements of the system (air gate, temperature and pressure gauges). Then, using the obtained data we build a Koopman operator approximation for each known mode of operation. During the testing phase prediction error is used as anomaly indicator. It is shown that online model update and its comparison with reference model can help in unknown fault isolation.

Numerical Investigation of Heat Transfer Enhancement Due to Flow Agitators Between Fins

Abstract The paper investigates the heat transfer characteristics of a channel system consisting of mean axial flow and oscillatory cross flow components. A numerical model has been developed to solve the governing equations associated with the flow. The paper identifies advection, diffusion, and oscillation time scales and intensity of squeezing in the channel as critical parameters controlling system behavior. The total Reynolds number parameter is considered in the paper to understand the combined effect of axial and transverse Reynolds numbers on the Nusselt number. Flow visualization techniques are used to understand the boundary layer changes that occur over an oscillation cycle. Nusselt number is found to increase with a reduction in advection and oscillation time scales. A linear relationship is observed between the Nusselt number and total Reynolds number when the axial and transverse Reynolds numbers are comparable. Nondimensional pressure drop is primarily defined by only two parameters: axial Reynolds number and squeezing fraction. The flow visualization results indicate significant heat transfer enhancement in a small fraction of the oscillation cycle characterized by flow conditions similar to Couette flow.

Loop-shaping for reset control systems: A higher-order sinusoidal-input describing functions approach

The ever-growing demands on speed and precision from the precision motion industry have pushed control requirements to reach the limitations of linear control theory. Nonlinear controllers like reset provide a viable alternative since they can be easily integrated into the existing linear controller structure and designed using industry-preferred loop-shaping techniques. However, currently, loop-shaping is achieved using the describing function (DF) and performance analysed using linear control sensitivity functions not applicable for reset control systems, resulting in a significant deviation between expected and practical results. This major bottleneck to the wider adaptation of reset control is overcome in this paper with two important contributions. First, an extension of frequency-domain tools for reset controllers in the form of higher-order sinusoidal-input describing functions (HOSIDFs) is presented, providing greater insight into their behaviour. Second, a novel method that uses the DF and HOSIDFs of the open-loop reset control system for the estimation of the closed-loop sensitivity functions is proposed, establishing for the first time — the relation between open-loop and closed-loop behaviour of reset control systems in the frequency domain. The accuracy of the proposed solution is verified in both simulation and practice on a precision positioning stage and these results are further analysed to obtain insights into the tuning considerations for reset controllers.

Seismic behavior of a two-level control system with double vertical shear links in series

To improve seismic behavior of structures, a two-level control system is proposed in this paper where by combining two vertical shear panels in series in a chevron bracing configuration, Double-Vertical Shear Panel, D-VSP is introduced. Utilizing two-levels of energy absorption for two different earthquake intensity levels, D-VSP is expected to beneficially change dynamic behavior parameters like strength, stiffness and damping ratio through increasing ductility. To validate research, a VSP is modeled in ABAQUS and related numerical results are compared to those of a previous experimental work. Pushover, quasistatic cyclic and seismic analyses are conducted on two models. The hysteresis curves show symmetric two-level behavior with stable strength and stiffness leading to increase ductility ratio up to 29.4%. Maximum displacement and maximum base shear under seismic loading decrease 5.91 and 11.18% respectively under moderate earthquakes when D-VSP system uses only first fuse, saving second fuse for severe earthquakes. However, in a strong earthquake, both of the shear panels absorb seismic energy and can control vibration better than conventional systems with one level control mechanism. The proposed system using a weaker panel can better control an extensive range of earthquakes as well as the earthquake with foreshocks.

Event-Triggered Adaptive Higher-Order Sliding Mode Tracking Control for Steer-by-Wire Systems

Abstract The model uncertainty of the steer-by-wire (SbW) system and the limitation of communication bandwidth will have a negative effect on its control performance. For this reason, this paper proposes an event-triggered high-order sliding mode control for uncertain SbW systems. First, to save communication and computing resources, an event-triggering mechanism that depends on the system state is proposed for the SbW system, such that both communication and computing resources can be saved. Second, an event-triggered adaptive higher-order sliding mode (ET-AHOSM) control is proposed for the closed-loop SbW system. The assumptions about the global Lipschitz of nonlinearity and the a priori bounds of the disturbance are no longer required in the control design. Much importantly, the control input continuity can be guaranteed even there is the event-triggering communication in the controller-to-actuator channel. Theoretical analysis shows that the global practical finite-time stability of the closed-loop SbW system can be obtained while avoiding Zeno behavior of the event-triggered control system. Finally, numerical simulation and experiments show that the designed control method can reduce more than 1/2 of the calculation and communication resources while ensuring satisfactory tracking accuracy.

A reservoir computing approach for forecasting and regenerating both dynamical and time-delay controlled financial system behavior.

Significant research in reservoir computing over the past two decades has revived interest in recurrent neural networks. Owing to its ingrained capability of performing high-speed and low-cost computations this has become a panacea for multi-variate complex systems having non-linearity within their relationships. Modelling economic and financial trends has always been a challenging task owing to their volatile nature and no linear dependence on associated influencers. Prior studies aimed at effectively forecasting such financial systems, but, always left a visible room for optimization in terms of cost, speed and modelling complexities. Our work employs a reservoir computing approach complying to echo-state network principles, along with varying strengths of time-delayed feedback to model a complex financial system. The derived model is demonstrated to act robustly towards influence of trends and other fluctuating parameters by effectively forecasting long-term system behavior. Moreover, it also re-generates the financial system unknowns with a high degree of accuracy when only limited future data is available, thereby, becoming a reliable feeder for any long-term decision making or policy formulations.

Aeroload Simulation of Interceptor Missile using Fin Load Simulator

Interceptor missiles are designed to destroy enemy targets in air. Targets can be destroyed either in atmosphere or out of atmosphere. So for Air Defence scenario, a two layer protection system is required with one taking care of exo atmosphere and another endo atmosphere. In this Air Defence scenario, irrespective of target trajectory interceptor should neutralise it. So the control, guidance are to be designed and validated thoroughly with various scenarios of interceptor and target. These interceptors sense the rates from rate gyroscopes and accelerations from accelerometers which are fitted on board the interceptor. The navigation algorithm calculates the interceptor’s position and velocity from these rates and accelerations from time to time. Using these interceptor data and target information received from ground RADAR or on board seeker, guidance calculates accelerations demand and subsequently rate demand. The control algorithm runs in on board mission computer along with guidance. The control algorithm calculates the commanded rate and eventually commanded deflections to the control fins to move towards the target. The fins have to move as per commanded deflections to meet the mission objective of hitting the target. But the load known as aeroload which comes on the control fins during mission, causes control fins not to move as per command. Due to the difference between control command and physical movement of fin, the expected path towards target deviates. This increases the miss distance and also misses the target hit. This aeroload scenario is to be simulated on ground and some feature is to be designed to take care of it during mission. By studying the control system behaviour due to load, the control autopilot is to be automatically tuned to compensate for the loss in commanded deflections. This scenario can be carried out in Hardware-in-Loop simulation (HILS) setup. Mission load conditions can be applied on hardware actuation system in HILS setup and mission performance can be seen and also with different loads and different autopilot tunings.

An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm

In recent years, a building’s energy performance is becoming uncertain because of factors such as climate change, the Covid-19 pandemic, stochastic occupant behavior and inefficient building control systems. Sufficient measurement data is essential to predict and manage a building’s performance levels. Assessing energy performance of buildings at an urban scale requires even larger data samples in order to perform an accurate analysis at an aggregated level. However, data are not only expensive, but it can also be a real challenge for communities to acquire large amounts of real energy data. This is despite the fact that inadequate knowledge of a full population will lead to biased learning and the failure to establish a data pipeline. Thus, this paper proposes a Gaussian mixture model (GMM) with an Expectation-Maximization (EM) algorithm that will produce synthetic building energy data. This method is tested on real datasets. The results show that the parameter estimates from the model are stable and close to the true values. The bivariate model gives better performance in classification accuracy. Synthetic data points generated by the models show a consistent representation of the real data. The approach developed here can be useful for building simulations and optimizations with spatio-temporal mapping.