Showing papers by "Hamid Reza Karimi published in 2020"

Takagi–Sugeno Model Based Event-Triggered Fuzzy Sliding-Mode Control of Networked Control Systems With Semi-Markovian Switchings

Abstract: This paper is focused on the event-triggered fuzzy sliding-mode control of networked control systems regulated by semi-Markov process. First, through movement-decomposition method, the networked control system is transformed into two lower-order subsystems. Then, an event-triggered scheme based on a delay system model approach is proposed in designing the switching surface and obtaining the sliding mode dynamics. Furthermore, a fuzzy sliding-mode controller is developed to realize reachability of a predefined switching surface and desirable sliding motion. Moreover, in terms of linear matrix inequality method, sufficient conditions for stochastic stability of the obtained sliding mode dynamics is developed in the sense of generally uncertain transition rates. Finally, the applicability of the proposed results are verified numerically on the single-link robot arm system.

Quantized Nonstationary Filtering of Networked Markov Switching RSNSs: A Multiple Hierarchical Structure Strategy

Abstract: This article addresses the quantized nonstationary filtering problem for networked Markov switching repeated scalar nonlinear systems (MSRSNSs). A more general issue is explored for MSRSNSs, where the measurement outputs are characterized by packet losses, nonstationary quantized output, and randomly occurred sensor nonlinearities (ROSNs) simultaneously. Note that both packet losses and ROSNSs are depicted by Bernoulli distributed sequences. By utilizing a multiple hierarchical structure strategy, the nonstationary filters are designed for MSRSNSs, in which the correlation among modes of systems, quantizer, and controller are presented in terms of nonstationary Markov process. A practical example is provided to verify the proposed theoretical results.

$H_\infty$ Refined Antidisturbance Control of Switched LPV Systems With Application to Aero-Engine

Abstract: In this paper, a parameter-dependent multiple discontinuous Lyapunov function (PMDLF) approach is proposed to study the $H_\infty$ refined antidisturbance control problem of switched linear parameter-varying systems. The $H_\infty$ refined antidisturbance means the disturbance appearing in the control channel can be accurately compensated by means of the estimation of the disturbance and the energy bounded external disturbance can be restrained. A key point is to set up a PMDLF framework that provides an effective tool for attenuating the energy bounded disturbances and rejecting the disturbances generated by the exosystem accurately. A parameter-driven and dwell time-dependent switching law is designed, and a solvability condition ensuring the $H_\infty$ refined antidisturbance performance is developed. Then, the $H_\infty$ refined antidisturbance switched parameter-dependent disturbance observers and the disturbance observer-based refined controllers are established to achieve required disturbance attenuation and rejection. Finally, an example of an aero-engine control system is given to verify the availability of the acquired approaches.

Sliding Mode Control for Nonlinear Stochastic Singular Semi-Markov Jump Systems

Abstract: This paper deals with the problem of sliding mode control design for nonlinear stochastic singular semi-Markov jump systems (S-MJSs). Stochastic disturbance is first considered in studying S-MJSs with a stochastic semi-Markov process related to Weibull distribution. The specific information including the bound of nonlinearity is known for the control design. Our attention is to design sliding mode control law to attenuate the influences of uncertainty and nonlinear term. First, by the use of the Lyapunov function, a set of sufficient conditions are developed such that the closed-loop sliding mode dynamics are stochastically admissible. Then, the sliding mode control law is proposed to ensure the reachability in a finite-time region. Finally, the practical system about dc motor model is given to verify the validity of the proposed method.

Adaptive Control of Nonlinear Semi-Markovian Jump T–S Fuzzy Systems With Immeasurable Premise Variables via Sliding Mode Observer

Abstract: The issue of observer-based adaptive sliding mode control of nonlinear Takagi–Sugeno fuzzy systems with semi-Markov switching and immeasurable premise variables is investigated. More general nonlinear systems are described in the model since the selections of premise variables are the states of the system. First, a novel integral sliding surface function is proposed on the observer space, then the sliding mode dynamics and error dynamics are obtained in accordance with estimated premise variables. Second, sufficient conditions for stochastic stability with an ${H}_{\infty }$ performance disturbance attenuation level ${\gamma }$ of the sliding mode dynamics with different input matrices are obtained based on generally uncertain transition rates. Third, an observer-based adaptive controller is synthesized to ensure the finite time reachability of a predefined sliding surface. Finally, the single-link robot arm model is provided to verify the control scheme numerically.

Fuzzy Output Tracking Control and Filtering for Nonlinear Discrete-Time Descriptor Systems Under Unreliable Communication Links

Abstract: In this paper, the problems of output tracking control and filtering are investigated for Takagi–Sugeno fuzzy-approximation-based nonlinear descriptor systems in the discrete-time domain. Especially, the unreliability of the communication links between the sensor and actuator/filter is taken into account, and the phenomenon of packet dropouts is characterized by a binary Markov chain with uncertain transition probabilities, which may reflect the reality more accurately than the existing description processes. A novel bounded real lemma (BRL), which ensures the stochastic admissibility with $H_{\infty } $ performance for fuzzy discrete-time descriptor systems despite the uncertain Markov packet dropouts, is presented based on a fuzzy basis-dependent Lyapunov function. By resorting to the dual conditions of the obtained BRL, a solution for the designed fuzzy output tracking controller is given. A design method for the full-order fuzzy filter is also provided. Finally, two examples are finally adopted to show the applicability of the achieved design strategies.

An Incremental Learning Framework for Human-like Redundancy Optimization of Anthropomorphic Manipulators

Abstract: Recently, the human-like behavior on anthropomorphic robot manipulators are increasingly accomplished by the kinematic model estabilshing the relationship of an anthropomorphic manipulator and human arm motions. Notably, the growth and broad availability of advanced techniques in data science facilitate the imitation learning process in anthropomorphic robotics. However, the enormous data set causes the labeling and prediction burden. In this paper, the swivel motion reconstruction approach was applied to imitate human-like behavior using the kinematic mapping in robot redundancy. For the sake of efficient computing, a novel incremental learning framework that combines an incremental learning approach with a deep convolutional neural network (IN-DCNN) is proposed for fast and efficient learning. The algorithm exploits a novel approach to detect changes from human motion data streaming and then to evolve its hierarchical representation of features. The incremental learning process is capable of fine-tuning the deep network only when model drifts detection mechanisms are triggered. Finally, we experimentally demonstrated this neural network's learning procedure and translated the trained human-like model to manage the redundancy optimization control of an anthropomorphic robot manipulator (LWR4+, KUKA, Germany). The anthropomorphic kinematic structure based redundant robots can be held by this approach. The experimental results showed that our architecture could not only enhance the regression accuracy but also significantly reduce the processing time of learning human motion data.

Synchronization of Network Systems via Aperiodic Sampled-Data Control With Constant Delay and Application to Unmanned Ground Vehicles

Abstract: In this paper, the synchronization issue for network systems with nonlinear dynamics is considered. Together with zero-order holder, the aperiodic sampled-data control law is utilized. Compared with the traditional periodic sampled-data control method, this approach demonstrates more greater flexibility. Adopting input delay approach, the initial sampled-data system is remodeled by continuous time system involving time-varying delays in the control signals. For the purpose of designing the sampling controllers suffering constant delays, an updated Lyapunov functional is developed from the augmentation of Wirtinger's inequality. Such a Lyapunov functional results in efficient and simplified synchronization conditions. A sufficient condition for synchronizability of network systems is set up. Then, for the case of unstable systems with some constant delays, a fresh discretized Lyapunov functional is introduced. Finally, we utilize the numerical simulation outcomes to prove the efficacy and advantage of our algorithm. Moreover, based on the network unmanned ground vehicle systems, the experiment results in a real scenario are provided to illustrate the effectiveness of the designed synchronization scheme.

Successive Waypoints Tracking of an Underactuated Surface Vehicle

Abstract: In marine guard, patrol, and racing scenarios, it is of great importance to autonomously helm an underactuated surface vehicle (USV) to accurately achieve successive waypoints tracking (SWT) with prescribed velocities and courses. In this paper, in the presence of completely unknown dynamics and environmental forces, the emerging SWT problem is innovatively solved by creating a novel model-free guidance-control integrated framework. In lieu of direct guidance to the waypoint which inevitably suffers from singularity, a new tool called bridge trajectory (BT) exactly passing through the generalized waypoint (GW) is first developed by defining marching and ahead points, i.e., a marching point (MP) and an ahead point (AP). Combining with pursuit guidance and finite-time unknown observer (FUO), successive BTs are switched ON and OFF, with the aid of MP and AP, respectively. By virtue of the FUO, cascade analysis, filtered backstepping, and Lyapunov approach, BT tracking control laws for surge and yaw motions are further synthesized to ensure successive GWs with desired positions, velocities, and courses can be tracked accurately, and thereby eventually contributing to a BT-guided model-free solution to the SWT problem. Simulation studies on a benchmark USV demonstrate remarkable performance of the proposed method.

Finite-Time Observer-Based Sliding Mode Control for Quantized Semi-Markov Switching Systems With Application

Abstract: This article investigates the problem of sliding mode control (SMC) for semi-Markov switching systems (S-MSSs) with quantized measurement in finite-time level. The transition between different subsystems obeys a stochastic semi-Markov process related to nonexponential distribution. Additionally, due to the sensor information constraints, the state vectors are not always measurable in practice. Moreover, compared with existing results in literature, the output quantization is first considered for finite-time SMC problem via a logarithmic quantizer. Our attention is to design an appropriate finite-time SMC law to attenuate the influences of parametrical uncertainty and external disturbance onto the overall performance of the system under consideration. First, by the key points of stochastic semi-Markov Lyapunov function and observer design theory, a desired SMC law is constructed to guarantee that the system trajectories can arrive at the specified sliding surface (SSS) within an assigned finite-time level. Then, ST-dependent sufficient conditions are established to ensure the required finite-time boundedness performance including both reaching phase and sliding motion phase. Finally, the applicability of the proposed results is demonstrated by a single-link robot arm model.

Interval Type-2 Fuzzy Sampled-Data $H_{\infty }$ Control for Nonlinear Unreliable Networked Control Systems

Abstract: This paper is concerned with the problem of interval type-2 (IT2) fuzzy sampled-data $H_{\infty }$ control for nonlinear networked control systems with parameter uncertainties, data dropout, and transmission delay. The IT2 fuzzy system used to describe the networked control systems and the IT2 networked sampled-data controller implemented by a zero-order holder is connected in the closed-loop system. By means of the input delay approach, the resulting closed-loop system is converted into a continuous-time delayed system. Subsequently, the continuous-time Lyapunov–Krasovskii functional theory is used to carry out the stability analysis. Some slack matrices and the bound information in membership functions are introduced to obtain the relaxed sufficient conditions formed by the linear matrix inequalities, which guarantee the anticipant $H_{\infty }$ performance. Finally, the efficiency and merits of the proposed design method are verified by four practical systems.

Sliding Mode Control for Nonlinear Stochastic Semi-Markov Switching Systems With Application to SRMM

Abstract: In this paper, the sliding mode control (SMC) design for nonlinear stochastic semi-Markov switching systems (S-MSSs) is studied via the bound of time-varying transition rate matrix, in which semi-Markov switching parameters, stochastic disturbance, uncertainty, and nonlinearity are all considered in a unified framework. The system under consideration is more general, which covers the Markov switching system with sojourn-time-independent transition rate matrix as a special case. Many practical systems subject to unpredictable structural variations can be characterized by nonlinear stochastic S-MSSs with sojourn-time-dependent transition rate matrix. The specific information about the bound of time-varying transition rate matrix is known for the sliding mode controller design. First, by using the stochastic semi-Markov Lyapunov function, sojourn-time-dependent sufficient conditions are developed to guarantee the closed-loop sliding mode dynamics stochastically stable. Then, the SMC law is constructed to ensure the reachability of the sliding mode dynamics in a finite-time level. Finally, one joint of space robot manipulator model is described as nonlinear stochastic S-MSSs to illustrate the validity of the proposed SMC design method.

Reduced-Order Observer Design for Switched Descriptor Systems With Unknown Inputs

Abstract: This paper presents a systematical reduced-order observer design method for a class of switched descriptor systems containing unknown inputs (UIs) in both the dynamic and the output equations. Generally speaking, when the output of the system contains UIs, the reduced-order observer design will become much more challenging. In order to overcome the difficulty brought by the UIs in the output, first, by introducing a new UI vector, a new equivalent system is obtained, which does not contain UIs in the corresponding output any more. Then, for the purpose of reduced-order observer design, the observer matching condition (OMC) and the minimal phase condition (MPC) are discussed, and it is shown that the new general system maintains both the OMC and the MPC. Subsequently, based on the discussions on the existence conditions, a reduced-order unknown input observer is developed to asymptotically estimate the state of the original system. Finally, an electronic circuit example is given to show the effectiveness of the proposed method.

Voltage Difference Residual-Based Open-Circuit Fault Diagnosis Approach for Three-Level Converters in Electric Traction Systems

Abstract: This paper proposes a voltage difference residual-based fault diagnosis approach for three-level converters in electric traction systems. Compared to the existing open-circuit fault diagnosis approaches, the proposed approach cannot only detect and identify the faulty devices among the transistors and clamp diodes in both of rectifier and inverter, but also hierarchically diagnose the leg-level and device-level faults. Furthermore, no additional sensors are required and all of specified parameters can be directly identified. Specifically, the current related term in the dc-link voltage difference equations are analyzed and modeled under the normal case and open-circuit fault cases in the transistors and clamp diodes, respectively. Subsequently, the estimation models of the dc-link voltage difference are developed, and then, the residuals and their evaluation functions between estimation and actual are generated. Furthermore, by analyzing the residual evaluation functions for various cases and building the current similarity functions, the hierarchical diagnosis algorithms are proposed to identify the leg-level and device-level faults. Finally, hardware-in-the-loop experiments are performed. The results show the effectiveness and robustness of the proposed approach.

Self-Triggered DMPC Design for Cooperative Multiagent Systems

Abstract: This paper considers the cooperation control problem for a team of dynamically decoupled agents with resource constraints. A codesign of self-triggered mechanism and distributed model predictive control (DMPC) is proposed to achieve the cooperative objectives while efficiently exploiting communication network. The proposed self-triggered DMPC (ST-DMPC) possesses three important features. First, the communication cost is explicitly incorporated in the cost function. In this way, the triggering instant and control inputs are simultaneously optimized, and a desired tradeoff between control performance and communication cost is achieved. Second, at triggering instants, the first element of the optimal control input sequence along with the current state instead of the whole trajectory is broadcast to neighbors for cooperation, which further reduces communication load. Third, sufficient conditions on design parameters related to predictive states of neighbor agents are constructed to ensure stability of the overall system. The application of the proposed ST-DMPC to four robot manipulators validates the effectiveness of this method.

Fuzzy-Model-Based Output Feedback Sliding-Mode Control for Discrete-Time Uncertain Nonlinear Systems

Abstract: This paper addresses the output feedback sliding-mode control (SMC) problem for discrete-time uncertain nonlinear systems through Takagi–Sugeno fuzzy dynamic models. Combining with the sliding surface, a descriptor system is constructed to characterize the sliding motion dynamics. Sufficient conditions for asymptotic stability analysis of the sliding motion are attained by the piecewise quadratic Lyapunov functions within a convex optimization setup. Two SMC design approaches are proposed to ensure the finite-time convergence of the sliding surface. Two simulation examples are presented to show the effectiveness of the proposed approaches.

Discrete-Communication-Based Bipartite Tracking of Networked Robotic Systems via Hierarchical Hybrid Control

Abstract: This paper investigates the bipartite tracking problem of networked robotic systems (NRSs) subject to input disturbances, discrete communications and signed directed graphs. Two new classes of hierarchical hybrid control algorithms (HHCAs), which involve both continuous and discontinuous signals in a uniform framework, are designed to solve the aforementioned problem in the model-independent control manner, i.e., without using the prior information of the system model. Besides, with the help of the Lyapunov statement and hybrid system theory, we establish several sufficient conditions for guaranteeing the convergence of the proposed hybrid algorithms. Finally, numerical examples are presented to illustrate the effectiveness of the proposed results.

Simultaneous Input and State Estimation for Integrated Motor-Transmission Systems in a Controller Area Network Environment via an Adaptive Unscented Kalman Filter

Abstract: As the requirements on powertrain efficiency of electric vehicles (EVs) are increasing, integrated motor-transmission (IMT) powertrain systems for EVs are becoming a promising solution. For the integration of IMT powertrain systems, the system state information and the actuator status are usually required for the closed-loop controller design or the on-board fault diagnosis. Embracing the demands, an observer for simultaneous estimation of input and system state of an IMT powertrain system is studied in this paper. It is well-known that controller area network (CAN) has been dominant in the vehicle network, which is used to communicate among controllers, sensors, and actuators. However, the CAN bus always induces time-varying delays when there are a number of communication nodes on the bus. The CAN-bus induced delay would result in vibrations in the vehicle powertrain or even deterioration of the entire closed-loop system. To deal with the CAN-bus induced delay in the estimation work for IMT powertrain systems, the potential random delays are considered in a three-state nonlinear model which represents the behavior of an IMT system. To estimate the input and state simultaneously, an adaptive unscented Kalman filter (AUKF) is adopted. As we know, the adopted AUKF has the benefits of dealing with system nonlinearities and calculating the noise covariance matrix automatically. Simulations and comparisons are carried out. We can see from the results that the proposed observer estimates the input and system state well. Moreover, the resulting estimation error is smaller comparing with the estimation error of the observer based on extended Kalman filter algorithm.

ADP-Based Security Decentralized Sliding Mode Control for Partially Unknown Large-Scale Systems Under Injection Attacks

Abstract: In this paper, the decentralized optimal control problem is addressed for a class of large-scale systems subject to injection attacks. All subsystem matrices are considered to be unavailable to the designer. A model-free decentralized sliding mode control (SMC) scheme for each subsystem is designed via just utilizing its own state information and the known bounds of the interconnections and the injection attacks. Moreover, the adaptive dynamic programming (ADP) approach is incorporated to deal with the infinite horizon optimal control problem for the sliding mode dynamics, which is equivalent to the solution of a set of parallel algebraic Riccati equations. Furthermore, a novel parallel policy iteration algorithm is developed to implement the proposed decentralized SMC scheme without using all subsystems dynamics matrices. Specifically, it is shown that during the whole policy iteration steps, the reachability of each sliding variable and the stability of each sliding mode dynamics are guaranteed simultaneously by the online updating decentralized SMC scheme. Finally, the applicability of the proposed novel ADP-based decentralized SMC strategy is illustrated by a two-machine power system subject to three different injection attacks.

Output-Constrained Robust Sliding Mode Based Nonlinear Active Suspension Control

Abstract: The nonlinear active suspension control design can be analyzed as a multiobjective control problem that caters to the enhancement in ride comfort levels while ensuring that road holding is maintained with the constrained suspension displacement movement. In this article, a robust constrained output feedback approach employing sliding mode controllers is proposed for the nonlinear active suspension system equipped with hydraulic actuators. Adhering to mechanical design limitations, the suspension displacement and the corresponding displacement rate are constrained using asymmetric barrier Lyapunov functions. Consequently, a nonlinear control law that incorporates the first-order sliding mode control is then formulated to regulate the hydraulic valve and thereby provide an active control effort. The robust control performance with high-performance improvements for ride comfort in the presence of parametric uncertainties, sensor noise, and over variable driving velocities for different road conditions is established using simulation studies.

A Deterministic Annealing Neural Network Algorithm for the Minimum Concave Cost Transportation Problem

Abstract: In this article, a deterministic annealing neural network algorithm is proposed to solve the minimum concave cost transportation problem. Specifically, the algorithm is derived from two neural network models and Lagrange–barrier functions. The Lagrange function is used to handle linear equality constraints, and the barrier function is used to force the solution to move to the global or near-global optimal solution. In both neural network models, two descent directions are constructed, and an iterative procedure for the optimization of the neural network is proposed. As a result, two corresponding Lyapunov functions are naturally obtained from these two descent directions. Furthermore, the proposed neural network models are proved to be completely stable and converge to the stable equilibrium state, therefore, the proposed algorithm converges. At last, the computer simulations on several test problems are made, and the results indicate that the proposed algorithm always generates global or near-global optimal solutions.

Input–Output Finite-Time Generalized Dissipative Filter of Discrete Time-Varying Systems With Quantization and Adaptive Event-Triggered Mechanism

Abstract: This article discusses the issue of input–output finite-time generalized dissipative filter design for a class of discrete time-varying systems. First, an adaptive event-triggered mechanism (AETM) with an adaptive law is proposed to adjust the threshold in the AETM according to the error between the system states and the filter states. Such an AETM determines whether the measurement output should be transmitted or not, which is more effective to economize the communication resources comparing with the traditional event-triggered mechanism. Second, in view of network-induced delays, the quantization and the AETM, a time-varying filter error system (TV-FES) is modeled. Then, a new augmented time-varying Lyapunov functional containing triple sum terms is provided. Based on the new finite-sum inequality and improved reciprocally convex combination lemma, delay-dependent conditions are obtained, which can ensure the TV-FES to be input–output finite-time stable and satisfy the given generalized dissipative performance. Moreover, the recursive linear matrix inequalities are presented to obtain the desired filter gains. Finally, numerical examples demonstrate the superiority and feasibility of the proposed method in this article.