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Adaptive control for a class of teleoperation systems with prescribed transient-state and steady-state tracking behavior is considered in this paper. Novel sliding variables are proposed such that ...

This paper investigates the controller design for the tracking problem of uncertain nonlinear systems with constraints on input and output. First, to avoid the full-state measurement, the high-gain observer is designed to estimate the unmeasured state. Compared to existing observer design for nonlinear systems, the high-gain observer design only requires a modified version of the Lipschitz condition. The output feedback controller is further presented on this basis. Second, to consider the transient constraints on the tracking performance, a barrier Lyapunov function with the user-defined time-varying performance is developed. In addition, the proposed controller design is shown to be free of control direction singularity. The convergence of the learning scheme and the boundedness of all the closed-loop signals during the learning phase are discussed theoretically. Finally, two simulation examples are conducted to verify the advantage of the proposed adaptive output feedback controller design.

This paper develops a novel adaptive optimal control design method with full-state constraints and input saturation in the presence of external disturbance. First, to consider the full-state constraints, a barrier function is developed for system transformation. Moreover, it is shown that, with the barrier-function-based system transformation, the stabilization of the transformed system is equivalent to the original constrained control problem. Second, the disturbance attenuation problem is formulated within the zero-sum differential games framework. To determine the optimal control and the worst-case disturbance, a novel barrier-actor-critic algorithm is presented for adaptive optimal learning while guaranteeing the full-state constraints and input saturation. It is proven that the closed-loop signals remain bounded during the online learning phase. Finally, simulation studies are conducted to demonstrate the effectiveness of the presented barrier-actor-critic learning algorithm.

This paper presents an observer-based adaptive control for uncertain nonlinear systems with input saturation and output constraints. Backstepping technology is used in the controller design. A state observer is established to estimate the unmeasured states. An asymmetric barrier Lyapunov function (BLF) with prescribed performance is constructed to deal with the output constrains. The input saturation is solved by using Nussbaum function-based method. The dynamic surface control (DSC) technique is employed to reduce the complexity of the virtual control. It is illustrated that all the closed-loop signals are semi-global uniformly ultimately bounded (SGUUB). Finally, a simulation example verifies the proposed results.

Composite adaptive control schemes, which use both the system tracking error and the prediction error to drive the updating law, have become widespread in achieving an improvement of system performance. However, a strong persistent-excitation (PE) condition is required to guarantee the convergence of the parameter estimation errors. This paper proposes a novel composite adaptive control for nonlinear teleoperation systems with dynamic uncertainties and time-varying communication delays, by which the parameter convergence is achieved without the PE condition. The novelty lies in the construction of the prediction errors by designing lower-bounded gain matrices of the prediction errors. The stability criteria of the closed-loop teleoperation system are given in terms of linear matrix inequalities. Simulation studies are given to show the effectiveness of the proposed method.

This paper presents a model-free solution to the robust stabilization problem of discrete-time linear dynamical systems with bounded and mismatched uncertainty. An optimal controller design method is derived to solve the robust control problem, which results in solving an algebraic Riccati equation (ARE). It is shown that the optimal controller obtained by solving the ARE can robustly stabilize the uncertain system. To develop a model-free solution to the translated ARE, off-policy reinforcement learning (RL) is employed to solve the problem in hand without the requirement of system dynamics. In addition, the comparisons between on- and off-policy RL methods are presented regarding the robustness to probing noise and the dependence on system dynamics. Finally, a simulation example is carried out to validate the efficacy of the presented off-policy RL approach.

This article addresses the distributed cooperative control design for a class of sampled-data teleoperation systems with multiple slave mobile manipulators grasping an object in the presence of communication bandwidth limitation and time delays. Discrete-time information transmission with time-varying delays is assumed, and the Round-Robin (RR) scheduling protocol is used to regulate the data transmission from the multiple slaves to the master. The control task is to guarantee the task-space position synchronization between the master and the grasped object with the mobile bases in a fixed formation. A fully distributed control strategy including neural-network-based task-space synchronization controllers and neural-network-based null-space formation controllers is proposed, where the radial basis function (RBF) neural networks with adaptive estimation of approximation errors are used to compensate the dynamical uncertainties. The stability and the synchronization/formation features of the single-master-multiple-slaves (SMMS) teleoperation system are analyzed, and the relationship among the control parameters, the upper bound of the time delays, and the maximum allowable sampling interval is established. Experiments are implemented to validate the effectiveness of the proposed control algorithm.

This paper aims to design the controller for the system with dynamic uncertainty and unknown dynamics, where the system states are not measurable. The neural networks are employed to solve the unknown nonlinear problem of systems. The dynamic surface control (DSC) technique is employed to avoid the repeated differentiation in virtual control, simplifying stability analysis and the controller design. The Luenberger observer is introduced to estimate the unknown state variables, and its effectiveness is verified. By introducing the state error transformation in the Lyapunov stability analysis, the dynamics uncertainties are well solved, and the uniformly ultimate boundedness of all states within the closed-loop system can be guaranteed. Finally, both theoretical analysis and simulation results prove that all signals are uniformly ultimately bounded and converge to zero rapidly, proving the effectiveness of the proposed approach.

In this paper, the global exponential stability of bilateral teleoperation systems with the composite adaptive control under the condition of sufficient excitation is investigated. An adaptation law that allows reconstruction of parameter estimation error is proposed provided sufficient excitation is satisfied. Furthermore, the global exponential stability of the closed-loop teleoperation system is testified based on Lyapunov functional synthesis. Finally, the simulation demonstrate the performance of the proposed method.

This paper presents an adaptive dynamic surface control for uncertain nonlinear strict feedback systems with constraints on both input and output. Backstepping technology is employed in the controller design. Prescribed performance method with asymmetric barrier Lyapunov function (BLF) is used to deal with the output constrains. The input saturation is solved by using the Nussbaum function. The dynamic surface control (DSC) technique is employed to reduce the complexity of the virtual control. It is illustrated that all closed-loop signals are semi-global uniformly ultimately bounded (SGUUB). Finally, a simulation example proves the proposed results.

In this paper, we present control designs for a class of single-input and single-output (SISO) strict-feedback nonlinear systems with unknown dead-zone input. To consider the dead zone dynamics, the nonlinear dead zone is first represented as a linear time-varying system with a bounded disturbance. Then, a backstepping control method is proposed for adaptive controller design. In addition to using dynamic surface control (DSC) to solve the problem of the "explosion of complexity", a compensating signal is added to eliminate the effect of the dynamic surface filter error. Moreover, the prediction error between the system state and the serial-parallel estimation model is taken into consideration. Meanwhile, neural networks (NN) are used to deal with the unknown functions in the controller. Finally, simulations illustrate the effectiveness of the proposed approach.

In this paper, a fixed-time control strategy for a class of uncertain multi-robot systems with prescribed performance under a directed graph is proposed. The leader is given as a reference trajectory and every follower is descriebed as a n-Degree of Freedom robot manipulator. The dynamic model of the robot manipulator is devided into the nominal part and the uncertain part. A Radial Basis Function neural network is used to approximate the uncertain part. By using an error transformation, the prescribed performance constrained system is transformed into an "unconstrained" one, and the prescribed performance control with nonsingular sliding mode is proposed to guarantee that the sliding mode and the transformed error are uniformly ultimately bounded and converge to a small neighborhood within a fixed time. Simulation studies are given to validate the effectiveness of our proposed control strategy.

This paper considers a class of multilateral teleoperation system consisting of a single master manipulator which is operated by a human operator and multiple slave manipulators cooperatively grasping a common constrained object. Considering the network bandwidth constraints, the Round-Robin scheduling protocol is used to orchestrate the sampled slave signals to the master, and adaptive controllers for the teleoperation system are proposed in the presence of uncertain parameters, time delays, and communication bandwidth limitations. The stability of the closed-loop is established by using linear matrix inequalities. It is demonstrated that velocities and position error between the master manipulator end-effector and the object asymptotically converge to zero. Finally, simulation studies are given to demonstrate the effectiveness of the proposed approach.

In this paper, an online intermittent actor-critic reinforcement learning method is used to stabilize nonlinear systems optimally while also guaranteeing safety. A barrier function-based transformation is introduced to ensure that the system does not violate the user-defined safety constraints. It is shown that the safety constraints of the original system can be guaranteed by assuring the stability of the equilibrium point of an appropriately transformed system. Then, an online intermittent actor-critic learning framework is developed to learn the optimal safe intermittent controller. Also, Zeno behavior is guaranteed to be excluded. Finally, numerical examples are conducted to verify the efficacy of the learning algorithm.

This paper develops optimal control protocols for the distributed output synchronization problem of leader-follower multiagent systems with an active leader. Agents are assumed to be heterogeneous with different dynamics and dimensions. The desired trajectory is assumed to be preplanned and is generated by the leader. Other follower agents autonomously synchronize to the leader by interacting with each other using a communication network. The leader is assumed to be active in the sense that it has a nonzero control input so that it can act independently and update its control to keep the followers away from possible danger. A distributed observer is first designed to estimate the leader's state and generate the reference signal for each follower. Then, the output synchronization of leader-follower systems with an active leader is formulated as a distributed optimal tracking problem, and inhomogeneous algebraic Riccati equations (AREs) are derived to solve it. The resulting distributed optimal control protocols not only minimize the steady-state error but also optimize the transient response of the agents. An off-policy reinforcement learning algorithm is developed to solve the inhomogeneous AREs online in real time and without requiring any knowledge of the agents' dynamics. Finally, two simulation examples are conducted to illustrate the effectiveness of the proposed algorithm.

The presence of time delays in communication introduces a limitation to the stability of bilateral teleoperation systems. In this study, we address the design of Internal Model Control (IMC)-based controllers for teleoperation systems with arbitrary time delays. The two degrees of freedom IMC structure is utilized for the master and the slave respectively and thus the passivity-assumption on the external forces is not required. The stability of the overall system is analyzed by regarding the whole system as a multivariable system. A performance-oriented controller design process which guarantees the delayed tracking between the master and the slave is also given. Finally, an example is given to validate the effectiveness of the proposed method.

This paper proposes a novel adaptive control framework for nonlinear teleoperation systems with dynamic and kinematic uncertainties and time-varying time delays. Master-slave synchronization in the task space is achieved. Compared with the previous work, the developed method can simultaneously handle the unknown kinematics/dynamics and the asymmetric varying time delays in a unified framework, and the inverses of the robots' Jacobians are not needed in the control design, and thus the master and slave robots can be redundant and the control law will be implemented more efficiently. Based on Lyapunov-Krasovskii methods, the stability criteria are established by linear matrix inequalities. It is shown by simulation studies that the developed control law can guarantee the task-space synchronization between the master and the slave.

This paper presents a distributed optimal model reference adaptive control approach for solving containment control of heterogeneous multi-agent systems (MASs) with non-autonomous leaders. First, a fully distributed adaptive observer is designed to provide for each agent the desired reference trajectory by estimating the convex hull spanned by leaders. The distributed observer dynamics serves as a reference model for each follower to synchronize. The global communication graph information or the leader dynamics is not required to design the observer. In contrast to existing model reference adaptive controllers (MRAC) for single-agent systems and containment control solutions for MASs, the proposed MRAC approach imposes optimality and presents a distributed adaptive optimal solution to the containment control problem. To impose optimality, a performance function is defined based on the adaptive observers' states as well as the followers' local measurements. It is shown that considering non-autonomous leaders in this optimal control problem leads to solving inhomogeneous algebraic Riccati equations (AREs), instead of normal AREs in standard optimal control problems. To obviate the requirement of knowing the agents' dynamics, an off-policy reinforcement learning approach implemented on an actor-critic structure is utilized for solving the inhomogeneous ARE. A simulation example is conducted to illustrate the effectiveness of the presented method.

This paper presents a Hamiltonian-driven framework of adaptive dynamic programming (ADP) for continuous time nonlinear systems, which consists of evaluation of an admissible control, comparison between two different admissible policies with respect to the corresponding the performance function, and the performance improvement of an admissible control. It is showed that the Hamiltonian can serve as the temporal difference for continuous-time systems. In the Hamiltonian-driven ADP, the critic network is trained to output the value gradient. Then, the inner product between the critic and the system dynamics produces the value derivative. Under some conditions, the minimization of the Hamiltonian functional is equivalent to the value function approximation. An iterative algorithm starting from an arbitrary admissible control is presented for the optimal control approximation with its convergence proof. The implementation is accomplished by a neural network approximation. Two simulation studies demonstrate the effectiveness of Hamiltonian-driven ADP.

Gas flow temperature of blast furnace is one of significant indicators of judging the anterograde state of blast furnace. Aiming at the defects of traditional gas flow temperature prediction model, this paper proposes a prediction model for gas flow temperature based on improved regularized extreme learning machine algorithm (RELM). Firstly, influencing factors are chosen through analyzing the distribution of blast furnace gas flow. Considering the blast furnace production data contain high frequency noise, the Fourier transform method is used for spectrum analysis, and the appropriate digital filter is selected according to the spectrum analysis result to eliminate the high frequency noise. Secondly, this paper establishes the prediction model of gas flow temperature. Regularized extreme learning machine and finite memory recursive least squares are combined to overcome Data saturation problem of ordinary extreme learning machine. And the improved algorithm is named as RFM-RELM. Finally, actual production data are used to train and test this model. Experimental result indicate that the model can quickly and accurately predict gas flow temperature ,which provides effective help and support for blast furnace operators to analyze the conditions of blast furnace.

This paper is focused on the adaptive control design for flexible joint robots with parameter uncertainties. Based on the singular perturbation theory, the flexible joint robot is transformed into a fast subsystem and a slow subsystem. For the fast subsystem, the PD control strategy is used to suppress elastic oscillations by the feedback of the motor angular displacement error. For the slow subsystem, an adaptive control scheme is proposed to guarantee the parameter estimation error convergence. The closed-loop system is testified to be globally exponentially stable via the designed Lyapunov function. The comprehensive simulation results demonstrate great tracking precision and high robustness of the proposed control scheme.

This paper proposes an adaptive control for nonlinear bilateral teleoperation systems with single master and multiple slaves in the presence of uncertain parameters and variable time delays, and the master-slaves synchronization in the task space is achieved. The Round-Robin scheduling protocol is used to orchestrate the sampled slave signals to the master. The stability criterion of the closed-loop system is given by using LMIs based on Lyapunov-Krasovskii functional synthesis. The simulation is carried out on a single master/three-slaves teleoperation system, and the results show the effectiveness of the proposed controller.

The burden distribution matrix is an important means of controlling the burden distribution of blast furnace. In order to provide references to foremen of blast furnaces, a model is established in this paper, which is a relationship model between blast furnace parameters and burden distribution matrix, and is based on the extreme learning machine algorithm (ELM). The model decides if the next burden distribution matrix should be adjusted through a series of blast furnace parameters, such as the gas utilization rate, the blast volume, the blast pressure, the top pressure, the blast velocity, the permeability index, and the utilization coefficient. Finally, compared to other methods used LSSVM and PNN, the method based on ELM is faster and more accurate, so it is more suitable.

This paper presents a novel intermittent suboptimal event-triggered controller design for continuous-time nonlinear systems. The stability of the equilibrium point of the closed-loop system, and the performances are analyzed and quantified theoretically. It is proven that the static and the dynamic event-triggered suboptimal controllers have a known degree of suboptimality compared to the conventional optimal control policy. In order to generate dynamic event-triggering framework, we introduce an internal dynamical system. Moreover, the Zeno behavior is excluded. Finally, a simulation example is conducted to show the effectiveness of the proposed intermittent mechanisms.

In practical applications, the actual models of the robots in teleoperation systems are difficult to be obtained. In addition, the data-communication exhibit asymmetric variable time delays. All of those will definitely affect the performance of the teleoperation system. In this paper, we use multiple neural networks to approximate the uncertain parts of the system, so as to realize the compensation for uncertain terms in the control scheme. By choosing an appropriate Lyapunov functional, we show that the teleoperation system is stable. The stability criteria of the closed-loop teleoperation system are given in terms of linear matrix inequality (LMI). It is shown that the position errors between the master and the slave, the velocities of each robot converge to zero, asymptotically. Simulation studies which are performed on a nonlinear teleoperation system with 2-two-degree-of-freedoms manipulators are given to illustrate the performance of the proposed control scheme in the presence of asymmetric variable time-delays.

This paper presents a data-driven method based on off-policy integral reinforcement learning to solve the semi-global output regulation of continuous-time linear systems with input saturation. A family of state feedback laws for the input constrained output regulation problem is designed based on solving an algebraic Riccati equation. In contrast to the existing methods, complete knowledge of the system dynamics is no longer required in this paper. Instead, the data collected from online implementation is efficiently utilized to design the controller. Therefore, the controller design in this paper is data-driven. It is shown that the presented method can find feedback control inputs with constraint of amplitude saturation and stabilize a given linear system with all its poles inside or on the imaginary axis. Finally, a simulation example is conducted to show the validity of the presented approach to solve the semi-global output regulation of continuous-time linear systems with input saturation.

This paper presents a model-free distributed event-triggered containment control scheme for linear multiagent systems. The proposed event-triggered scheme guarantees asymptotic stability of the equilibrium point of the containment error as well the avoidance of the Zeno behavior. To relax the requirement of complete knowledge of the dynamics, we combine an off-policy reinforcement learning algorithm in an actor critic structure with the event-trigger control mechanism to obtain the feedback gain of the distributed containment control protocol. A simulation experiment is conducted to verify the effectiveness of the approach.

In this paper, a data-driven method is developed based on off-policy reinforcement learning to solve the semi-global output regulation of discrete-time linear systems with input saturation. Algebraic Riccati equation based method is used to design a family of state feedback laws for the constrained output regulation problem. In contrast to the existing methods, complete knowledge of the system dynamics is no longer required in this paper. Instead, the data collected from on-line is efficiently utilized to obtain the adaptive optimal control policy. It is shown that the presented method can find feedback control inputs with constraint of amplitude saturation and the ability to stabilize a given linear system with all its poles inside or on the unit circle. Finally, a simulation example is carried out to demonstrate the conclusions of the whole paper.

In this paper, bilateral teleoperation of multiple slaves coupled to a single master under scheduling communication is investigated. The sampled-data transmission between the master and the multiple slaves is fulfilled over a delayed communication network, and at each sampling instant, only one slave is allowed to transmit its current information to the master side according to some scheduling protocols. To achieve the master-slave synchronization, Round-Robin scheduling protocol and Try-Once-Discard scheduling protocol are employed, respectively. By designing a scheduling-communication-based controller, some sufficient stability criteria related to the controller gain matrices, sampling intervals, and communication delays are obtained for the closed-loop teleoperation system under Round-Robin and Try-Once-Discard scheduling protocols, respectively. Finally, simulation studies are given to validate the effectiveness of the proposed results., Comment: 13 pages, 12 figures, 4 tables, submitted to IEEE Transactions on Control Systems Technology

It is well known that parameter convergence in adaptive control can bring about an improvement of system performance, including accurate online identification, exponential tracking, and robust adaptation without parameter drift. However, strong persistent-excitation (PE) or sufficient-excitement (SE) conditions should be satisfied to guarantee parameter convergence in the classical adaptive control. This paper proposes a novel adaptive control to guarantee parameter convergence without PE and SE conditions for nonlinear teleoperation systems with dynamic uncertainties and time-varying communication delays. The stability criterion of the closed-loop teleoperation system is given in terms of linear matrix inequalities. The effectiveness of this approach is illustrated by simulation studies, where both master and slave are assumed to be two-link manipulators with full nonlinear system dynamics.

A procedure for guaranteed cost control design of delayed linear bilateral teleoperation systems with nonlinear external forces is proposed. The assumption that the external forces are nonlinear functions of velocities and/or positions of local devices, and one part of these forces satisfies a sector condition has been made. A virtual tool system is introduced to 'observe' the forces at the remote sides, the position and velocity information of the master, the slave and the virtual tool are feedbacked to the controllers, hence the proposed control scheme actually has a four-channel architecture. A delay-dependent stability criterion is formulated, and then a sub-optimal guaranteed cost controller is obtained by solving a convex optimization problem in the form of linear matrix inequalities (LMIs). The behavior of the resulting teleoperation system is illustrated in simulations. (C) 2015 The Franklin Institute. Published by Elsevier Ltd. All rights reserved. (Less)

In this paper, active control of impulsive noise with symmetric α-stable (SαS) distribution is studied. A general step-size normalized filtered-x Least Mean Square (FxLMS) algorithm is developed based on the analysis of existing algorithms, and the Gaussian distribution function is used to normalize the step size. Compared with existing algorithms, the proposed algorithm needs neither the parameter selection and thresholds estimation nor the process of cost function selection and complex gradient computation. Computer simulations have been carried out to suggest that the proposed algorithm is effective for attenuating SαS impulsive noise, and then the proposed algorithm has been implemented in an experimental ANC system. Experimental results show that the proposed scheme has good performance for SαS impulsive noise attenuation.

A procedure for the guaranteed cost control design of delayed bilateral teleoperation systems with nonlinear external forces is proposed. The assumption that the external forces are nonlinear functions of velocities and/or positions of local devices and that one part of these forces satisfies sector condition has been made. A stability criterion is formulated firstly and then the optimal guaranteed cost controller is obtained by solving the equivalent convex optimization problem in the form of linear matrix inequalities (LMIs). The controller preserves closed-loop stability regardless of the delay length. The behavior of the resulting teleoperation system is illustrated in simulations.

Restarting with speed for PMSM is critical to Rail Train application. And the switching frequencies must be restricted to several hundred hertz to minimize the switching losses. In order to output more torque with limited DC supply voltage, square wave should be used in the condition of high speeds. Hybrid synchronized PWM control schemes are often used to satisfy these restrictions. The restart and control schemes contain a variety of PWM patterns which is applicable to the whole speed range. And a simple but very effective method is proposed to determine the PWM patterns that should be used when restart is enabled. The motor speed feedback as a flag is employed in this method. An improved current controller for IPMSM is proposed to solve the zero speed start problem.

In this paper, an off-policy reinforcement learning method is developed for the robust stabilizing controller design of discrete-time uncertain linear systems. The proposed robust control design consists of two steps. First, the robust control problem is transformed to an optimal control problem. Second, the off-policy RL method is used to design the optimal control policy which guarantees the robust stability of the original system with uncertainty. The condition for the equivalence between the robust control problem and the optimal control problem is discussed. The off-policy does not require any knowledge of the system knowledge and efficiently utilize the data collected from on-line to improve the performance of approximate optimal control policy in each iteration successively. Finally, a simulation example is carried out to verify the effectiveness of the presented algorithm for the robust control problem of discrete-time linear system with uncertainty.

A procedure for acceleration feedback control of the delayed nonlinear teleoperation system is proposed. The acceleration feedback is applied to the master controller, while in the slave side the position error plus nonlinear damping controller is used to guarantee the input-to-state stability (ISS) of the controlled system. The Lyapunov–Krasovskii methodology is applied to analyse the ISS of the closed-loop system, and hence the passivity assumption for human operators and the environment is not required. The stability criterion is formulated in the form of linear matrix inequalities. The behaviour of the resulting teleoperation system is illustrated in simulations, while the simulation results show that the acceleration feedback controller not only preserves good position tracking performance but also eludes contact instability.

In view of the fact that the traditional genetic algorithm easily falls into local optimum in the late iterations, an improved chaos genetic algorithm employed chaos theory and genetic algorithm is presented to optimize the low side-lobe for T-shaped MIMO radar antenna array. The novel two-dimension Cat chaotic map has been put forward to produce its initial population, improving the diversity of individuals. The improved Tent map is presented for groups of individuals of a generation with chaos disturbance. Improved chaotic genetic algorithm optimization model is established. The algorithm presented in this paper not only improved the search precision, but also avoids effectively the problem of local convergence and prematurity. For MIMO radar, the improved chaos genetic algorithm proposed in this paper obtains lower side-lobe level through optimizing the exciting current amplitude. Simulation results show that the algorithm is feasible and effective. Its performance is superior to the traditional genetic algorithm.

The servo systems are always difficult to design with high-accuracy due to nonlinear stick-slip at the low velocities and the uncertainties of friction dynamic model. A linear active disturbance rejection control (ADRC) dynamic compensation solution is presented independent of the friction model. To deal with the problem of observing or tracking for unknown discontinuous signals of friction with Stribeck effect, a kind of linear extended state observer (LESO) algorithm is designed. Then a linear control law is proposed to compensate the friction disturbance dynamically. In comparison with proportional-integral-derivative (PID) controller, the simulation results show that the proposed method not only has better accuracy of the discontinuous friction signal tracking, but also ensures better dynamic friction compensation at the low velocities.

To deal with the problem of observing or tracking for discontinuous signal, a kind of second order linear extended state observer (LESO) algorithm is proposed. By ESO estimating the uncertain disturbance, a simple second-order linear ESO with less tunable parameters was designed in the absence of dynamic model, and the method of discrete time realization was given. The proposed LESO was applied to the tracking of discontinuous Stribeck Friction in comparison with adaptive state observer. Simulation results show that the proposed ESO is effective and practical.

This paper investigates the problem of nonfragile H∞ and H2 filter designs for continuous-time linear systems. Additive filter gain variations to reflect the imprecision in filter implementation are considered. The nonfragile filter design is first formulated as a robust convex optimization problem. Then, both deterministic and randomized algorithms are employed to solve the obtained robust convex optimization problem. Compared with the deterministic algorithm, the proposed randomized one has two advantages: On one hand, it has acceptable computational complexity for systems with high dimensions; on the other hand, it can alleviate the conservatism of deterministic algorithms. Several examples are given to illustrate the effectiveness of the proposed method.

This study deals with the estimation of the stability domain of the spacecraft attitude control system with delayed feedback. For autonomous functional–differential equations, by using the invariance principle, a general estimation theorem is established to explicitly estimate the stability domain. Furthermore, based on the estimation theorem and an applicable Lyapunov functional, an estimation criterion of the stability domain for the spacecraft attitude control system with delayed proportional-derivative (PD) controller is obtained. It is worth noting that the estimation criterion can be described as a generalised eigenvalue problem and thus can be solved by linear matrix inequality (LMI) optimisation techniques. Finally, numerical examples show the effectiveness of the estimation criterion.

This paper investigates the stabilization problem of discrete-time two-dimension (2-D) Takagi-Sugeno (T-S) fuzzy systems. Based on a novel non-parallel distributed compensation (non-PDC) control scheme combined with a new non-quadratic Lyapunov function, less conservative stabilization conditions are developed. The proposed non-quadratic Lyapunov function is homogeneous polynomially parameter-dependent on membership functions. As the degree of the Lyapunov function increases, the conservatism of the obtained stabilization conditions is gradually reduced. By exploiting the algebraic property of membership functions, the stabilization conditions approach to exactness in the sense of convergence. Compared with the existing methods, no slack variables are introduced in control synthesis, and hence the same or less conservative results can be obtained with a lower computational cost. A numerical example is given to illustrate the effectiveness of the proposed method.

Procedure for acceleration feedback control of teleoperation systems with asymmetric time-varying delays is proposed. The acceleration feedback is applied to the master controller, while in the slave side the position error plus damping controller is used. The ISS/IOS small-gain theory is applied to analyze the stability of the closed-loop system, and hence the passivity assumption for the human operator and the environment is not required. Simulation results are presented, which show that the resulting teleoperation system is stable with good tracking performance between the master and the slave.

This study investigates the problem of state feedback robust stabilisation for discrete-time fuzzy singularly perturbed systems (SPSs) with parameter uncertainty. The considered system is approximated by Takagi–Sugeno fuzzy model. Based on a matrix spectral norm approach, new sufficient conditions, which ensure the existence of state feedback controller such that the resulting closed-loop system is asymptotically stable, are given. The gains of controllers are obtained by solving a set of ɛ-independent linear matrix inequalities (LMIs) such that, the ill-conditioned problems caused by ɛ can be easily avoided. In contrast to the existing results, the proposed method can be applied to both certain and uncertain SPSs with greater singular perturbation parameter ɛ. A numerical example is provided to illustrate the reduced conservatism of the authors’ results.

This paper presents the novel results for stabilizing uncertain standard discrete-time fuzzy singularly perturbed systems (SPSs) via a state feedback control law. Two standard discrete-time fuzzy SPSs are constructed firstly by using the Takagi-Sugeno (T-S) fuzzy model. Based on a matrix spectral norm approach, two new e-dependent stability conditions are derived, which guarantee the resulting closed-loop systems are asymptotically stable. The gains of controllers are obtained by solving a set of e-dependent linear matrix inequalities (LMIs). In contrast to the existing results, the proposed methods have two advantages: (i) the designed controllers can overcome the external disturbances and parameter uncertainty; and (ii) the upper bound of e is improved, especially it is not required to be smaller than one. Examples are provided to illustrate the reduced conservatism of our results. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

This note proposes an adaptive fault diagnosis scheme for the continuous time-delay repetitive system subject to the sensor fault. The theory of iterative learning control(ILC) is employed in the fault reconstructed method. With the increase of iterative step, the sensor fault estimation signal can track the actual fault tightly including slow- and fast- varying fault. In order to verify the effectiveness of the proposed fault diagnosis method, three kinds of sensor faults are taken into consideration in the simulation part. The results show that the reconstructed fault signal can track the actual fault well despite of the kinds of sensor faults.

Due to the nonlinearities, model uncertainties and disturbances of the two-tank liquid level cascade system, active disturbance rejection controller (ADRC) solution was developed. First, a nonlinear mechanism model and its linearized model were built based on the theories of fluid dynamics for the system. Second, first-order linear ADRCs for the outer loop of the two cascade systems with an unknown order were designed, respectively. With a simple integrator as the normalized type of the feedback system, other parts of dynamic system which are different from the normalized type are treated as the total disturbances. The disturbances were estimated by the second-order linear extended state observer and compensated in the control law. Then the system, with nonlinearities, uncertainties and disturbances, is reduced to only a linear integrator. In comparison with the traditional PI controller, simulation results show that both the proposed ADRCs have better robustness and adaptability against model uncertainties.

A novel active disturbance rejection control (ADRC) solution and a particular tuning method are presented for a class of time delay system (TDS) with uncertainty. First, the complicated process dynamics is modeled as a simple first order plus large time delay (FOPTD) plant, with the difference between the actual dynamics and its model treated as disturbances to be rejected. Then the reduced order linear extended state observer (RLESO) with input delay is proposed to estimate the time delay state and disturbance. It is shown how the time delay could be eliminated from the characteristic equation of the closed-loop system by manipulations of controller parameters. Secondly, the one parameter tuning (OPT) technique is developed where all controller parameters are made function of a single coefficient. In comparison with optimal proportional-integral-derivative (PID) controller and twice optimum controller (TOC), the simulation results show that the proposed method not only has better accuracy and faster response, but also ensures better robustness and adaptability against uncertain model parameters and external disturbances, especially for the plant with very large time delays.

Due to the nonlinearities, disturbances and uncertainties of the single water tank system, an active disturbance rejection control (ADRC) solution with high-precision was proposed. Firstly, a nonlinear mechanism model was built based on the theories of fluid dynamics for water tank system. Then, the nonlinear model was linearized in static work point to get its simplified model. Secondly, first-order linear ADRC solutions for the system models with unknown order were designed. With a simple “integrator series” as the normalized type of the feedback system, the parts of dynamic system is different from the normalized type are treated as the total disturbance. So it was estimated by the second-order linear extended state observer and canceled out in the control law. The system, with disturbances, uncertainties and nonlinearities, is reduced to the canonical form of the linear chain of integrators. Simulation results show that both the ADRCs have better performances than PID controller on disturbance-rejection and robustness.

This paper provides a tutorial introduction and overview of design techniques for a system with both parametric uncertainty and unmodeled uncertainty, using mixed μ-synthesis. This paper also includes LFTs representation for modeling and an example of two-cart mass-spring-damper system (MSDs) is used to analyze its robust stability and performance, based on mixed μ-synthesis.