Showing papers in "International Journal of Control Automation and Systems in 2015"

A survey on Markovian jump systems: Modeling and design

Abstract: Markovian jump systems are a special class of hybrid and stochastic systems which can be used to describe many real world applications, such as manufacturing systems, power systems, chemical systems, economic systems, communication and control, etc. In this paper, a survey on recent developments of modeling, analysis and design of Markovian jump systems is presented. First, stability issues on Markovian jump systems are addressed. Then a variety of control and filter design methods are systematically recalled. Furthermore, the new trends of Markovian jump systems with uncertain transition rates as well as semi-Markovian jump systems are also discussed.

Geometric control of a quadrotor UAV transporting a payload connected via flexible cable

Abstract: We derived a coordinate-free form of equations of motion for a complete model of a quadrotor UAV with a payload which is connected via a flexible cable according to Lagrangian mechanics on a manifold. The flexible cable is modeled as a system of serially-connected links and has been considered in the full dynamic model. A geometric nonlinear control system is presented to asymptotically stabilize the position of the quadrotor while aligning the links to the vertical direction below the quadrotor. Numerical simulation and experimental results are presented and a rigorous stability analysis is provided to confirm the accuracy of our derivations. These results will be particularly useful for aggressive load transportation that involves large deformation of the cable.

Robust Path Tracking Control of Nonholonomic Wheeled Mobile Robot: Experimental Validation

Abstract: The article addresses a robust control strategy for efficient path tracking of nonholonomic wheeled mobile robot (WMR) based on time delay approach. Depending on the application requirements, nonholonomic WMR system might be subjected to various payloads, which affects the overall system mass, inertia, position of center of mass and other hardware parameters statically or dynamically. Under such circumstances, accurate modeling of nonholonomic robots is difficult and challenging. The proposed controller negotiates uncertainties caused due to payload variations as well as associated disturbances and reduces modeling effort through approximation of the overall uncertainties with a composite function. It has been shown that the controller does not require any bounds on the uncertainties, thus providing unconstrained working paradigm. The controller is proposed for a nonholonomic WMR and its effectiveness is verified through simulation and experimentally while WMR is commanded to track various paths. The superior performance is also noted against adaptive sliding mode control law.

Robust sliding-mode observer-based sensor fault estimation, actuator fault detection and isolation for uncertain nonlinear systems

Abstract: This paper deals with the issues of sensor fault estimation, actuator fault detection and isolation for a class of uncertain nonlinear systems. By taking the sensor fault vector as a part of an extended state vector, the original system with sensor faults, actuator faults and unknown inputs is transformed into an augmented singular system which is just with actuator faults and unknown inputs. For the constructed singular system, a robust sliding-mode observer is developed to simultaneously estimate the states and sensor faults of original system, and the observer gain matrices are computed in terms of linear matrix inequalities by solving an optimization problem. Then an actuator fault detector is designed to detect actuator faults when ones occur, and multiple observers used as actuator fault isolators are proposed to identify which actuator is with fault. Finally, a simulation example is given to illustrate the effectiveness of the proposed methods.

Adaptive control of nonlinear pure-feedback systems with output constraints: Integral barrier Lyapunov functional approach

Abstract: This paper studies an adaptive control problem of completely non-affine pure-feedback systems with an output constraint. The implicit function theorem and the mean value theorem are employed to deal with unknown output-constrained non-affine nonlinearities. Then, a dynamic surface design approach based on an appropriate Integral Barrier Lyapunov Functional is presented to design an adaptive controller ensuring both the constraint satisfaction and the desired tracking ability. For the controller design, the function approximation technique using neural networks is used for estimating unknown nonlinear terms with control direction nonlinearities. It is shown that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error converges to an adjustable neighborhood of the origin while the output constraint is never violated.

Design of a class of new nonlinear disturbance observers based on tracking differentiators for uncertain dynamic systems

Abstract: The main contribution of this paper is to present a general design method of new nonlinear disturbance observer (NDO) based on tracking differentiator (TD) for uncertain dynamic system. The stability and convergence of the proposed NDO can be guaranteed by TD. This new NDO can be used to estimate many types of uncertain disturbances, and can overcome the disadvantages of existing NDOs that need the priori information concerning the upper and lower bounds of the disturbance and its ith derivative’s Lipschitz upper bound. It can be also applied in uncertain dynamic system for various purposes such as disturbance estimate and compensation, solving the problem of control input constraint, and reducing even eliminating chattering of control input. Simulation results are presented to show the effectiveness of the developed NDO.

Resilient Control in the Presence of DoS Attack: Switched System Approach

Abstract: This paper studies the switched resilient control of the Cyber-Physical Systems (CPSs) under Denial-of-Service (DoS) attack. We interpret the term ‘resilience’ as the ability to be both robust to the external disturbances in the physical layer, and defending against DoS attacks in the cyber layer. A hybrid-theoretical framework is proposed which is comprised of a physical control subsystem, a cyber security subsystem, and an interface which integrates the control design with the security configuration. Within this framework, the controller will switch in accordance with the competing result of the cyber attacker and defender. Our approach highlights the interaction between the physical control layer and cyber security layer and achieves the goal of coupled design. Some algorithms are provided to obtain the so-called joint defense strategies. Finally, the proposed method is applied to the voltage regulation of the Uninterrupted Power System (UPS).

Discrete-Time Fractional-Order PID Controller: Definition, Tuning, Digital Realization and Some Applications

Abstract: In some of the complicated control problems we have to use the controllers that apply nonlocal operators to the error signal to generate the control. Currently, the most famous controller with nonlocal operators is the fractional-order PID (FOPID). Commonly, after tuning the parameters of FOPID controller, its transfer function is discretized (for realization purposes) using the so-called generating function. This discretization is the origin of some errors and unexpected results in feedback systems. It may even happen that the controller obtained by discretizing a FOPID controller works worse than a directly-tuned discrete-time classical PID controller. Moreover, FOPID controllers cannot directly be applied to the processes modeled by, e.g., the ARMA or ARMAX model. The aim of this paper is to propose a discrete-time version of the FOPID controller and discuss on its properties and applications. Similar to the FOPID controller, the proposed structure applies nonlocal operators (with adjustable memory length) to the error signal. Two methods for tuning the parameters of the proposed controller are developed and it is shown that the proposed controller has the capacity of solving complicated control problems.

Dynamic modeling of a two-wheeled inverted pendulum balancing mobile robot

Abstract: Many of the currently available dynamic models for the two-wheeled balancing mobile robot have some common mistakes, which are mainly due to misunderstanding about the coordinate systems to describe the rotating motions and a lack of rigorous comparison with former derivations. This paper investigates the modeling procedures for the 2WBMR in terms of the Lagrangian approach and Kane’s method, through which an exact dynamic model is given, and we discuss how the modeling errors in the former works were induced. Numerical examples are given to see the effect of the erroneous terms on the postural stability.

Coordinated design of TCSC and PSS controllers using VURPSO and Genetic algorithms for multi-machine power system stability

Abstract: Thyristor controlled series capacitor (TCSC) can regulate line impedance and therefore increase transferred power of the system. On the other hand power system stabilizer (PSS) increases dynamic stability of generator. To enhance the stability, combination of TCSC and PSS can be applied, and in such a case coordination of TCSC and PSS is essential. This paper applies this combined controller in order to enhance the stability of multi-machine system. Parameters of these controllers are optimized by velocity update relaxation particle swarm optimization (VURPSO) algorithm and Genetic algorithm (GA). The simulation results show that the combination of VURPSO algorithm and GA leads to a better design and stability.

High-speed train navigation system based on multi-sensor data fusion and map matching algorithm

Abstract: Navigation system for high-speed trains is necessary for increased operational safety and efficiency, new services for customers, and low maintenance cost. This paper proposes a high accuracy navigation system for high-speed trains based on a sensor fusion algorithm, with non-holonomic constraints, for multiple sensors, such as accelerometers, gyroscopes, tachometers, Doppler radar, differential GPS, and RFID, and a map matching algorithm. In the proposed system, we consider the federated Kalman filter for sensor fusion, where local filters utilize filter models developed for various sensor types. Especially, the local Kalman filter for RFID positioning, that is detected at irregular time intervals due to the varying train speed and RFID tag spacing, is developed to maintain high performance during GPS outage. In addition, an orthogonal projection map matching algorithm is developed to improve the performance of the proposed system. The performance of the proposed system is demonstrated with numerous simulations for a high-speed train in Korea. The simulation results are analyzed with respect to the existence of tunnel, RFID deployment spacing, RFID location uncertainty, and DGPS error.

Fuzzy sliding mode control of container cranes

Abstract: A fuzzy sliding mode control strategy for container cranes is discussed in this paper. In which, the sway motion of a payload is integrated into the trolley dynamics in a sliding surface. This scheme guarantees the asymptotic stability of the closed-loop system. Moreover, the control gain, which is the most important component, is a flexible gain and is tuned based on fuzzy laws to avoid chattering phenomena of the system. The performance of the closed-loop system has been simulated using MATLAB. In addition, to illustrate the efficiency of the proposed control law, experimental results are also provided.

A novel data fusion scheme using grey model and extreme learning machine in wireless sensor networks

Abstract: With the increasing presence and adoption of wireless sensor networks (WSNs), the demand of data acquisition and data fusion are becoming stronger and stronger In WSN, sensor nodes periodically sense data and send them to the sink node Since the network consists of plenty of low-cost sensor nodes with limited battery power and the sensed data usually are of high temporal redundancy, prediction- based data fusion has been put forward as an important issue to reduce the number of transmissions and save the energy of the sensor nodes Considering the fact that the sensor node usually has limited capabilities of data processing and storage, a novel prediction-based data fusion scheme using grey model (GM) and optimally pruned extreme learning machine (OP-ELM) is proposed The proposed data fusion scheme called GM-OP-ELM uses a dual prediction mechanism to keep the prediction data series at the sink node and sensor node synchronous During the data fusion process, GM is introduced to initially predict the data of next period with a small number of data items, and an OPELM- based single-hidden layer feedforward network (SLFN) is used to make the initial predicted value approximate its true value with extremely fast speed As a robust and fast neural network learning algorithm, OP-ELM can adaptively adjust the structure of the SLFN Then, GM-OP-ELM can provide high prediction accuracy, low communication overhead, and good scalability We evaluate the performance of GM-OP-ELM on three actual data sets that collected from 54 sensors deployed in the Intel Berkeley Research lab Simulation results have shown that the proposed data fusion scheme can significantly reduce redundant transmissions and extend the lifetime of the whole network with low computational cost

Robust fault tolerant tracking control design for a linearized hypersonic vehicle with sensor fault

Abstract: In this study, the robust fault tolerant tracking problem is investigated for a linearized hypersonic vehicle model with the bounded external disturbance and the sensor fault. Firstly, the nonlinear longitudinal dynamics of hypersonic vehicle is linearized as a linear time-invariant system with sensor fault, a reference model is introduced for the aim of fault tolerant tracking control. And then an observer-based fault tolerant output feedback tracking controller design approach is proposed by using the linear matrix inequalities (LMIs) technique. The asymptotic stability of the whole closed-loop system is analyzed using the well-known Lyapunov stability method. Finally, the simulation results are presented to verify the applicability of the developed fault tolerant approach.

Approximation-based adaptive tracking control of nonlinear pure-feedback systems with time-varying output constraints

Abstract: An adaptive neural network control problem of completely non-affine pure-feedback systems with a time-varying output constraint and external disturbances is investigated. For the controller design, we presents an appropriate Barrier Lyapunov Function (BLF) considering both the time-varying output constraint and the control direction nonlinearities induced from the implicit function theorem and mean value theorem. From an error transformation, the BLF dependent on the time-varying constraint is transformed into the explicitly time-independent BLF. Based on the explicitly time-independent BLF, an adaptive dynamic surface control scheme using the function approximation technique is designed to ensure both the constraint satisfaction and the desired tracking ability. It is shown that all signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error converges to an adjustable neighborhood of the origin while the time-varying output constraint is never violated.

GPS spoofing detection using accelerometers and performance analysis with probability of detection

Abstract: This paper considers a GPS spoofing detection problem. A GPS spoofing attack attempts to deceive a GPS receiver by broadcasting counterfeit GPS signals. GPS spoofing attacks are very significant since spoofing amounts to intentional interference and the target receiver is not aware of its being attacked by GPS spoofers. We suppose that accelerometers can be used to detect the GPS spoofing signal by comparing the accelerometer outputs with the acceleration estimated from the GPS outputs. In this paper, we analyze the performance based on the probability of detection for the K-consecutive alarm method, as well as the modified Tong and modified M of N methods. The performance of the GPS spoofing detection algorithm is analyzed for two cases: a fixed threshold and a fixed probability of false alarm.

Design of an exoskeleton with minimized energy consumption based on using elastic and dissipative elements

Abstract: This study proposes the design of an exoskeleton featuring minimized energy consumption during stand-to-sit and sit-to-stand (STS) motion and walking while carrying a load through the utilization of elastic and dissipative elements. In order to determine which phase and joint can utilize elastic and dissipative elements, we analyzed a human’s walk and STS motions. With this human motion data, we propose an elastic element for hip adduction and abduction (Ad/Ab), series dissipative actuation (SDA) using a semi-active hydraulic system for hip flexion and extension (Fl/Ex) and parallel elastic and series dissipative actuation (PESDA) for the knee Fl/Ex, which is combined with the SDA and the parallel elastic element. The effect of the developed exoskeleton (EXO) with a hip Ad/Ab spring, hip SDA and knee PESDA was evaluated by measuring the user’s ground reaction force (GRF). When wearing the EXO with a hip Ad/Ab spring, hip SDA and knee PESDA, the subject’s GRF was smaller as compared to when the subject was not wearing the EXO while walking and performing the STS motion under a 20-kg load condition, except during the heel strike of the walk motion.

Robust observer-based sensor fault reconstruction for discrete-time systems via a descriptor system approach

Abstract: This paper addresses the problem of observer-based sensor fault reconstruction for discretetime systems subject to external disturbances via a descriptor system approach. First, an augmented descriptor system is formulated by letting the sensor fault term be an auxiliary state vector; then a discrete-time descriptor state observer is constructed to achieve concurrent reconstructions of original system states and sensor faults. Sufficient and necessary conditions for the asymptotic stability of the proposed observer are explicitly provided. To broaden its application scope, less restrictive existence conditions are further discussed. Further, sufficient conditions for the robust stability of the proposed observer are formulated in terms of linear matrix inequalities (LMIs) that can be conveniently solved using LMI optimization techniques. After that, an extension of the proposed linear approach to discretetime nonlinear systems with Lipschitz constraint is investigated. At last, two illustrative examples are given to verify the effectiveness of the proposed techniques.

Adaptive leader-follower formation control of mobile robots with unknown skidding and slipping effects

Abstract: This paper investigates an adaptive leader-follower formation control problem of multiple mobile robots in the presence of unknown skidding and slipping. First, we employ the concept of virtual robots to achieve the desired formation and derive the kinematics of the virtual leader and follower robots considering skidding and slipping effects. Then, we design an adaptive formation controller based on a two-dimensional error surface where the adaptive technique is used for compensating the unknown skidding and slipping effects that influence the follower robots. From Lyapunov stability theorem, we show that all errors of the closed-loop system are uniformly ultimately bounded, and thus the desired formation is successfully achieved regardless of the presence of unknown skidding and slipping effects. Simulation results are provided to demonstrate the effectiveness of the proposed formation control scheme.

An Autonomous Underwater Vehicle as an Underwater Glider and Its Depth Control

Abstract: To provide a conventional autonomous underwater vehicle with gliding capability, we assume a moving battery and a buoyancy bag installed in a torpedo shaped autonomous underwater vehicle. We develop a mathematical model for the underwater vehicle and derive a stable gliding condition for it. Then an LQR controller is designed to control the zigzag depth of the vehicle, where the derived gliding condition is used as set-points of the control system. For control efforts in the gliding movement, the changes in the center of gravity and the net buoyancy are used, but neither thruster nor rudders are used. By using the gliding capability, the underwater vehicle can move to a farther location silently with less energy consumption and then start operating as a normal autonomous underwater vehicle. We show the feasibility of the proposed method by simulations using Matlab/Simulink.

Periodic event-triggered state-feedback and output-feedback control for linear systems

Abstract: This paper investigates the periodic event-triggered control for linear systems. Both state-feedback and output-feedback controllers are considered. The resulting closed-loop systems are modeled as delay systems and event conditions are constructed in more general forms. The stability analyses rely on the proposed Lyapunov functional and the improving free weighing matrix method. Criteria co-designing the feedback gain matrices and the event condition parameters are established to guarantee the asymptotic stability of the closed-loop systems. Moreover, we can obtain the maximum verifying period by solving a generalized eigenvalue problem. Simulation examples verify the less conservativeness and efficiency of the theoretical results.

Domain adaption of vehicle detector based on convolutional neural networks

Abstract: Generally the performance of a vehicle detector will decrease rapidly, when it is trained on a fixed training set but applied to a specific scene with view changes. The reason is that in the training set only a few samples are helpful for vehicle detection in the specific scene while other samples disturb the accurate detections. To solve this problem, we propose a novel transfer learning method to adapt the trained vehicle detector based on convolutional neural networks (ConvNets) to a specific scene with several new labeled samples. At first we reserve the share-filters and update the non-shared filters to improve the sensitivity of the vehicles in the specific scene. Then we combine the similar feature maps to accelerate the detection speed. At last for making the vehicle detector stable, we fine-tune it several times with the updated training set. Our contributions are an original research on transferring the vehicle detector based on ConvNets and an optimization approach about removing the redundant connections in the ConvNet vehicle detector. The extensive comparative experiments on three different datasets demonstrate that the transferred detectors achieve the improvements on both of the accuracy and speed.

Unknown Input Observer Design for Takagi-Sugeno Fuzzy Stochastic System

Abstract: This paper deals with the unknown input observer design for Takagi-Sugeno (T-S) fuzzy models subjected to measurement noise and stochastic noise. The method applies the singular system theory by considering the measurement noise as an augmented system state, and then an unknown input observer based on the techniques of singular systems is developed to estimate both the system states and measurement noise simultaneously. Under a necessary assumption, the error dynamic system of the observer is free from the unknown inputs. And the observer gain matrix is determined by means of minimum covariance matrix of state residual. Two simulation examples are given to illustrate the correctness and effectiveness of the proposed method.

Fuzzy swinging-up with sliding mode control for third order cart-inverted pendulum system

Abstract: Cart Inverted Pendulum (CIP) system is a benchmark problem in nonlinear automatic control. In this paper, two third-order differential equations were derived to create a combining model for the cart-pendulum with its DC motor dynamics. Motor voltage was considered the system input in the presented model. The friction between the cart and rail was included in the system equations through a nonlinear friction model. Fuzzy Swinging-up controller was designed to swing the pendulum to the upright position, once reaching the upward position; Sliding Mode Controller (SMC) is activated, to balance the system. In order to verify the performance of the proposed SMC, a Linear Quadratic Regulator Controller (LQRC) was suggested and compared with the proposed SMC. Simulation and experimental results have shown a significant improvement of the proposed SMC over LQRC where, the pendulum angle oscillations were decreased by 80% in the real implementation.

Leader-following consensus of double-integrator multi-agent systems with noisy measurements

Abstract: This paper proposes a leader-following consensus control for continuous-time double-integrator multi-agent systems in noisy communication environment with a constant velocity reference state. Each follower in the team inaccurately measures its neighbors’ positions and the leader’s position if this follower has access to the leader, that the measured positions are corrupted by noises. The constant velocity of the leader is a priori well known. The consensus protocol is constructed based on algebraic graph theory and some stochastic tools. Conditions to ensure the tracking consensus in mean square are derived for both fixed and switching directed topologies. Finally, to illustrate the approach presented, some numerical simulations are carried out.

Quaternion-based finite-time control for attitude tracking of the spacecraft without unwinding

Abstract: This paper investigates two finite-time controllers for the attitude tracking control of the spacecraft based on the quaternion by terminal sliding mode control. Because quaternion is unable to represent the set of attitudes both globally and uniquely, it can result in unwinding. Unwinding makes a spacecraft perform an unnecessary large-angle maneuver when a small-angle maneuver in the opposite rotational direction is sufficient to achieve the objective. The first controller converges to the equilibrium without singularity in finite time, while the second one converges to the region near the equilibrium without singularity, chattering and unwinding in finite time. Saturation function is introduced to the first controller to eliminate singularity, while a novel fast terminal sliding mode control is introduced to the second controller to eliminate singularity and unwinding. Theoretical analysis shows that the controllers can make the spacecraft follow a time-varying reference attitude signal in finite time and guarantee the stability of the overall closed-loop system. Numerical simulations also demonstrate the effectiveness of the proposed control schemes.

Data Fusion-based Resilient Control System under DoS Attacks: A Game Theoretic Approach

Abstract: In this paper, the resilient control under the Denial-of-Service (DoS) attack is rebuilt within the framework of Joint Directors of Laboratories (JDL) data fusion model. The JDL data fusion process is characterized by the so-called Game-in-Game approach, where decisions are made at different layers. The interactions between different JDL levels are considered which take the form of Packet Delivery Rate of the communication channel. Some criterions to judge whether the cyber defense system is able to protect the underlying control system is provided. Finally, a numerical example is proposed to verify the validity of the proposed method.

Adaptive fault diagnosis and active tolerant control for wind energy conversion system

Abstract: The fault mathematic model of the transmission part of wind energy conversion system (WECS) is established, and adaptive fault observer is constructed in the presence of unknown disturbance, it can detect the faults of the system, and estimate these faults. Then, based on fault observer, an active tolerant controller is designed to ensure the stability of the transmission part of WECS with fault.The simulation results of different type faults of generator show the effectiveness and feasibility of adaptive fault diagnosis methods.

Optimal control problem of singular Boolean control networks

Abstract: In this paper, the optimal control problem of singular Boolean control networks is considered via semi-tensor product. Using an analogous needle variation, for multi-input case, a necessary condition for the existence of optimal control is provided based on the algebraic form of singular Boolean control networks, and the result is specialized to the single-input case. Then, an algorithm is presented to calculate an optimal control. Illustrative examples, including the single-input case, are given to show the feasibility of the theoretical results.

Approximate dynamic programming for two-player zero-sum game related to H ∞ control of unknown nonlinear continuous-time systems

Abstract: This paper develops a concurrent learning-based approximate dynamic programming (ADP) algorithm for solving the two-player zero-sum (ZS) game arising in H ∞ control of continuous-time (CT) systems with unknown nonlinear dynamics. First, the H ∞ control is formulated as a ZS game and then, an online algorithm is developed that learns the solution to the Hamilton-Jacobi-Isaacs (HJI) equation without using any knowledge on the system dynamics. This is achieved by using a neural network (NN) identifier to approximate the uncertain system dynamics. The algorithm is implemented on actor-critic-disturbance NN structure along with the NN identifier to approximate the optimal value function and the corresponding Nash solution of the game. All NNs are tuned at the same time. By using the idea of concurrent learning the need to check for the persistency of excitation condition is relaxed to simplified condition. The stability of the overall system is guaranteed and the convergence to the Nash solution of the game is shown. Simulation results show the effectiveness of the algorithm.