Control Law Parameters Research Articles

Mean square bounded attitude coordination control of spacecraft formation with stochastic uncertainties

This paper addresses the attitude coordination control problem of spacecraft formation on SO(3) with attitude constraints and stochastic uncertainties. To achieve the control objective, the attitude coordination control law is designed based on the behavioral formation approach and the modified super-twisting control techniques. The sufficient conditions are given for the closed-loop stochastic SFF system to reach the mean square bounded attitude coordination without breaking the attitude constraints. These conditions clearly indicate the way to select the control law parameters, which is convenient in practice. Under these conditions, upper bounds of the system state errors are explicitly estimated, which depends on the stochastic uncertainties, the behavior weights, and the control law parameters. Simulation results verify the applicability and effectiveness of the proposed control law.

Grammatical-Evolution-based parameterized Model Predictive Control for urban traffic networks

While Model Predictive Control (MPC) is a promising approach for network-wide control of urban traffic, the computational complexity of the, often nonlinear, online optimization procedure is too high for real-time implementations. In order to make MPC computationally efficient, this paper introduces a parameterized MPC (PMPC) approach for urban traffic networks that uses Grammatical Evolution to construct continuous parameterized control laws using an effective simulation-based training framework. Furthermore, a projection-based method is proposed to remove the nonlinear constraints that are imposed on the parameters of the parameterized control laws and to guarantee the feasibility of the solution of the MPC optimization problem. The performance and computational efficiency of the constructed parameterized control laws are compared to those of a conventional MPC controller in an extensive simulation-based case study. The results show that the parameterized control laws, which are automatically constructed using Grammatical Evolution, decrease the computational complexity of the online optimization problem by more than 80% with a decrease in performance by less than 10%.

Distributed Adaptive Control with Momentum for Synchronization of Linear Agents

This paper studies the problem of learning-based adaptive state feedback control for networks of heterogeneous agents with input and matched uncertainties. We propose a control strategy that guarantees that the closed-loop synchronization error of all agents in the network is bounded. Adaptive control laws are designed via matching conditions to approximate the dynamics of each agent, which are partially unknown. Uncertainty parameters in the model are suppressed using adaptive optimal modification theory. Moreover, a momentum-based technique is also introduced in our framework for improving the convergence behavior of the adaptive control law parameters computation. Finally, a simulation example for Cooperative Adaptive Cruise Control (CCAC) is presented to validate the effectiveness of the control scheme. Numerical examples show that momentum-based techniques reach the bounded error set faster than gradient-based techniques.

Exponentially Stable Adaptive Control of MIMO Systems with Unknown Control Matrix

The scope of this research is a problem of direct model reference adaptive control of linear time-invariant multi-input multi-output (MIMO) plants without any a priori knowledge about system matrices. To handle it, a new method is proposed, which includes three main stages. Firstly, using the well-known DREM procedure, the plant parametrization is made to obtain the linear regressions, in which the plant matrices and state initial conditions are the unknown parameters. Secondly, such regressions are substituted into the known equations for the controller parameters calculation. Thirdly, the controller parameters are identified using the novel adaptive law with the exponential rate of convergence. To the best of the authors’ knowledge, such a method is the first one to provide the following features simultaneously: 1) it is applicable for the unknown MIMO systems (e.g. without any information about state or control allocation matrices, the sign of the latter, etc.); 2) it guarantees the exponential convergence of both the parameter and tracking errors under the mild requirement of the regressor finite excitation; 3) it ensures element-wise monotonicity of the transient curves of the control law parameters matrices. The results of the conducted experiments with the model of a rubber and ailerons control of a small passenger aircraft corroborate all theoretical results.

Experimental Study on the Optimal-Based Vibration Control of a Wind Turbine Tower Using a Small-Scale Electric Drive with MR Damper Support

The paper presents an experimental implementation of an optimal-based vibration control for a scaled wind turbine tower-nacelle structure. A laboratory model of the approximate power scale of 340 W, equipped with a nonlinear tuned vibration absorber (TVA), is analysed. For control purposes, a combined operation of a small-scale electric servo drive and a magnetorheological (MR) damper is used in the TVA system. Nonlinearities of both the electric drive and the MR damper are intrinsic parts of the adopted nonlinear control concept. The aim of the research is the simple-hardware real-time implementation and the experimental investigation of the simultaneous actuator and damper control, including the analysis of the influence of optimal control law parameters and quality function weights on the vibration attenuation efficiency and actuator energy demand. As a reference, an optimal-based, modified ground-hook control with the single goal of the primary structure deflection minimisation is used along with the passive system with zero MR damper current and idling electric actuator, proving the advantages of the proposed method. The regarded solutions guarantee 57% maximum structure deflection reduction concerning the passive TVA configuration, using an MR damper of 32 N maximum force and an electric drive of 12.5 N nominal force and 0.76 W nominal power. An interesting alternative is the optimal control concept tuned with regard to the actuator power minimisation—it provides 30% maximum structure deflection attenuation (concerning the passive TVA configuration) while using a passive damper of 3.3 N maximum force and an actuator of 0.17 W nominal power only. It makes evident the advantage of the properly tuned optimal control algorithm over the modified ground-hook law—it requires 51% less actuator energy than the latter parametrised to exhibit the same vibration attenuation properties.

Sliding mode controller design based on simple closed loop set point experiment for higher order processes with dead time

Abstract In this work, a sliding mode controller (SLMC) design approach has been proposed based on second order plus dead time process (SOPDTP). The SOPDTP model of the industrial processes have been obtained by performing a simple closed-loop set-point experiment (CLSPE) having a proportional controller only. The operating procedure of SLMC comprises of continuous and discontinuous control law. The parameters of continuous control law are derived by considering SOPDTP parameters and using the root locus technique. The discontinuous control law parameters are obtained by minimizing a performance index with the help of grasshopper optimization technique. The proposed SLMC design method has been validated by considering several examples with higher order process having diverse dynamics. The performance improvement by the proposed method over the recently reported work has been observed under nominal, perturbed and noisy conditions.

Cybersecurity and dynamic operation in practice: Equipment impacts and safety guarantees

Though dynamic operation of chemical processes has been extensively explored theoretically in contexts such as economic model predictive control or even considering the potential for cyberattacks on control systems creating non-standard operating policies, important practical questions remain regarding dynamic operation. In this work, we look at two of these with particular relevance to process safety: (1) evaluating dynamic operating policies with respect to process equipment fidelity and (2) evaluating procedures for determining the parameters of an advanced control law that can promote both dynamic operation as well as safety if appropriately designed. Regarding the first topic, we utilize computational fluid dynamics and finite element analysis simulations to analyze how cyberattacks on control systems could impact a metric for stress in equipment (maximum Von Mises stress) over time. Subsequently, we develop reduced-order models showing how both a process variable and maximum Von Mises stress vary over time in response to temperature variations at the boundary of the equipment, to use in evaluating how advanced control frameworks might impact and consider the stress. We close by investigating options for obtaining parameters of an economic model predictive control design that would need to meet a variety of theoretical requirements for safety guarantees to hold. This provides insights on practical safety aspects of control theory, and also indicates relationships between control and design from a safety perspective that highlight further relationships between design and control under dynamic operation to deepen perspectives from the computational fluid dynamics and finite element analysis discussions.

Quaternion feedback attitude control system design based on weighted–[formula omitted]–gain performance

The operation and accomplishment of a satellite's mission depend on its capacity to control its attitude. A rotation matrix parameterization, such as a (unit) quaternion, is frequently used to represent the attitude information needed for attitude control. The quaternion can represent the attitude globally. However, the non-uniqueness property of the quaternion causes difficulty in ensuring stability. A quaternion-based attitude control system with an augmented dynamical system is a class of nonlinear continuous quaternion feedback system to tackle that problem. However, tuning the control law's parameters causes further difficulty. This paper addresses the parameters tuning based on weighted–L2–gain performance in the form of linear matrix inequalities such that the equilibrium set of the attitude control system has the guarantee of the asymptotically stable in the large. The proposed parameter tuning method reduces the setting complexity of the controller parameters that comprise scalars and matrices, i.e., from five to two parameters. It is equivalent to reducing from 37 to 4 parameters' elements. In addition, a disturbance observer is proposed as an additional control torque to overcome angular velocity bias and external disturbance bad effects. We proved that the disturbance estimation error is bounded. Simulations verified the effectiveness of the proposed control systems.

Multivariable-Feedback Sliding-Mode Control of Bidirectional DC/DC Converter in DC Microgrid for Improved Stability with Dynamic Constant Power Load

In order to mitigate the influence of negative impedance characteristics on the stability of a DC microgrid with a constant power load, and the influence of nonlinear characteristics of a bidirectional converter on DC bus voltage, a control method of a bidirectional converter based on a multivariable-feedback sliding-mode control is proposed. The traditional control method usually uses the DC bus voltage error as a single controlled variable to optimize the design of the control law, which will lead to the lack of global coordination of each state variable. In this paper, the multi-state process variables-feedback sliding-mode control of a bidirectional DC/DC converter is designed, which can effectively improve the stability of the DC bus voltage, despite dynamic power disturbances. Firstly, the model of a bidirectional DC/DC converter, as well as the state equations of a converter with constant power load and resistive load, are analyzed. Secondly, to express various dynamic characteristics of the DC bus voltage fluctuation process, the output voltage error, the inductor current error and its integral are defined as the controlled state variables. Then, the multivariable weight combination-based sliding-mode surface is defined, and the sliding-mode controller is derived from a fast exponential power reaching law. Thirdly, the existence and stability of the multivariable-feedback sliding-mode control and the choice of control law parameters are discussed. Finally, MATLAB / Simulink software is used to simulate the proposed controller. Compared with PI and intelligent PID controllers, the proposed controller has the fastest response speed, the smallest steady-state voltage deviation and the best adjustment performance.

Swarm formation for perimeter surveillance in rectangular strips: a distributed model predictive approach

In this paper, a distributed control architecture is presented for addressing the cooperative perimeter surveillance of rectangular areas for multi-agent system whose dynamics is described by double integrator models subject to exogenous bounded disturbances. In particular, a novel methodology to generate proper state trajectories of a swarm of agents is provided and then exploited as references for the underlying model predictive controllers bank. Specifically, the swarm is first driven along the perimeter of a rectangular strip and next imposed to enter a given containment region while remaining simultaneously outside a forbidden region around a prescribed target. Steady-state conditions are analyzed and sufficient conditions derived in terms of the control law parameters. Simulation results put in light the main properties of the control architecture that is designed to adequately switch between two different distributed algorithms in order to guarantee constraints satisfaction within the capturing region despite any disturbance realization.

Finite-Time and Fixed-Time Synchronization of Coupled Switched Neural Networks Subject to Stochastic Disturbances

In this paper, we address the finite-time and fixed-time synchronization of a general class of switched neural networks (SNNs) with time delays subject to stochastic disturbances. Considering two types of switching in this class of SNNs: 1) intra-SNN state-dependent switching and 2) inter-SNN Markovian switching, we develop three control laws and derive three sets of sufficient conditions for both finite-time and fixed-time synchronization of SNNs subject to stochastic disturbances. We make two remarks on the effects of control-law parameters on synchronization settling time. Moreover, we derive several upper bounds of synchronization settling time and evaluate their pros and cons. Finally, we elaborate on two numerical examples to illustrate the viability of the theoretical results.

C-GLISp: Preference-Based Global Optimization Under Unknown Constraints With Applications to Controller Calibration

Preference-based global optimization algorithms minimize an unknown objective function only based on whether the function is better, worse, or similar for given pairs of candidate optimization vectors. Such optimization problems arise in many real-life examples, such as finding the optimal calibration of the parameters of control law. The calibrator can judge whether a particular combination of parameters leads to a better, worse, or similar closed-loop performance. Often, the search for the optimal parameters is also subject to unknown constraints. For example, the vector of calibration parameters must not lead to closed-loop instability. This article extends an active preference learning algorithm introduced recently to handle unknown constraints. The proposed method, called C-GLISp, looks for an optimizer of the problem only based on preferences expressed on pairs of candidate vectors and on whether a given vector is reported feasible and/or satisfactory. C-GLISp learns a surrogate of the underlying objective function based on the expressed preferences and a surrogate of the probability that a sample is feasible and/or satisfactory based on whether each of the tested vectors was judged as such. The surrogate functions are used iteratively to propose a new candidate vector to test and judge. Numerical benchmarks and a semiautomated control calibration task demonstrate the effectiveness of C-GLISp, showing that it can reach near-optimal solutions within a small number of iterations.

Similarity Relations of PID Flight Control Parameters of Scaled-Model and Full-Size Aircraft

To carry out a scaled-model flight test of a high-gain aircraft with a proportional plus integral plus derivative (PID) flight control design, the similarity relations of the flight control law parameters of scaled-model and full-size aircraft must be systematically studied. In this article, the general similarity relations applicable to commonly used flight control law configurations and feedback signals are derived and analyzed. Based on the control principle, PID control parameters are divided into two types: feedback gains and series combined dynamic elements. The general similarity relations for the control parameters of these two types of elements are derived from system similarity theory. Finally, closed-loop flight control simulations of a fighter aircraft and its scaled model are completed. The simulation results show that the scaled-model and full-size aircraft with similar PID control law parameters are physically similar, validating the similarity relation conclusions proposed in this article.

Handiski simulator performance under PSO-based washout and control parameters optimization

The transfer of advanced technology to the person with a disability who wishes to practice a sporting activity is gaining momentum in the science/engineering world. This paper seeks to approve more comfort and sensations for people with paraplegia on a motion simulation platform during a ski operation. The Motion Cueing Algorithm (MCA) has proven itself for sensation reproduction, which we propose to improve by integrating the physical limits of our 8-DoF mechatronics platform. An extended classical MCA is proposed to respond to the significant lack of restored sensation in the intermediate frequency range. A Particle Swarm Optimization (PSO) is constructed to perform optimal washout filter parameters and control law parameters. Consequently, the reproduced skier trajectory stability is obtained. The results show that the proposed algorithm will overcome the physical limitation problem in the Handiski simulator, improve the realism of movement sensation, and reduce the false cues to enhance dynamic fidelity.

Fuzzy-Based Adaptive Dynamic Surface Control for a Type of Uncertain Nonlinear System with Unknown Actuator Faults

In this paper, the adaptive control problem of a type of uncertain nonlinear system is addressed. The system discussed includes unknown nonlinear functions, uncertain nonlinear dynamics, and unknown actuator faults. Based on the fuzzy logic systems and dynamic surface control technique, an adaptive fuzzy control law is designed to solve the tracking control problem. In control law design, fuzzy logic systems are utilized to approximate uncertain nonlinear functions, and with the help of the dynamic surface control technique, the problem of the “explosion of complexity” can be overcome. Through stability analysis, it is confirmed that all of the signals in the closed-loop system are semi-global bounded, and the convergence of the tracking error to the specified small neighborhood of the origin can be ensured by adjusting the control law parameters. Finally, the effectiveness of the proposed control law is verified by simulation examples.

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.

Automation of the coordinated road traffic control process

Purpose. To increase output of traffic network while deriving new dependencies of transport flow characteristics upon control law parameters and using them to develop an algorithm to automate a process of the transport flow coordinated regulation within towns and cities. Methodology. The aggregated simulation model, describing processes of road traffic formation within a local section of urban traffic system, is used for the analysis of dependencies to assess quality of transport flow control upon correcting conditions being parameters of traffic cycles within intersections. The abovementioned should involve a PID controller as well as a negative feedback idea to automate control of the road traffic process. In this context, control criterion is applied to determine critical flow intensity if a traffic jam occurs. The criterion makes it possible to identify changes in the road traffic nature. To determine the optimal settings of the PID controller for different stocks of the stability control system, the standard deviation of the controlled variable from the set value and the mean static control error are defined. The derived dependencies of control quality criteria upon a law of flow regulation have helped develop a new algorithm of the coordinated automated road traffic control within a local section of urban traffic section using standard control action. Simulation experiments made it possible to assess efficiency of the proposed algorithm of control compared with available ones applied in terms of different road conditions. Findings. As a part of the studies, algorithm of the coordinated automated road traffic has been developed. It helps support maximum output of road systems while monitoring changes in the traffic environment characteristics. Originality. For the first time, dependencies of assessment criteria of traffic flow control upon the parameters of a law of flow intensity regulation have been identified if maximum output of road systems is provided. The dependencies have helped substantiate optimum control criteria for different road conditions while automating a process of coordinated traffic flow regulation within local section of urban traffic network. Practical value. The proposed dependencies of assessment criteria of traffic flow control upon the parameters of a law of flow intensity regulation as well as the algorithm of automated coordinated control are the theoretical foundations to solve such an important applied scientific problem as automation of a process of urban traffic flow regulation.

Design and Performance Evaluation of a Step-Up DC–DC Converter with Dual Loop Controllers for Two Stages Grid Connected PV Inverter

In this work, a non-isolated DC–DC converter is presented that combines a voltage doubler circuit and switch inductor cell with the single ended primary inductor converter to achieve a high voltage gain at a low duty cycle and with reduced component count. The converter utilizes a single switch that makes its control very simple. The voltage stress across the semiconductor components is less than the output voltage, which makes it possible to use the diodes with reduced voltage rating and a switch with low turn-on resistance. In particular, performance principle of the proposed converter along with the steady state analysis such as voltage gain, voltage stress on semiconductor components, and design of inductors and capacitors, etc., are carried out and discussed in detail. Moreover, to regulate a constant voltage at a DC-link capacitor, back propagation algorithm-based adaptive control schemes are designed. These adaptive schemes enhance the system performance by dynamically updating the control law parameters in case of PV intermittency. Furthermore, a proportional resonant controller based on Naslin polynomial method is designed for the current control loop. The method describes a systematic procedure to calculate proportional gain, resonant gain, and all the coefficients for the resonant path. Finally, the proposed system is simulated in MATLAB and Simulink software to validate the analytical and theoretical concepts along with the efficacy of the proposed model.

A Discrete Fractional Order Adaptive Law for Parameter Estimation and Adaptive Control

In this article, a discrete fractional order adaptive law (DFOAL) is designed based on the Caputo fractional difference to perform parameter estimation of structured uncertainties. The paper provides a rigorous stability analysis of the DFOAL parameter estimation method. The DFOAL is then modified in order to improve parameter estimator performance to show that, under certain conditions, it provides asymptotic convergence to the true parameter values even when the regressor is not persistently exciting. A method to allow for practical implementation of the DFOAL and the modified DFOAL is developed. Finally, the modified DFOAL is used to identify the plant parameters in an indirect adaptive control law for a class of nonlinear discrete-time systems with structured uncertainty.

Suppression of axial-torsional vibrations of a distributed drilling system by the eigenvector contradiction method

This article proposes an active control strategy to suppress self-excited coupled axial-torsional vibrations of a distributed drill-string system while the coupling takes place through the bit-rock interaction. The drill-string model is expressed as Neutral-type Delay Differential Equations (NDDEs) with constant and state-dependent state delays and constant input delays. As a first step in the novel controller design, an implementable input transformation is introduced, resulting in the elimination of the neutral terms from the equations of motion. This supports a simplified next step of stabilizing controller design. In the second step, a new analytic method named the “Eigenvector Contradiction Method” is proposed to provide sufficient conditions to ensure that all eigenvalues have real parts less than a prescribed value. Based on this criterion, an automated parametric feedback control law is designed. A case study simulation is presented to illustrate the effectiveness of the proposed control strategy.