General Control Research Articles

Global Structure of Determining Matrices for a Class of Differential Control Systems

This paper developed and established unprecedented global results on the structure of determining matrices of generic double time-delay linear autonomous functional differential control systems, with a view to obtaining the controllability matrix associated with the rank condition for the Euclidean controllability of the system. The computational process and implementation of the controllability matrix were demonstrated on the MATLAB platform to determine the controllability disposition of a small-problem instance. Finally, the work examined the computing complexity of the determining matrices.

Anthropometric scaling of musculoskeletal models of the hand captures age-dependent differences in lateral pinch force

Musculoskeletal models and computer simulations enable non-invasive study of muscle function and contact forces. Hand models are useful for understanding the complexities of hand strength, precision movement, and the dexterity required during daily activities. Yet, generic models fail to accurately represent the entire scope of the population, while subject-specific models are labor-intensive to create. The objective of this study was to assess the efficacy of scaled generic models to represent the broad spectrum of strength profiles across the lifespan. We examined one hundred lateral pinch simulations using a generic model of the wrist and thumb anthropometrically scaled to represent the full range of heights reported for four ages across childhood, puberty, older adolescence, and adulthood. We evaluated maximum lateral pinch force produced, muscle control strategies, and the effect of linearly scaling the maximum isometric force. Our simulations demonstrated three main concepts. First, anthropometric scaling could capture age-dependent differences in pinch strength. Second, a generic muscle control strategy is not representative of all populations. Lastly, simulations do not employ optimal fiber length to complete a lateral pinch task. These results demonstrate the potential of anthropometrically-scaled models to study hand strength across the lifespan, while also highlighting that muscle control strategies may adapt as we age. The results also provide insight to the force–length relationship of thumb muscles during lateral pinch. We conclude that anthropometric scaling can accurately represent age characteristics of the population, but subject-specific models are still necessary to represent individuals.

A Generic Two-Vector Model Predictive Control for Hybrid Multilevel Converters

In this article, a generic two-vector model predictive control (TV-MPC) strategy is proposed for the hybrid multilevel converters (HMCs). The proposed method selects two optimal voltage vectors among all the vector candidates using a geometric positioning approach to reduce the computational burden, which is a common issue in the existing MPC methods for HMC. Then duty cycles of the two selected vectors are optimized to minimize the current tracking error, such that the current tracking performance can be enhanced compared with the conventional MPC. In addition, the voltages of the floating dc capacitors can be balanced by evaluating all switching sequences that belong to the optimal voltage vectors with optimal duty cycles. The concept of the proposed TV-MPC is generic and applicable for any HMCs. A typical HMC based on active neutral-point-clamped topology is adopted as a case study in this work. Comprehensive simulation and experimental studies are performed on an all silicon-carbide HMC prototype to validate the effectiveness of the proposed control scheme.

Hierarchical task-based formation control and collision avoidance of UAVs in finite time

This work addresses the problem of distributedly controlling the position of a group of Unmanned Aerial Vehicles (UAVs), that are considered as holonomic three-dimensional agents, to achieve a desired formation while collisions are avoided. We propose a generic control scheme computed by the adaptive convex combination of two control laws dealing with two, possibly conflicting, tasks, which are formation control and obstacle avoidance. The main contributions of the paper are threefold: First, the whole proposed scheme is distributed by nature, since the formation control is formulated as a consensus problem of virtual agents and the collision avoidance strategy is reactive, valid for unknown environments. Second, two control protocols are proposed to guarantee convergence to the desired formation in finite or predefined time; in the last case, the formation is achieved in a constant time independently of the initial UAVs positions. And finally, the control scheme generates continuous control signals in spite of activation and deactivation of the obstacle avoidance task, and stability is proved even in the transition of tasks. The effectiveness of the proposed approach is shown through realistic simulations and real experiments for a group of quadrotors.

Self-Learning Controllers in the Oil and Gas Industry

Recently, solving the optimization-control problems by using artificial intelligence has widelyappeared in the petroleum fields in exploration and production. This paper presents the stateof-the-art reinforcement-learning algorithm applying in the petroleum optimization-controlproblems, which is called a direct heuristic dynamic programming (DHDP). DHDP has twointeractive artificial neural networks, which are the critic network (provider acritique/evaluated signal) and the actor network (provider a control signal). This paper focuseson a generic on-line learning control system in Markov decision process principles.Furthermore, DHDP is a model-free learning design that does not require prior knowledgeabout a dynamic model; therefore, DHDP can be appllied with any petroleum equipment ordevise directly without needed to drive a mathematical model. Moreover, DHDP learns byitself (self-learning) without human intervention via repeating the interaction between anequipment and environment/process. The equipment receives the states of theenvironment/process via sensors, and the algorithm maximizes the reward by selecting thecorrect optimal action (control signal). A quadruple tank system (QTS) is taken as a benchmarktest problem, that the nonlinear model responses close to the real model, for three reasons:First, QTS is widely used in the most petroleum exploration/production fields (entire system orparts), which consists of four tanks and two electrical-pumps with two pressure control valves.Second, QTS is a difficult model to control, which has a limited zone of operating parametersto be stable; therefore, if DHDP controls on QTS by itself, DHDP can control on otherequipment in a fast and optimal manner. Third, QTS is designed with a multi-input-multioutput (MIMO) model for analysis in the real-time nonlinear dynamic system; therefore, theQTS model has a similar model with most MIMO devises in oil and gas field. The overalllearning control system performance is tested and compared with a proportional integralderivative (PID) via MATLAB programming. DHDP provides enhanced performancecomparing with the PID approach with 99.2466% improvement.

A novel adaptive generic model control strategy for internal thermally coupled air separation columns with multivariable recursive estimation

A gasification process is key in clean electricity production and decarbonization in an integrated gasification combined cycle (IGCC) plant. The quality of the gasification process depends on the stability of the product-concentration streamed from the internal thermally coupled air separation column (ITCASC) unit. There is a need for a stable product-concentration supply to sustain clean electricity production. A proportional-integral-derivative control (PID) control and a nonlinear internal model control (NIMC) scheme are first explored to control the IGCC-ITCASC. Moreover, a robust generic model control (RGMC) is employed to deal with the IGCC-ITCASC inherent nonlinearity and strong interaction that make it very difficult to control. To obtain a satisfactory control performance, an adaptive generic model control (AGMC) with a multivariable recursive estimation is developed. The compared control systems show that AGMC can better handle both the system nonlinearity and strong interaction while improving the control performance.

Shortcomings of human-in-the-loop optimization of an ankle-foot prosthesis emulator: a case series

Human-in-the-loop optimization allows for individualized device control based on measured human performance. This technique has been used to produce large reductions in energy expenditure during walking with exoskeletons but has not yet been applied to prosthetic devices. In this series of case studies, we applied human-in-the-loop optimization to the control of an active ankle-foot prosthesis used by participants with unilateral transtibial amputation. We optimized the parameters of five control architectures that captured aspects of successful exoskeletons and commercial prostheses, but none resulted in significantly lower metabolic rate than generic control. In one control architecture, we increased the exposure time per condition by a factor of five, but the optimized controller still resulted in higher metabolic rate. Finally, we optimized for self-reported comfort instead of metabolic rate, but the resulting controller was not preferred. There are several reasons why human-in-the-loop optimization may have failed for people with amputation. Control architecture is an unlikely cause given the variety of controllers tested. The lack of effect likely relates to changes in motor adaptation, learning, or objectives in people with amputation. Future work should investigate these potential causes to determine whether human-in-the-loop optimization for prostheses could be successful.

Discussion of Numerical Methods used in Positive Displacement Comprehensive Mechanistic Models: Case Study using the Z-Compressor

Comprehensive mechanistic models have been widely used for simulating positive displacement (PD) compressors or expanders. Such models are based on a thermodynamic analysis of generic control volumes that describe the working chambers. The thermodynamic analysis results in a set of non-linear ordinary differential equations (ODEs) that describe conservation of mass and energy. The derived ODEs are used to predict the evolution of the thermodynamic properties of the working fluid with respect to crank angle or time. The solution scheme of a mechanistic model involves different layers of solvers that handle the step-by-step integration, the cycle-to-cycle continuity as well as the overall energy balance. Since most of the PD machines feature multiple control volumes that merge or split (e.g. scroll-type) or wrap-around a rotor (e.g. screw-type) the integration scheme faces challenges due to the stiffness of the equations or discontinuities in the control volume definition.In this paper, a discussion of different integration schemes and numerical considerations are proposed that are applied to a novel compressor called the z-compressor which presents a high-degree of intrinsic stiffness. A simplified comprehensive model is developed which is validated against experimental mass flow rate data for a prototype compressor with an average model-predicted error of less than 2%. This model is used to explore the differences in two ODE solvers from the ODE suite in MATLAB, (ODE15s, and ODE113). These results are further compared against an existing, simplified, Z-compressor model developed in PDSim which uses an explicit, lower order, solver the Adaptive Runge-Kutta (RKF). The results suggest that the semi-implicit solver (ODE15s) provides an efficient means for stepping through the compression process ODEs compared to the explicit solvers.

A Quadratic Programming Approach to Manipulation in Real-Time Using Modular Robots

Motion planning in high-dimensional space is a challenging task. In order to perform dexterous manipulation in an unstructured environment, a robot with many degrees of freedom is usually necessary, which also complicates its motion planning problem. Real-time control brings about more difficulties in which robots have to maintain the stability while moving towards the target. Redundant systems are common in modular robots that consist of multiple modules and are able to transform into different configurations with respect to different needs. Different from robots with fixed geometry or configurations, the kinematics model of a modular robotic system can alter as the robot reconfigures itself, and developing a generic control and motion planning approach for such systems is difficult, especially when multiple motion goals are coupled. A new manipulation planning framework is developed in this paper. The problem is formulated as a sequential linearly constrained quadratic program (QP) that can be solved efficiently. Some constraints can be incorporated into this QP, including a novel way to approximate environment obstacles. This solution can be used directly for real-time applications or as an off-line planning tool, and it is validated and demonstrated on the CKBot and the SMORES-EP modular robot platforms.

Investigation of the closed-loop control system on the DFIG dynamic models in transient stability studies

In the power systems where a significant part of the total generated power is supplied by wind energy, the transient stability of the grid should be analyzed properly. This paper discusses the influence of the closed-loop control system of doubly-fed induction generator (DFIG) on transient stability. In this process, the stator and rotor electrical dynamics are considered during tuning the controllers. Accordingly, the DFIG with power electronics converters in the closed-loop control mode with generic PI controllers is considered. For this study, the dynamic model of the rotor side converter with more detail of the control system is used, because it has a direct effect on the dynamics of the generator speed/torque and hence on the system stability. Also, to tune the control parameters of the power electronics converters, the Gershgorin theorem is applied. Once an appropriate set of control parameters is obtained, the DFIG model is simplified and time-domain simulation is performed. For validation of the influence of modeling adequacy on closed-loop controlled DFIG, transient stability study under the different operating conditions is investigated. According to the simulation results, it is observed that for the closed-loop DFIG, neglecting the stator transients does not remove the high-frequency mode. Thus, the high-frequency mode is due to the rotor electrical dynamics. Additionally, the power system stability studies for closed-loop DFIG are not model order dependent. Accordingly, for the closed-loop controlled DFIG in the transient stability studies where fast electrical transients are not of interest, a simplified model, whereby both stator and rotor dynamics are neglected, is adequate.

Filtered Split-Path Nonlinear Integrator: A Hybrid Controller for Transient Performance Improvement

The filtered split-path nonlinear integrator (F-SPANI) is a generic nonlinear controller designed to improve the transient performance of linear (motion) systems in terms of overshoot. The main idea underlying F-SPANI is that the amplitude and phase of an integrator can be tuned using independent filters, resulting in more efficient use of the buffer of the integrator. In this article, a general description of F-SPANI is presented. In addition, a stability analysis result is presented that provides sufficient conditions in the form of linear matrix inequalities (LMIs) for closed-loop stability analysis on the basis of construction of a common quadratic Lyapunov function (CQLF). The ease of the design, implementation, and the potential of the proposed controller are illustrated both in simulations and in experiments on an industrial pick-and-place machine.

Forecasting Models of Daily Energy Generation by PV Panels Using Fuzzy Logic

This paper contains studies of daily energy production forecasting methods for photovoltaic solar panels (PV panel) by using mathematical methods and fuzzy logic models. Mathematical models are based on analytic equations that bind PV panel power with temperature and solar radiation. In models based on fuzzy logic, we use Adaptive-network-based Fuzzy Inference Systems (ANFIS) and the zero-order Takagi-Sugeno model (TS) with specially selected linear and non-linear membership functions. The use of mentioned membership functions causes that the TS system is equivalent to a polynomial and its properties can be compared to other analytical models of PV panels found in the literature. The developed models are based on data from a real system. The accuracy of developed prognostic models is compared, and a prototype software implementing the best-performing models is presented. The software is written for a generic programmable logic controller (PLC) compliant to the IEC 61131-3 standard.

GCACS-IoD: A certificate based generic access control scheme for Internet of drones

Internet of drones (IoD) has gained significant importance in recent times due to its applications in several critical domains ranging from commercial to defense and rescue operations. With several drones flying in different zones to carry out specified tasks, the IoD can be beneficial to gather the real time data for interpretation by the users. However, the data access is carried out through an open channel and battery operated drones. Therefore, the drones’ security and privacy are crucial for accomplishing mission-critical, safety-critical, or surveillance operations. In 2020, Bera et al. presented a certificate based access control scheme for securing the IoD access and argued the scheme’s security through formal and informal methods. However, the analysis presented in this paper shows that the scheme of Bera et al. does not provide anonymity and is insecure against multiple threats, including drone impersonation, the man in the middle, and replay attacks. We then designed a generic certificate based access control scheme to provide inter-drone and drone to ground station access control/authentication in the IoD domain (GCACS-IoD). The GCACS-IoD is provably secure against the known attacks and provides anonymity. GCACS-IoD extends security while preserving computation and communication efficiencies.

On Inf-Convolution-Based Robust Practical Stabilization Under Computational Uncertainty

This work is concerned with practical stabilization of nonlinear systems by means of inf-convolution-based sample-and-hold control. It is a fairly general stabilization technique based on a generic non-smooth control Lyapunov function (CLF) and robust to actuator uncertainty, measurement noise, etc. The stabilization technique itself involves computation of descent directions of the CLF. It turns out that non-exact realization of this computation leads not just to a quantitative, but also qualitative obstruction in the sense that the result of the computation might fail to be a descent direction altogether and there is also no straightforward way to relate it to a descent direction. Disturbance, primarily measurement noise, complicate the described issue even more. This work suggests a modified inf-convolution-based control that is robust w. r. t. system and measurement noise, as well as computational uncertainty. The assumptions on the CLF are mild, as, e. g., any piece-wise smooth function, which often results from a numerical LF/CLF construction, satisfies them. A computational study with a three-wheel robot with dynamical steering and throttle under various tolerances w. r. t. computational uncertainty demonstrates the relevance of the addressed issue and the necessity of modifying the used stabilization technique. Similar analyses may be extended to other methods which involve optimization, such as Dini aiming or steepest descent.

Implementable Self-Learning PID Controller Using Least Mean Square Adaptive Algorithm

More than 95% of the industrial controllers in use today are PID or modified PID controllers. However, the PID is manually tuning to be responsive so that the Process Variable is rapidly and steady moved to track the set point with minimize overshoot and stable output. The paper presents generic teal-time PID controller architecture. The developed architecture is based on the adaption of each of the three controller parameters (PID) to be self- learning using individual least mean square algorithm (LMS). The adaptive PID is verified and compared with the classical PID. The rapid realization of the adaptive PID architecture allows the readily fabrication into a hardware version either ASIC or reconfigurable.

A hierarchical representation of behaviour supporting open ended development and progressive learning for artificial agents

One of the challenging aspects of open ended or lifelong agent development is that the final behaviour for which an agent is trained at a given moment can be an element for the future creation of one, or even several, behaviours of greater complexity, whose purpose cannot be anticipated. In this paper, we present modular influence network design (MIND), an artificial agent control architecture suited to open ended and cumulative learning. The MIND architecture encapsulates sub behaviours into modules and combines them into a hierarchy reflecting the modular and hierarchical nature of complex tasks. Compared to similar research, the main original aspect of MIND is the multi layered hierarchy using a generic control signal, the influence, to obtain an efficient global behaviour. This article shows the ability of MIND to learn a curriculum of independent didactic tasks of increasing complexity covering different aspects of a desired behaviour. In so doing we demonstrate the contributions of MIND to open-ended development: encapsulation into modules allows for the preservation and re-usability of all the skills acquired during the curriculum and their focused retraining, the modular structure serves the evolving topology by easing the coordination of new sensors, actuators and heterogeneous learning structures.

IGCACS-IoD: An Improved Certificate-Enabled Generic Access Control Scheme for Internet of Drones Deployment

Due to wide-spread use of the Information and Communications Technology (ICT) and Internet of Things (IoT) enabled smart devices, called unmanned aerial vehicles (UAVs) (popularly known as drones), a lot of potential applications of Internet of Drones (IoD) are available ranging from the military to civilian applications. Access control mechanism is an important potential security service that is needed to secure communication among the drones in their respective flying zones, and also among the drones and the Ground Service Station (GSS). In 2021, Chaudhry et al. proposed a certificate based generic access control scheme for IoD environment, called GCACS-IoD. Their claims about the possible security attacks resistant of GCACS-IoD is not justified. In fact, we first prove that GCACS-IoD is unable to protect the disclosure of the private key r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CR</sub> of the trusted control room (CR), which is extremely unfortunate careless design flaw and it leads to compromise the entire network. Using the disclosed private key r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CR</sub> , we further show that GCACS-IoD is completely insecure against other serious attacks, such as malicious drones deployment attack, drone/GSS impersonation attacks and Ephemeral Secret Leakage (ESL) attack, which lead to compromise the session key between any two drones communicating in a particular flying zone. We thus feel that there is a strong need to remedy such serious weaknesses found in Chaudhry et al.'s GCACS-IoD. An improved certificate-enabled generic access control scheme for IoD deployment, called as iGCACS-IoD, has been suggested, which overcomes the weaknesses found in the previous GCACS-IoD. The practical demonstration of iGCACS-IoD has been done through formal security verification and also through NS2 simulation study.

Generic PLL-Based Grid-Forming Control

In grid-forming control, the swing equation of a synchronous machine is emulated by a power controller. Thereby, frequency droop, power-oscillation damping, and/or virtual inertia can be obtained. In this letter, it is shown that a phase-locked loop—which is normally grid following—can be designed so as to, in turn, emulate a generic power controller, thus, becoming grid-forming.

Tiltrotor Conversion Maneuver Analysis with RCAS

Aeromechanics analysis is performed using the comprehensive analysis code RCAS (Rotorcraft Comprehensive Analysis System) to study the transient conversion maneuver of a tiltrotor. The analytical model is based on the XV-15 research tiltrotor aircraft in size and dynamic characteristics. A generic (not representative of XV-15) tiltrotor control system is developed to simulate a conversion maneuver. Hover and cruise performance of the present XV-15 analytical model is validated against available test data. The conversion calculation begins with a trim analysis at hover, which is followed by the conversion maneuver. During the maneuver analysis, the pilot control model is activated to fly the aircraft following a desired airspeed profile and minimum altitude change. Time histories of vehicle dynamics, rotor controls, rotor flapping, rotor performance, and blade structural loads are investigated for various transient conversion maneuvers. The aircraft acceleration during the transient maneuver has a significant influence on the rotor performance and loads.

A Generic ROS-Based Control Architecture for Pest Inspection and Treatment in Greenhouses Using a Mobile Manipulator

To meet the demands of a rising population greenhouses must face the challenge of producing more in a more efficient and sustainable way. Innovative mobile robotic solutions with flexible navigation and manipulation strategies can help monitor the field in real-time. Guided by Integrated Pest Management strategies, robots can perform early pest detection and selective treatment tasks autonomously. However, combining the different robotic skills is an error prone work that requires experience in many robotic fields, usually deriving on ad-hoc solutions that are not reusable in other contexts. This work presents Robotframework, a generic ROS-based architecture which can easily integrate different navigation, manipulation, perception, and high-decision modules leading to a faster and simplified development of new robotic applications. The architecture includes generic real-time data collection tools, diagnosis and error handling modules, and user-friendly interfaces. To demonstrate the benefits of combining and easily integrating different robotic skills using the architecture, two flexible manipulation strategies have been developed to enhance the pest detection in its early state and to perform targeted spraying in simulated and field commercial greenhouses. Besides, an additional use-case has been included to demonstrate the applicability of the architecture in other industrial contexts.