Reliable Controller Research Articles

Simulation of cyber physical systems behaviour using timed plant models

When developing a reliable controller for cyber-physical systems, one of the main issues is to guarantee that all behaviour properties of such systems will be accomplished. For this purpose, it is very important to find and use formalisms and tools in order to model the controller and the respective plant. Moreover, it is very important to consider the same formalism for modelling both. The accurate model of a plant is usually difficult to obtain. To solve this problem, several techniques for modelling the plant have been developed in the recent past years. However, some of them have lacks that do not allow a fast, reliable and flexible way for plant modelling. In this paper a systematic approach for modelling cyber physical systems using timed automata is presented. The proposed approach allows defining a systematic way for creating global models for both the plant and the controller. These models, may be used to simulate and validate the cyber physical systems. For this purpose software tools, as UPPAAL, and other simulation configurations, as Model-in-the-loop and Hardware-in-the-loop, can be used. In order to present and explain the proposed methodology, a virtual platform and a physical workbench have been developed, from a transport objects station example, where Model-in-the-loop and Hardware-in-the-loop configurations are used in a subsequent manner in the context of the presented system modelling methodology.

Modular design of artificial potential field and nonlinear model predictive control for a vehicle collision avoidance system with move blocking strategy

The collision avoidance (CA) system is a pivotal part of the autonomous vehicle. Ability to navigate the vehicle in various hazardous scenarios demands reliable actuator interventions. In a complex CA scenario, the increased nonlinearity requires a dependable control strategy. For example, during collisions with a sudden appearing obstacle (i.e. crossing pedestrian, vehicle), the abrupt increment of vehicle longitudinal and lateral forces summation during the CA maneuver demands a system with the ability to handle coupled nonlinear dynamics. Failure to address the aforementioned issues will result in collisions and near-miss incidents. Thus, to solve these issues, a nonlinear model predictive control (NMPC)-based path tracking strategy is proposed as the automated motion guidance for the host vehicle CA architecture. The system is integrated with the artificial potential field (APF) as the motion planning strategy. In a hazardous scenario, APF measures the collision risks and formulates the desired yaw rate and deceleration metrics for the path replanning. APF ensures an optimal replanned trajectory by including the vehicle dynamics into its optimization formulation. NMPC then acts as the coupled path and speed tracking controller to enable vehicle navigation. To accommodate vehicle comfort during the avoidance, NMPC is constrained. Due to its complexity as a nonlinear controller, NMPC can be time-consuming. Therefore, a move blocking strategy is assimilated within the architecture to decrease the system’s computational burden. The modular nature of the architecture allows each strategy to be tuned and developed independently without affecting each others’ performance. The system’s tracking performance is analyzed by computational simulations with several CA scenarios (crossing pedestrian, parked bus, and sudden appearing moving vehicle at an intersection). NMPC tracking performance is compared to the nominal MPC and linear controllers. The effect of move blocking strategies on NMPC performance are analyzed, and the results are compared in terms of mean squared error values. The inclusion of nonlinear tracking controllers in the architecture is shown to provide reliable CA actions in various hazardous scenarios. The work is important for the development of a reliable controller strategy for multi-scenario CA of the fully autonomous vehicle.

Non-fragile reliable sampled-data controller for nonlinear switched time-varying systems

This paper addresses dissipative-based stability analysis and robust mixed non-fragile reliable control design for an uncertain nonlinear switched system. In particular, actuator failures and time-varying delay with a known upper bound on the sampling intervals are taken into account of the considered system. By using a matrix inequality approach and by constructing a proper Lyapunov–Krasovskii functional that utilizes the complete available information about the actual sampling pattern and time-varying delay, we formulate stability conditions and a control design procedure in the form of linear matrix inequalities for obtaining the required result. More precisely, a dissipative based non-fragile reliable sampled-data controller is designed such that the resulting closed-loop switched system is reliable in the sense that it is exponentially stable and satisfies certain dissipative performance index under the given conditions. It should be mentioned that the implemented Lyapunov functionals are more general since they incorporate the nonlinearities of the system model in its design. Further, the performance of the proposed controller is demonstrated through a numerical example to show the validity and applicability of the proposed design technique.

Non‐fragile reliable control synthesis of the sugarcane borer

This study considers the problem of non-fragile reliable control synthesis for mathematical model of interaction between the sugarcane borer (Diatraea saccharalis) and its egg parasitoid Trichogramma galloi. In particular, the control could be substituted by periodic releases of a small population of natural enemies and hence it is important to propose the time-varying controller in sugarcane borer. The main aim of this study is to design a state feedback non-fragile (time-varying) reliable controller such that the states of the sugarcane borer system reach the equilibrium point within the desired period. A novel approach is proposed to deal with the uncertain matrices which appear in non-fragile reliable control. Finally, simulations based on sugarcane borer systems are conducted to illustrate the advantages and effectiveness of the proposed design technique. The result reveals that the proposed non-fragile control provides good performance in spite of periodic releases of a small population of natural enemies occurs.

Observer-Based Consensus Control Against Actuator Faults for Linear Parameter-Varying Multiagent Systems

This paper addresses the robust consensus reliable control problem against actuator faults for linear parameter-varying multiagent systems. First, the actuator faults are modeled via a polytopic uncertainty method. Second, a distributed observer is designed for the single agent by sharing the communication network sensors to estimate the state information. Then by these estimated information, a robust consensus reliable control protocol against actuator faults is obtained and desired disturbance rejection performance can be guaranteed by this proposed protocol. Third, the nonconvexity conditions of the consensus control protocol can be translated into an linear matrix inequality optimization problem via simple matrix calculation. Finally, the effectiveness of the proposed reliable controller scheme is illustrated by two examples.

Circuit Configuration and Control of a Grid-Tie Small-Scale Wind Generation System for Expanded Wind Speed Range

In this paper, a grid-tie small-scale wind generation system is proposed with innovations in both of the topology and the controller. A novel high step-up zero-current turning-on soft-switching rectifier and a storage branch are employed to improve the low-wind-speed (LWS) and high-wind-speed (HWS) performances. The optimized LWS operation is guaranteed by the rectifier, the battery pack, and film capacitors. The enhanced HWS performance is fulfilled by parallel operation of the inverter and the battery charge converter. Furthermore, the control strategy aiming at improving overall system efficiency and extending battery lifespan and exploitable wind speed range is adopted. A state-of-charge (SOC) control method is used and ensures SOC is most frequently within a range of 20–80%. The proposed method not only slows down the battery capacity fading by SOC scope limitation and by charge/discharge current restriction, but also provides safety capacity margin in case of extreme HWS condition. Also, a simple but reliable dead-zone controller is applied on the battery charge/discharge converter to control the dc-link voltage. Finally, the theoretical analysis and the control method are verified by simulations, experiments, and are tested in the real wind field.

Sampled‐data reliable control for T–S fuzzy semi‐Markovian jump system and its application to single‐link robot arm model

This study investigates the sampled-data reliable control for a class of Takagi-Sugeno (T-S) fuzzy semi-Markovian jump system (FSMJS). The system under consideration involves semi-Markov stochastic process that is described by the property of cumulative distribution function. To relax the difficulty of finding upper bounds in sample-and-hold behaviour of the FSMJS, a novel method named mismatched membership function is presented. Sufficient conditions that can guarantee the T-S FSMJS to be stochastically stable are given. More precisely, the reliable sampled-data controller is designed in terms of linear matrix inequalities. Finally, the validity of the presented approach is demonstrated by a controller design for a single-link robot arm model.

Approaches to T–S Fuzzy-Affine-Model-Based Reliable Output Feedback Control for Nonlinear Itô Stochastic Systems

This paper deals with the problem of reliable and robust $\mathscr {H}_{\infty }$ static output feedback (SOF) controller synthesis for continuous-time nonlinear stochastic systems with actuator faults. The nonlinear stochastic plant is expressed by an Ito-type Takagi–Sugeno fuzzy-affine model with parametric uncertainties, and a Markov process is employed to model the occurrence of actuator fault. The purpose is to design an admissible piecewise SOF controller, such that the resulting closed-loop system is stochastically stable with a prescribed disturbance attenuation level in an $\mathscr {H}_{\infty }$ sense. Specifically, based on a Markovian Lyapunov function combined with Ito differential formula, S-procedure, and some matrix inequality convexification procedures, two new approaches to the reliable SOF controller analysis and synthesis are proposed for the underlying stochastic fuzzy-affine systems. It is shown that the existence of desired reliable controllers is fully characterized in terms of strict linear matrix inequalities. Finally, simulation examples are presented to illustrate the effectiveness and advantages of the developed methods.

Toward Dependable Embedded Model Predictive Control

While model predictive control (MPC) is the industrially preferred method for advanced control in the process industries, it has not found much use in consumer products and safety-critical embedded systems applications in industries such as automotive, aerospace, medical, and robotics. The main barriers are implementability and dependability, where important factors are implementation of advanced numerical optimization algorithms on resource-limited embedded computing platforms and the associated complexity of verification. This challenge comes from a requirement of the use of ultrareliable hardware and software architectures in safety-critical applications, low-cost hardware in consumer products, or both. This paper surveys the state-of-the-art in the emerging field of dependable embedded MPC, and discusses some key challenges related to its design, implementation, and verification. A novel result is the study of a simulator-based performance monitoring and control selection method that monitors and predicts MPC performance and switches to a highly reliable backup controller in cases when the MPC experiences performance issues.

Fault-tolerant sampled-data control of singular networked cascade control systems

ABSTRACTThis paper investigates the reliable sampled-data control problem for a class of singular networked cascade control systems with randomly occurring actuator failures, time-varying delay and disturbances. In particular, the randomly occurring actuator failures describes the phenomenon of the actuator failure appearing in a random way and is modelled by a Bernoulli distributed white noise sequences with a known conditional probability. The main purpose of this work is to design a reliable sampled-data controller such that an optimised upper bound of the predefined performance index is well guaranteed for the systems under consideration even in the presence of both the randomly occurring actuator failures and disturbances. The resulting controllers are reliable in the sense that they will provide guaranteed stochastically admissible and performance index when all actuators are operational as well as when some actuators experiences failure. Finally, two numerical examples are given to exhibit the effectiveness of the proposed control scheme.

T–S Fuzzy-Affine-Model-Based Reliable Output Feedback Control of Nonlinear Systems with Actuator Faults

This article presents a singular system approach to the reliable $$\mathscr {H}_{\infty }$$ static output feedback (SOF) control for continuous-time nonlinear systems with Markovian jumping actuator faults. The approximation of nonlinear plants is carried out in a Takagi–Sugeno fuzzy-affine (FA) environment, and the Markov chain is adopted to characterize the actuator failure phenomenon. Specifically, by utilizing a singular model transformation strategy, the traditional closed-loop system is converted into a singular FA system. With the construction of a mode-dependent Lyapunov function, and invoking S-procedure and some convexifying techniques, the reliable piecewise SOF controller design is then carried out for the underlying systems via a convex program. An illustrative example is finally given to show the efficacy of the developed method.

Finite-time resilient reliable sampled-data control for fuzzy systems with randomly occurring uncertainties

This paper deals with the problem of finite-time passivity-based H∞ resilient reliable state feedback sampled-data control for Takagi–Sugeno fuzzy systems with randomly occurring uncertainties and gain variations, where the randomly occurring characteristics are designed by stochastic variables satisfying the Bernoulli distribution. The main objective of this paper is to design a resilient reliable sampled-data controller such that the resulting closed-loop system is finite-time bounded and satisfies finite-time mixed H∞ and passive performance index γ. Based on the constructed Lyapunov–Krasovskii functional and Wirtinger integral inequality, some delay-dependent sufficient conditions are derived for achieving the required result of the considered systems. Moreover the derived finite-time stability conditions are established in the form of linear matrix inequalities (LMIs) which can be solved by Matlab LMI toolbox. Finally two numerical examples are presented to demonstrate the applicability of the proposed control design technique.

Reliable Output Feedback Control for T-S Fuzzy Systems With Decentralized Event Triggering Communication and Actuator Failures.

Due to the unavailability of full state variables in many control systems, this paper is concerned with the design of reliable observer-based output feedback controller for a class of network-based Takagi-Sugeno fuzzy systems with actuator failures. In order to better allocate network resources under the case that the sensor nodes are physically distributed, the decentralized event triggering communication scheme is adopted such that each sensor node is capable to determine the transmission of its local measurement information independently. Considering that the implementation of the controller may not be synchronized with the plant trajectories due to asynchronous premise variables with such communication mechanism, a novel piecewise fuzzy observer-based output feedback controller is developed. By applying a piecewise Lyapunov function and some techniques on matrix convexification, an approach to the design of observer and controller gain is derived for the augmented closed-loop system to be asymptotically stable with a guaranteed H∞ performance and reduced transmission frequency. Finally, two examples are given to show the effectiveness of the developed method.

Reliable control for hybrid-driven T–S fuzzy systems with actuator faults and probabilistic nonlinear perturbations

This paper is concerned with the reliable controller design for hybrid-driven nonlinear systems via T–S fuzzy model with probabilistic actuator faults and probabilistic nonlinear perturbations. To reduce unnecessary transmissions in the network, a hybrid-driven scheme is introduced, in which the communication transmission scheme can be selected between time-driven scheme and event-driven scheme. The switch law of the communication schemes is modeled as Bernoulli distributed stochastic variable. Considering the network-induced delay, nonlinear perturbations and the probabilistic actuator faults, a new model is constructed under the hybrid driven scheme. By using Lyapunov–Krasovskii functionals and stochastic analysis techniques, sufficient conditions are established which ensure the asymptotical stability of the augmented system under hybrid driven scheme. Furthermore, a unified co-design algorithm for the desired controller and the hybrid-driven scheme are developed. Finally, a typical simulation example is used to demonstrate the validity of obtained results.

Reliable control for bilateral teleoperation systems with actuator faults using fuzzy disturbance observer

In this study, the synchronisation problem of bilateral teleoperation systems with time-delay and actuator faults is considered. First, the error dynamic system with actuator faults is modelled as a time-delay system with disturbance, and a fuzzy disturbance observer (FDO) is designed to estimate the unknown non-linear disturbance. Moreover, an FDO-based robust controller is provided for the master robot and a reliable controller is designed for the slave robot, respectively. Next, a sufficient condition is given to ensure the tracking error; the disturbance observation error and the parameter error are uniformly ultimate bounded. Finally, a numerical example is shown to illustrate the effectiveness of the proposed method.

Data Refining and Transforming via CloudTechnology through Local Desktop

Objectives: To fulfil the FeRAM and Cloud infrastructure for the avoidance of external Storage and for IAAS methods. Methods/Statistical Analysis: In this method, we approach with the Eucalyptus Technique and we use node controller to manipulate the exact performance. For interacting with the Device like FeRAM (Ferro Electric RAM) we used Euca2ools for performance statistics. This can be done from the analysis of Cloud Services with the reliable Cloud Controller. Findings: This model deals about the features that can be adopted in the Cloud computing environment along with the Usage of FeRAM's advantages. Our proposal is to adopt all values inside the FeRAM without the usage of Hard Disk. As the storage controller would be having all the patterns to conclude load balancer which simulates our workload along with the scaling listener, it maps with the additionally stored FeRAM. At this point, we would be integration the mechanism involved in the cloud controller and with the FeRAM as this would add more efficient with our works. In future, there will be a need for avoidance of external storage for storing large data. Applications: In this perspective, a novel optimization model has been proposed in which the cloud architecture is redefined with new RAM (FeRAM) and other external resources. Through Internet Connectivity, we make proper authentication and services with secured password protection manner (OTP) or email verification for every login transitions and thus we propose a model OUT_OF_HARDDRIVE method. The resources are provided to the user in the form of connecting bridge as virtual servers and are possibly distributed, running in cloud environment via Internet.

Synchronization of Lur׳e systems via stochastic reliable sampled-data controller

This paper deals with the problem of synchronization methods for Lur׳e system with stochastic reliable sampled-data control scheme. By constructing the framework of linear matrix inequalities (LMIs) based on Lyapunov method and utilizing Wirtinger-based integral inequality (WBI), Jensen׳s inquality (JI), and other lemmas, synchronization criteria of sampled-data control for Lur׳e systems are derived and compared with the existing works. The necessity and validity of the proposed results are illustrated by two numerical examples.

Robust reliable control of uncertain nonlinear stochastic systems subject to actuator fault

This paper addresses the issue of robust reliable stabilization for a class of uncertain nonlinear stochastic systems with both discrete and distributed time-varying delays and possible occurrence of actuator faults. By constructing a new Lyapunov functional and using linear matrix inequality technique, a new set of sufficient conditions is established for the stochastic stability of the uncertain nonlinear stochastic systems. Then, sufficient conditions are obtained for the solvability of the robust stabilization problem via robust reliable controller. More precisely, the derived control law guarantees the robust stabilization of nonlinear stochastic systems in the presence of known actuator failure matrix and uncertainties. Further, the results are extended to study the stabilization of stochastic systems with unknown actuator failure matrix. Moreover, the obtained criteria are formulated in terms of LMIs and also the reliable controller can be designed in terms of the solutions to certain linear matrix inequalities. Finally, numerical examples with simulation result are presented to demonstrate the validity and less conservatism of the obtained results.

The Performance of Structure-Controller Coupled Systems Analysis Using Probabilistic Evaluation and Identification Model Approach

This study evaluates the performance of passively controlled steel frame building under dynamic loads using time series analysis. A novel application is utilized for the time and frequency domains evaluation to analyze the behavior of controlling systems. In addition, the autoregressive moving average (ARMA) neural networks are employed to identify the performance of the controller system. Three passive vibration control devices are utilized in this study, namely, tuned mass damper (TMD), tuned liquid damper (TLD), and tuned liquid column damper (TLCD). The results show that the TMD control system is a more reliable controller than TLD and TLCD systems in terms of vibration mitigation. The probabilistic evaluation and identification model showed that the probability analysis and ARMA neural network model are suitable to evaluate and predict the response of coupled building-controller systems.

Notice of Violation of IEEE Publication Principles: Reliable Output Feedback Control of Discrete-Time Fuzzy Affine Systems With Actuator Faults

Notice of Violation of IEEE Publication Principles <br><br>After careful consideration by a duly constituted committee, an author of this article, Hamid Reza Karimi, was found to have acted in violation of the IEEE Principles of Ethical Publishing by artificially inflating the number of citations to this article. <br/> This article studies the robust and reliable H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> static output feedback (SOF) control for nonlinear systems with actuator faults in a descriptor system framework. The nonlinear plant is characterized by a discrete-time Takagi-Sugeno (T-S) fuzzy affine model with parameter uncertainties, and the Markov chain is utilized to describe the actuator-fault behaviors. Specifically, by adopting a state-output augmentation approach, the original system is firstly reformulated into the descriptor fuzzy affine system. Based upon a novel piecewise Markovian Lyapunov function (LF), the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance analysis condition for the underlying system is then presented, and furthermore the robust and reliable SOF controller synthesis is carried out. It is shown that by invoking the redundancy properties induced by the descriptor formulation, combined with some convexifying techniques, the existence of the desired reliable controller can be explicitly determined by the solution of a convex optimization problem. Finally, simulation studies are applied to confirm the effectiveness of the developed method.