Showing papers in "Asian Journal of Control in 2015"
TL;DR: In this paper, the problem of quantized filtering for a class of continuous-time Markovian jump linear systems with deficient mode information is investigated, where the measurement output of the plant is quantized by a mode-dependent logarithmic quantizer, and the defect in the Markov stochastic process simultaneously considers the exactly known, partially unknown, and uncertain transition rates.
Abstract: This paper investigates the problem of quantized filtering for a class of continuous-time Markovian jump linear systems with deficient mode information. The measurement output of the plant is quantized by a mode-dependent logarithmic quantizer, and the deficient mode information in the Markov stochastic process simultaneously considers the exactly known, partially unknown, and uncertain transition rates. By fully exploiting the properties of transition rate matrices, together with the convexification of uncertain domains, a new sufficient condition for quantized performance analysis is first derived, and then two approaches, namely, the convex linearization approach and iterative approach, to the filter synthesis are developed. It is shown that both the full-order and reduced-order filters can be obtained by solving a set of linear matrix inequalities (LMIs) or bilinear matrix inequalities (BMIs). Finally, two illustrative examples are given to show the effectiveness and less conservatism of the proposed design methods.
140 citations
TL;DR: Nine research articles are selected from high-quality results presented at the conference, and they represent the latest developments in distributed and networked control systems (NCSs) to provide potential readers with a broader perspective of this currently hot research topic.
Abstract: received worldwide, and 353 papers were accepted for presentation at the invited, regular, and interactive sessions. Among these papers, we have selected nine research articles for this special issue from high-quality results presented at the conference, and they represent the latest developments in distributed and networked control systems (NCSs). The papers are consolidated to provide potential readers with a broader perspective of this currently hot research topic, as well as a comprehensive background of the state-of-the-art approaches for designing distributed and NCSs. Recent advancements in communication technologies , distributed, and NCSs have resulted in a wide range of realistic engineering applications, including environmental monitoring, traffic control, telerobotic systems , smart grids, and even space systems. Typically, these systems comprise spatially distributed plants, actu-ators, sensors, and controllers, connected together via a communication network or bus. Connection of distributed subsystems allows for sensory signals to be shared efficiently; eliminating unnecessary wiring while providing an effective means to add new sensors, actuators, and controllers in an ad-hoc fashion with very little cost and structural changes to the overall architecture. However, control of these complex systems is highly challenging due to the huge volume of data, strong het-erogeneity in different subsystems, and imperfection in the communication network. Estimating global states and achieving global objectives using localized sensing, signal processing, and control, both homogenous and heterogeneous , are also amongst the main challenges. In essence, a distributed control system is a connection of control subsystems via a communication network, where each subsystem is regulated by one or more local controllers. To ensure overall system stability, the Lyapunov function approach is widely used to drive the subsystem states to zero according to selected non-negative potential functions [1]. For optimal control, minimization of convergence speed [2] and minimization of the sum of convex cost functions for individual subsystems [3] were also studied. The distributed model predictive control of polytopic uncertain subsystems was considered in [4], and based on an adaptive protocol, the multi-agent consensus problem considering high-order nonlinear dynamics modeled using neural networks was addressed in [5]. Stability of NCSs is usually studied considering communication issues, such as time delays [6] and packet drops [7] due to long transmission distances and network congestion, and was handled using multirate sampling [8] and quantization [9]. Competition amongst nodes is also inevitable as the communication network can be shared by other control loops or data communication tasks [10]. To date, …
99 citations
TL;DR: In this article, the authors investigated the event-triggered control of linear systems with saturated state feedback and saturated observer-based feedback, respectively, and showed that the problem of simultaneously deriving stabilizing event-tiggered controllers and tackling saturation nonlinearity is cast into a standard linear matrix inequalities problem.
Abstract: This paper investigates the event-triggered control of linear systems with saturated state feedback and saturated observer-based feedback, respectively. The problem of simultaneously deriving stabilizing event-triggered controllers and tackling saturation nonlinearity is cast into a standard linear matrix inequalities problem. Key topics are studied, such as event-triggered observer design and event-triggered saturated observer-based feedback synthesis. Important issues are touched on, including the existence of the positive lower bound for inter-event times, and self-triggered algorithms.
72 citations
TL;DR: The present paper makes a break with past deterministic methods, and considers the swarm as a statistical ensemble for which guidance can be performed from a probabilistic point of view, leading to computationally tractable and implementable swarm guidance laws.
Abstract: Motivated by biological swarms occurring in nature, there is recent interest in developing swarms comprised completely of engineered agents. The main challenge for developing swarm guidance laws compared to earlier formation flying and multi-vehicle coordination approaches is the sheer number of agents involved. While formation flying applications might involve up to 10 to 20 agents, swarms are desired to contain hundreds to many thousands of agents. In order to deal with the sheer size, the present paper makes a break with past deterministic methods, and considers the swarm as a statistical ensemble for which guidance can be performed from a probabilistic point of view. The probability-based approach takes advantage of the law of large numbers, and leads to computationally tractable and implementable swarm guidance laws. Agents following a probabilistic guidance algorithm make statistically independent probabilistic decisions based solely on their own state, which ultimately guides the swarm to the desired density distribution in the configuration space. Two different synthesis methods are introduced for designing probabilistic guidance laws. The first is based on the Metropolis-Hastings (M-H) algorithm, and the second is based on using linear matrix inequalities (LMIs). The M-H approach ensures convergent swarm behavior subject to enforced desired motion constraints, while the LMI approach additionally ensures exponential convergence with a prescribed decay rate, and allows minimization of a cost function that reflects fuel expenditure. In addition, both algorithms endow the swarm with the property of self-repair, and the capability to strictly enforce zero-probability keep-out regions. This last property requires a slight generalization of the Perron-Frobenius theory, and can be very useful in swarm applications that contain regions where no agents are allowed to go. Simulation examples are given to illustrate the methods and demonstrate desired properties of the guided swarm.
62 citations
TL;DR: In this paper, the problem of non-fragile synchronization control for Markovian jumping complex dynamical networks with probabilistic time-varying coupling delays is studied.
Abstract: This paper studies the problem of non-fragile synchronization control for Markovian jumping complex dynamical networks with probabilistic time-varying coupling delays. By constructing a new Lyapunov–Krasovskii functional (LKF) and combining the reciprocal convex technique, sufficient conditions for the complex dynamical networks to be globally asymptotically synchronized in the mean square sense are derived. The probability distribution of the delays have been proposed and delay probability-distribution-dependent conditions are derived in the form of linear matrix inequalities (LMIs). The derived conditions depend not only on the size of the delay but also on the probability of the delay taking values in some intervals. Further, a non-fragile synchronization controller is proposed. Finally, a numerical example is given to demonstrate the effectiveness of the proposed methods.
62 citations
TL;DR: In this paper, the shifted Legendre orthogonal polynomials are used for numerical solution of a new formulation for the multi-dimensional fractional optimal control problem (M-DFOCP) with a quadratic performance index.
Abstract: The shifted Legendre orthogonal polynomials are used for the numerical solution of a new formulation for the multi-dimensional fractional optimal control problem (M-DFOCP) with a quadratic performance index. The fractional derivatives are described in the Caputo sense. The Lagrange multiplier method for the constrained extremum and the operational matrix of fractional integrals are used together with the help of the properties of the shifted Legendre orthonormal polynomials. The method reduces the M-DFOCP to a simpler problem that consists of solving a system of algebraic equations. For confirming the efficiency and accuracy of the proposed scheme, some test problems are implemented with their approximate solutions.
58 citations
TL;DR: In this article, a nonlinear robust control strategy was proposed to solve the path tracking problem for a quadrotor unmanned aerial vehicle (UAV) in the presence of sustained external disturbances affecting the six degrees of freedom, parametric uncertainties and unmodeled dynamics.
Abstract: This paper presents a nonlinear robust control strategy to solve the path tracking problem for a quadrotor unmanned aerial vehicle. The main objective is to design controllers that provide certain required performances during the quadrotor flight, such as null tracking error and robustness in the presence of sustained external disturbances affecting the six degrees of freedom, parametric uncertainties, and unmodeled dynamics. The control structure is performed through a nonlinear H∞ controller to stabilize the rotational movements and a control law based on the backstepping approach with integral action to track the reference trajectory. Simulation results are carried out to corroborate the effectiveness and the robustness of the proposed strategy.
49 citations
TL;DR: This paper investigates the event-triggered consensus problem of second-order multi-agent systems, where data sampling and communication are executed once an event condition is satisfied, and proposes two schemes.
Abstract: This paper investigates the event-triggered consensus problem of second-order multi-agent systems, where data sampling and communication are executed once an event condition is satisfied. A centralized event-triggered control scheme first is established with a state-dependent event condition relying on global state information. Then, in the decentralized counterpart, an event condition for each agent is designed with respect to its own state and the last sampled data of itself and its neighbors. Under the two schemes proposed, consensus is reached with enlarged average sampling periods and no Zeno behavior. Simulations validate the effectiveness of the theoretical results.
47 citations
TL;DR: In this paper, a tensor product transformation based nonlinear feedback controller with additional neural network based friction compensator is proposed for 3D tower crane, which consists of the tensor products transformation and a linear matrix inequalities utilizing a parameter varying Lyapunov function.
Abstract: Fast and accurate positioning and swing minimization of heavy loads in crane manipulation are demanding and, in the same time, conflicting tasks. For accurate positioning, the main problem is nonlinear friction effect, especially in the low speed region. In this paper authors propose a control scheme for 3D tower crane, that consists of the tensor product transformation based nonlinear feedback controller, with additional neural network based friction compensator. Tensor product based controller is designed using linear matrix inequalities utilizing a parameter varying Lyapunov function. Neural network parameters adaptation law is derived using Lyapunov stability analysis. The simulation and experimental results on 3D laboratory crane model are given.
42 citations
TL;DR: In this article, a continuous adaptive state-feedback controller for a class of uncertain high-order nonlinear systems with time delays is proposed, under somewhat necessary restrictions on the system nonlinearities, by the method of adding a power integrator and the related adaptive technique.
Abstract: This paper is concerned with adaptive stabilization for a class of uncertain high-order nonlinear systems with time delays. To the authors' knowledge, there has been no analogous result. Hence during investigation, the conditions on delay effect and the control design framework should be established for the first time. In this paper, under somewhat necessary restrictions on the system nonlinearities, by the method of adding a power integrator and the related adaptive technique, a procedure is developed to design the continuous adaptive state-feedback controller without overparametrization. Moreover, the uniform stability and convergence of the resulting closed-loop system are rigorously proven, with the aid of a suitable Lyapunov-Krasovskii functional. Finally, a numerical example is provided to illustrate the effectiveness of the theoretical result.
40 citations
TL;DR: In this paper, an iterative learning control (ILC) based automatic train operation is proposed to address a high speed train (HST) tracking problem with consideration of the iteration-varying operation condition.
Abstract: The iterative learning control (ILC)-based automatic train operation is proposed to address a high speed train (HST) tracking problem with consideration of the iteration-varying operation condition. The iteration-varying operation condition considered in this paper is the air resistance coefficient of an HST, which may be completely different at two consecutive operation processes due to different weather conditions. In addition, to alleviate the effect of measurement noise, the proposed method is modified further. The effectiveness of these two proposed methods are verified by theoretical analysis and numerical simulation.
TL;DR: In this article, a sliding mode observer (SMO) technique is used to estimate the actuator and sensor faults for Lipschitz nonlinear systems with unstructured uncertainties.
Abstract: This paper proposes a new scheme for estimating the actuator and sensor fault for Lipschitz nonlinear systems with unstructured uncertainties using the sliding mode observer (SMO) technique. Initially, a coordinate transformation is introduced to transform the original state vector into two parts such that the actuator faults only appear in the dynamics of the second state vector. The concept of equivalent output error injection is then employed to estimate the actuator fault. The effects of system uncertainties on the estimation errors of states and faults are minimized by integrating an uncertainty attenuation level into the observer. The sufficient conditions for the state estimation error to be bounded and satisfy a prescribed performance are derived and expressed as a linear matrix inequality (LMI) optimization problem. Furthermore, the proposed actuator fault estimation method is extended to sensor fault estimation. Finally, the effectiveness of the proposed scheme in estimating actuator and sensor faults has been illustrated considering an example of a single-link flexible joint robot system.
TL;DR: In this paper, a novel image-based visual servoing (IBVS) controller based on quasi-min-max model predictive control (MPC) is presented, where the image Jacobian matrix is transformed into a convex combination of linear time-invariant vertices form with the tensor-product (TP) model transformation method.
Abstract: This paper presents a novel image-based visual servoing (IBVS) controller based on quasi-min-max model predictive control (MPC). By transforming the image Jacobian matrix (i.e. interaction matrix) into a convex combination of linear time-invariant vertices form with the tensor-product (TP) model transformation method, the visual servoing system is represented as a polytopic linear parameter-varying (LPV) system. A robust controller is designed for the robotic visual servoing system subject to input and output constraints such as robot physical limitations and visibility constraints. The control signal is calculated online by carrying out the convex optimization involving linear matrix inequalities (LMIs) in model predictive control. The proposed visual servoing method avoids the inverse of the image Jacobian matrix and hence can solve the intractable problems for the classical IBVS controller, such as large displacements between the initial and the desired position of the camera. The ability of handling constraints can keep the image features in the boundary of the desired field of view (FOV). To verify the effectiveness of the proposed algorithm, the simulation results on a 6 degrees-of-freedom (DOF) robot manipulator with eye-in-hand configuration are presented and discussed.
TL;DR: In this paper, a single-integrator agent model is proposed for multi-agent formation maneuvering and target interception problems, where the target velocity is unknown and the control laws are only a function of the relative position of agents in an infinitesimally and minimally rigid graph.
Abstract: In this paper, we introduce control laws for multi-agent formation maneuvering and target interception problems. In the target interception problem, we consider that the target velocity is unknown. Using a single-integrator agent model, the proposed controls consist of a formation acquisition term, dependent on the graph rigidity matrix, and a formation maneuvering or target interception term. The control laws are only a function of the relative position of agents in an infinitesimally and minimally rigid graph, and either the desired maneuvering velocity of the formation or the target's relative position to the leader. The target interception control includes a continuous dynamic estimation term to identify the unknown target velocity. A Lyapunov-like stability analysis is used to prove that the control objectives are met.
TL;DR: The combination of tensor product (TP)‐based model transformation and of fuzzy control as a cost effective cascade control system (CS) structure is proposed and the efficiency of the cascade CS structure is proved by real‐time experimental results for a laboratory three tank system.
Abstract: This paper proposes the combination of tensor product (TP)-based model transformation and of fuzzy control as a cost effective cascade control system (CS) structure. A novel two-step design approach is suggested. A parallel distributed compensation technique is used in the first step in the TP-based design of the inner state feedback control loops which is next characterized by simple models using equivalent state feedback matrices. The modulus optimum method and the modal equivalent principle are used in the second step to design the Mamdani PI-fuzzy controller in the outer control loops. The cost effective feature of the original cascade CS combination concerns the simplicity of the CS structure, of the controllers and of the design approach. The presentation is focused on the level control in horizontal three tank systems where two cascade CS structures are separately designed to control two water levels. The efficiency of the cascade CS structure is proved by real-time experimental results for a laboratory three tank system. Comparisons with the Mamdani PI-fuzzy CS and with the cascade combination of TP and PI CS are included.
TL;DR: The generalized TP model transformation is introduced, which is a tractable, non‐heuristic numerical tool that can be executed on sets of functions and is capable of readily manipulating the resulting polytopic representation for further design requirements.
Abstract: The main motivation of the TP model transformation is that modern identification and control analysis, along with design methodologies, are based on various kinds of different representations that have different benefits and drawbacks in terms of identification and modeling effort and structure, however, the link between these representations is in many cases difficult to establish, especially if the model components are not given by closed formulae but rather by various soft-computing-based or black box models. This paper shows that the TP model transformation can serve as a gateway between different representations by bridging to a widely adopted polytopic representation. Further, it is capable of readily manipulating the resulting polytopic representation for further design requirements, which in many cases has a strong effect on the resulting control performance and the conservativeness of the solution. The paper discusses how the TP model transformation can be used as a final step of modeling and, at the same time, as a preprocessing step in polytopic model based design approaches. The paper introduces the generalized TP model transformation, which is a tractable, non-heuristic numerical tool that can be executed on sets of functions. Different manipulation techniques are investigated to show the benefits of the generalized TP model transformation. Finally, a stability verification technique is proposed for cases where system components are available in different representations. © 2015 Chinese Automatic Control Society and Wiley Publishing Asia Pty Ltd.
TL;DR: In this article, a tensor product (TP) model transformation-based controller design for control and synchronization of a class of the fractional-order chaotic systems is investigated, and a novel linear matrix inequality (LMI)-based stabilization condition for fractionalorder TP models with a controller derived via a parallel distributed compensation structure is proposed.
Abstract: Fractional-order chaotic systems are the complex systems that involve non-integer order derivatives. In this paper, tensor product (TP) model transformation-based controller design for control and synchronization of a class of the fractional-order chaotic systems is investigated. We propose a novel linear matrix inequality (LMI)-based stabilization condition for fractional-order TP models with a controller derived via a parallel distributed compensation (PDC) structure. In the controller design, the controlled system first is transformed into a convex state-space TP model using the TP model transformation. Based on the transformed TP model, the controller is determined by solving the proposed LMI condition. To the best of our knowledge, this is the first investigation of TP model transformation based design in fractional-order systems. Several illustrative examples are given to demonstrate the convenience of the proposed LMI condition and the effectiveness of the controller design.
TL;DR: This work provides conditions for mean exponential stability of the networked closed loop in terms of matrix inequalities, both for investigating the stability of given protocols, such as static round‐robin protocols and dynamic maximum error first‐try once discard protocols, and conditions to design new dynamic protocols.
Abstract: We consider networked control systems in which sensors, controllers, and actuators communicate through a shared network that introduces stochastic intervals between transmissions, delays, and packet drops. Access to the communication medium is mediated by a protocol that determines which node one of the sensors, one of the actuators, or the controller is allowed to transmit a message at each sampling/actuator-update time. We provide conditions for mean exponential stability of the networked closed loop in terms of matrix inequalities, both for investigating the stability of given protocols, such as static round-robin protocols and dynamic maximum error first-try once discard protocols, and conditions to design new dynamic protocols. The main result entailed by these conditions is that, if the networked closed loop is stable for a static protocol, then we can provide a dynamic protocol for which the networked closed loop is also stable. The stability conditions also allow for obtaining an observer-protocol pair that reconstructs the state of a linear time invariant plant in a mean exponential sense and for less conservative stability results than other conditions previously appearing in the literature.
TL;DR: An optimal terminal iterative learning control (TILC) approach is presented, where the stopping position and initial braking speed are chosen as the terminal system output and the control input, respectively.
Abstract: Thetrainstopcontrolisatypicalset-pointcontroltask,whereonlythefinalstate( ie ,theterminaltrainstopposition)isofconcernandspecifiedForsuchacontrolproblem,anoptimalterminaliterativelearningcontrol(TILC)approachispresentedinthispaper,wherethestoppingpositionandinitialbrakingspeedarechosenastheterminalsystemoutputandthecontrolinput,respectivelyThecontrollerdesignonlydependsonthemeasuredinput/output(I/O)datawithoutrequiringanymodelinginformationofthetrainoperationsystem,andthelearninggainisupdatedbythesystemI/OdataiterativelytoaccommodatethesystemuncertaintiesThemonotonicconvergenceoftheterminaltrackingerrorisguaranteedbyrigorousmathematicalanalysisExtensivesimulationresultsareprovidedtoshowtheapplicabilityandeffectivenessoftheproposedapproach Key Words: Automatictrainstopsystem,automatictraincontrol,optimalterminaliterativelearningcontrol I INTRODUCTION Thetrainstationstopcontrolisabasicfunctionoftheautomatictrainoperation(ATO)system,andthreeperformanceindices,includingthestationstoppositionerror,theenergyconsumption,andthevariationrateofthebrakingforce,shouldbeconsideredfortheautomatictrain stop system [1] Accurate stopping will facilitatepassengersgettingonoroffatrain,anditisespeciallyimportantonmetrolinesForthetraditionalautomatictrain operation [2], the train stop control is regardedasaspeedcontrolofatrainInmoredetail,adesiredtrain speed must be designed first, based on the New-tonkineticsmodel,usingcurrentspeed,brakingdistance,trackparameters,andtotalmassofatrain
TL;DR: In this article, an observer-based active disturbance rejection controller is proposed for robust trajectory tracking for a parallel robot, which is based on purely linear disturbance observation and linear feedback control techniques modulo nonlinear input gain injections and cancellations.
Abstract: In this article, the problem of robust trajectory tracking, for a parallel robot is tackled via an observer-based active disturbance rejection controller. The proposed design method is based on purely linear disturbance observation and linear feedback control techniques modulo nonlinear input gain injections and cancellations. The estimations are carried out through Generalized Proportional Integral (GPI) observers, endowed with output integral injection to ease the presence of possible zero mean measurement noise effects. As the lumped (both exogenous and endogenous) disturbance inputs are estimated, they are being used in the linear controllers for on-line disturbance cancellation, while the phase variables are being estimated by the same GPI observer. The estimations of the phase variables are used to complete a linear multivariable output feedback controller. The proposed control scheme does not need the exact knowledge of the system, which is a good alternative to classic control schemes such as computed torque method, reducing the computation time. The estimation and control method is approximate, ensuring small as desired reconstruction and tracking errors. The reported results, including laboratory experiments, are better than the results provided by the classical model-based techniques, shown to be better when the system is subject to endogenous and exogenous uncertainties.
TL;DR: In this paper, the authors introduce a systematic modelling and control design schema enabling a gateway between the different delayed system representations and the tensor product (TP) type convex polytopic models which allows the direct use of linear matrix inequality (LMI)-based controller and observer synthesis.
Abstract: Varying time delays are inherent and unavoidable properties of a large set of control systems causing stability issues in most cases. Control design methods that guarantee the stability, such as those based on the Lyapunov–Krasovskii functional, are mathematically highly complicated and therefore have hardly spread into everyday engineering practice. This paper introduces a systematic modelling and control design schema enabling a gateway between the different delayed system representations and the tensor product (TP) type convex polytopic models which allows the direct use of linear matrix inequality (LMI)-based controller and observer synthesis. The approach presented assumes known, bounded, time-varying internal time-delays where the delay derivative is not considered. The proposed modelling scheme is demonstrated via a numerical example.
TL;DR: In this article, an additional control force of soft obstacles is introduced to protect the UAV from local minima of additional control forces, and the optimal solution to the path planning problem, in terms of the additional controller force method and the computational efficiency according to the receding horizon control, both contribute to the proposed algorithm.
Abstract: This paper deals with a UAV path planning problem in the environment where both solid obstacles and soft obstacles exist. The artificial potential field approach is updated by introducing an additional control force and integrating it with the concept of receding horizon control for UAV trajectory optimization. The original problem is converted into a multi-objective optimization problem by regarding the involved additional control term as the optimization variable. Seeing as the establishment of an additional control force of soft obstacles is dependent on the probability of certain specifications such as survivability, the additional control term accomplishes the description of the specified property index better than those that have been considered in the past, such as distance and control energy. The optimal solution to the path planning problem, in terms of the additional control force method, and the computational efficiency according to the receding horizon control, both contribute to the proposed algorithm. Meanwhile, the proposed method is able to protect the UAV from local minima of additional control force. The numerical examples verify the advantages of this approach.
TL;DR: Using convergence theory, matrix theory and the method of Lyapunov functions, a sufficient condition for the networked multi-agent systems to achieve multi-consensus is derived and the agreement of each group is studied.
Abstract: In this paper, a multi-consensus problem for networked multi-agent systems in a weighted and directed graph with a nonlinear protocol is investigated. By using convergence theory, matrix theory and the method of Lyapunov functions, a sufficient condition for the networked multi-agent systems to achieve multi-consensus is derived. And the agreement of each group is studied. A feedback controller for the networked multi-agent systems is then designed to drive the system to achieve multi-consensus. Numerical simulations illustrate the effectiveness of the theoretical results.
TL;DR: In this paper, a robust model predictive control (RMPC) method for a linear parameter varying (LPV) system that has both probabilistic uncertain and time-varying parameters is considered to be measured online.
Abstract: This paper considers robust model predictive control (RMPC) methods for a linear parameter varying (LPV) system that has both probabilistic uncertain and time-varying parameters. The parameters are considered to be measured online. In this regard, the aircraft landing gear system is considered as an LPV system whose parameters variation can affect both stability and performance. By transforming this system into a convex combination of linear time-invariant vertices form with the tensor-product (TP) model transformation method, the landing gear system is represented as a polytopic linear parameter-varying system. A computationally efficient RMPC control signal law is calculated online by carrying out the convex optimization involving linear matrix inequalities (LMIs) in MPC which leads to finding the solutions that can guarantee the closed-loop robust stability and performance. The proposed controller can effectively suppress the shimmy vibration of the landing gear with variable taxiing velocity and wheel caster length, also with the probabilistic uncertain torsional spring stiffness.
TL;DR: In this article, the authors proposed an algorithm to extract a desired emptied strict minimal siphon SMS from a given emptied siphon based on loop resource subsets, which can obtain a maximally permissive liveness-enforcing supervisor with reduced structural complexity and no weighted monitors.
Abstract: This paper deals with the problems of computational and structural complexity in designing maximally permissive liveness-enforcing supervisors for a class of Petri nets called Systems of Simple Sequential Processes with Resources S3PR without i¾?-resources. The supervisor consists of two parts: the first part proposes an algorithm to extract a desired emptied strict minimal siphon SMS from a given emptied siphon based on loop resource subsets. This is faster than the existing ones. The second part proposes a siphon-based deadlock prevention policy, which can obtain a maximally permissive liveness-enforcing supervisor with reduced structural complexity and no weighted monitors, owing to the contribution of the first part, which can compute a desired SMS such that one with the smallest number of resource places is selected first for control. Several flexible manufacturing systems are used to show the proposed method and its superior performance over the previous ones.
TL;DR: In this paper, the authors presented the design and experimental implementation of a single-input single-output (SISO) model predictive control (MPC) scheme with a vibration compensator which is based on an identified model of the piezoelectric tube scanner (PTS).
Abstract: Nanotechnology is an area of modern science which deals with the control of matter at dimensions of 100 nm or less. In recent years, of all the available microscopy techniques, atomic force microscopy (AFM) has proven to be extremely versatile as an investigative tool in this field. However the performance of AFM is significantly limited by the effects of hysteresis, creep, cross-coupling, and vibration in its scanning unit, the piezoelectric tube scanner (PTS). This article presents the design and experimental implementation of a single-input single-output (SISO) model predictive control (MPC) scheme with a vibration compensator which is based on an identified model of the PTS. The proposed controller provides an AFM with the capability to achieve improved tracking and results in compensation of the nonlinear effects. The experimental results, which compare the tracking performances of the proposed controller for different reference signals, highlight the usefulness of the proposed control scheme.
TL;DR: Using this analysis, controller parameters can be obtained once the ultimate band is chosen and it is shown that on choosing the least ultimate band, the control becomes non‐switching and Gao's reaches law becomes identical to Utkin's reaching law.
Abstract: The paper presents a generalized understanding on the dynamics of the sliding variable for discrete time sliding mode control systems via a fresh approach involving several bands in the space of the sliding variable. Using this analysis, controller parameters can be obtained once the ultimate band is chosen. It is shown that on choosing the least ultimate band, the control becomes non-switching and Gao's reaching law becomes identical to Utkin's reaching law. On other occasions, the ultimate band obtained using this approach is clearly less than that obtained in previous works using Gao's reaching law. The analysis is presented for both state feedback and multirate output feedback cases.
TL;DR: This paper discusses different strategies that could be used on the sampling step of the TP model transformation (which in turn lead to different membership function properties of a TS fuzzy model), and discusses how the other steps can be used to reduce the number of rules of a given TS fuzzy models.
Abstract: The tensor-product (TP) model transformation is a numerical technique that finds a convex representation, akin to a Takagi-Sugeno (TS) fuzzy model, from a given linear parameter varying (LPV) model of a system. It samples the LPV model over a limited domain, which allows the use of the higher order singular value decomposition (HOSVD) and convex transformations that leads to the TS representation of the LPV model. In this paper, we discuss different strategies that could be used on the sampling step of the TP model transformation (which in turn lead to different membership function properties of a TS fuzzy model). Additionally, this paper discusses how the other steps could be used to reduce the number of rules of a given TS fuzzy model. In cases where nonzero singular values were discarded in the rule reduction, we also show how to obtain an uncertain model that covers the original.
TL;DR: In this article, the authors present a complete control design approach based on quasi-linear parameter varying (qLPV) modelling and linear matrix inequality (LMI)-based synthesis utilizing the tensor product (TP) model transformation to determine the convex polytopic representation of the parameter dependent nonlinear system.
Abstract: Vibration actuators are widely used in handheld devices to provide vibrotactile feedback or silent notification to the users. In most cases, miniature DC motors with eccentric rotors or the so-called coin-type shaftless vibration motors are utilized. The common disadvantage of the single rotor designs is that the frequency and the intensity of the generated vibration cannot be adjusted separately. The construction composed of two independently driven coaxial eccentric rotors – which makes a strongly coupled nonlinear system – allows for the separate control of the frequency and amplitude by the adjustment of the angular speed and the total eccentricity. This paper presents a complete control design approach based on quasi-linear parameter varying (qLPV) modelling and linear matrix inequality (LMI)-based synthesis utilizing the tensor product (TP) model transformation to determine the convex polytopic representation of the parameter dependent nonlinear system. The design approach is demonstrated via a concrete numerical example using the parameters of a real dual-excenter prototype device. The control performance is validated through numerical simulations. This case study goes through a complete nonlinear control problem from the modelling phase to the implementation-ready controller, while drawing generalizable conclusions for qLPV modelling.