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Showing papers on "Control theory published in 2020"


Journal ArticleDOI
21 Oct 2020
TL;DR: The presented work indicates that robust locomotion in natural environments can be achieved by training in simple domains.
Abstract: Legged locomotion can extend the operational domain of robots to some of the most challenging environments on Earth. However, conventional controllers for legged locomotion are based on elaborate state machines that explicitly trigger the execution of motion primitives and reflexes. These designs have increased in complexity but fallen short of the generality and robustness of animal locomotion. Here, we present a robust controller for blind quadrupedal locomotion in challenging natural environments. Our approach incorporates proprioceptive feedback in locomotion control and demonstrates zero-shot generalization from simulation to natural environments. The controller is trained by reinforcement learning in simulation. The controller is driven by a neural network policy that acts on a stream of proprioceptive signals. The controller retains its robustness under conditions that were never encountered during training: deformable terrains such as mud and snow, dynamic footholds such as rubble, and overground impediments such as thick vegetation and gushing water. The presented work indicates that robust locomotion in natural environments can be achieved by training in simple domains.

575 citations


Journal ArticleDOI
TL;DR: This paper proposes a multi-stage procedure that estimates a model from a few experimental trials, estimates the error in that model with respect to the truth, and then designs a controller using both the model and uncertainty estimate, and provides end-to-end bounds on the relative error in control cost.
Abstract: This paper addresses the optimal control problem known as the linear quadratic regulator in the case when the dynamics are unknown. We propose a multistage procedure, called Coarse-ID control, that estimates a model from a few experimental trials, estimates the error in that model with respect to the truth, and then designs a controller using both the model and uncertainty estimate. Our technique uses contemporary tools from random matrix theory to bound the error in the estimation procedure. We also employ a recently developed approach to control synthesis called System Level Synthesis that enables robust control design by solving a quasi-convex optimization problem. We provide end-to-end bounds on the relative error in control cost that are optimal in the number of parameters and that highlight salient properties of the system to be controlled such as closed-loop sensitivity and optimal control magnitude. We show experimentally that the Coarse-ID approach enables efficient computation of a stabilizing controller in regimes where simple control schemes that do not take the model uncertainty into account fail to stabilize the true system.

489 citations


Journal ArticleDOI
TL;DR: This work describes a principled way of formulating the chance-constrained MPC problem, which takes into account residual uncertainties provided by the GP model to enable cautious control and presents a model predictive control approach that integrates a nominal system with an additive nonlinear part of the dynamics modeled as a GP.
Abstract: Gaussian process (GP) regression has been widely used in supervised machine learning due to its flexibility and inherent ability to describe uncertainty in function estimation. In the context of control, it is seeing increasing use for modeling of nonlinear dynamical systems from data, as it allows the direct assessment of residual model uncertainty. We present a model predictive control (MPC) approach that integrates a nominal system with an additive nonlinear part of the dynamics modeled as a GP. We describe a principled way of formulating the chance-constrained MPC problem, which takes into account residual uncertainties provided by the GP model to enable cautious control. Using additional approximations for efficient computation, we finally demonstrate the approach in a simulation example, as well as in a hardware implementation for autonomous racing of remote-controlled race cars with fast sampling times of 20 ms, highlighting improvements with regard to both performance and safety over a nominal controller.

383 citations


Posted Content
12 Dec 2020
TL;DR: This paper proposes a novel parameter searching approach by utilizing uniform design (UD) algorithm, by which the satisfactory controller parameters under a performance index could be selected.
Abstract: Parameter selection is one of the most important parts for nearly all the control strategies. Traditionally, controller parameters are chosen by utilizing trial and error, which is always tedious and time consuming. Moreover, such method is highly dependent on the experience of researchers, which means that it is hard to be popularized. In this light, this paper proposes a novel parameter searching approach by utilizing uniform design (UD) algorithm. By which the satisfactory controller parameters under a performance index could be selected. In this end, two simulation examples are conducted to verify the effectiveness of proposed scheme. Simulation results show that this novel approach, as compared to other intelligent tuning algorithms, excels in efficiency and time saving.

362 citations


Journal ArticleDOI
TL;DR: The proposed MPC algorithm with Hammerstein model in this paper can ensure that the UAV exactly tracking the target while maintaining stability, even with external disturbances.

323 citations


Journal ArticleDOI
TL;DR: The Lyapunov stability theory is introduced to demonstrate that the adaptive controller achieves the desired control goals and a numerical simulation is performed which verifies the significance and feasibility of the presented control scheme.
Abstract: This paper presents an adaptive control method for a class of uncertain strict-feedback switched nonlinear systems. First, we consider the constraint characteristics in the switched nonlinear systems to ensure that all states in switched systems do not violate the constraint ranges. Second, we design the controller based on the backstepping technique, while integral Barrier Lyapunov functions (iBLFs) are adopted to solve the full state constraint problems in each step in order to realize the direct constraints on state variables. Furthermore, we introduce the Lyapunov stability theory to demonstrate that the adaptive controller achieves the desired control goals. Finally, we perform a numerical simulation, which further verifies the significance and feasibility of the presented control scheme.

323 citations


Journal ArticleDOI
TL;DR: The intermittent fault-tolerance scheme is taken into fully account in designing a reliable asynchronous sampled-data controller, which ensures such that the resultant neural networks is asymptotically stable.

313 citations


Journal ArticleDOI
TL;DR: This article proposes a memory-based event-triggering load frequency control (LFC) method for power systems through a bandwidth-constrained open network, which couples the effects of METS and random deception attacks in a unified framework.
Abstract: This article proposes a memory-based event-triggering $H_{\infty }$ load frequency control (LFC) method for power systems through a bandwidth-constrained open network. To overcome the bandwidth constraint, a memory-based event-triggered scheme (METS) is first proposed to reduce the number of transmitted packets. Compared with the existing memoryless event-triggered schemes, the proposed METS has the advantage to utilize series of the latest released signals. To deal with the random deception attacks induced by open networks, a networked power system model is well established, which couples the effects of METS and random deception attacks in a unified framework. Then, a sufficient stabilization criterion is derived to obtain the memory $H_{\infty }$ LFC controller gains and event-triggered parameters simultaneously. Compared with existing memoryless LFC, the control performance is greatly improved since the latest released dynamic information is well utilized. Finally, an illustrative example is used to show the effectiveness of the proposed method.

270 citations


Journal ArticleDOI
TL;DR: A finite-time controller, which is capable of ensuring the semiglobal practical finite- time stability for the closed-loop systems, is developed using the adaptive neural networks control method, adding one power integrator technique and backstepping scheme.
Abstract: This article addresses the finite-time optimal control problem for a class of nonlinear systems whose powers are positive odd rational numbers. First of all, a finite-time controller, which is capable of ensuring the semiglobal practical finite-time stability for the closed-loop systems, is developed using the adaptive neural networks (NNs) control method, adding one power integrator technique and backstepping scheme. Second, the corresponding design parameters are optimized, and the finite-time optimal control property is obtained by means of minimizing the well-defined and designed cost function. Finally, a numerical simulation example is given to further validate the feasibility and effectiveness of the proposed optimal control strategy.

269 citations


Journal ArticleDOI
TL;DR: It is shown that all the signals are bounded, and the consensus tracking errors are located in a small neighborhood of the origin based on the Lyapunov stability theory and backstepping approach and is proved by simulation results.
Abstract: This paper considers the event-triggered tracking control problem of nonlinear multiagent systems with unknown disturbances. The event-triggering mechanism is considered in the controller update, which decreases the amount of communication and reduces the frequency of the controller update in practice. By designing a disturbance observer, the unknown external disturbances are estimated. Moreover, a part of adaptive parameters are only dependent on the number of followers, which weakens the computational burden. It is shown that all the signals are bounded, and the consensus tracking errors are located in a small neighborhood of the origin based on the Lyapunov stability theory and backstepping approach. Finally, the effectiveness of the approach proposed in this paper is proved by simulation results.

257 citations


Journal ArticleDOI
TL;DR: The problem of asymptotic tracking control for a class of uncertain switched nonlinear systems under fuzzy approximation framework is solved by constructing a nonsmooth Lyapunov function and introducing a novel discontinuous controller with dynamic feedback compensator in the design procedure.
Abstract: The problem of asymptotic tracking control for a class of uncertain switched nonlinear systems under fuzzy approximation framework is solved in this paper. Superior to most existing results based on fuzzy adaptive control strategy that can only achieve bounded error tracking performance, our proposed control scheme can guarantee the local asymptotic tracking performance for the uncertain switched nonlinear systems under consideration. This is accomplished by constructing a nonsmooth Lyapunov function and introducing a novel discontinuous controller with dynamic feedback compensator in the design procedure. Meanwhile, some concepts, such as differential inclusion and set-valued map, are introduced to theoretically verify the local asymptotic tracking performance of the systems with our proposed controller. With the help of set-valued Lie derivative, the common virtual control functions, the desired controller, and the adaptive laws can be precisely constructed. Finally, simulation results are given to show the effectiveness of the proposed method.

Journal ArticleDOI
TL;DR: A linear-matrix-inequality-based criterion is provided to design stabilizing state-feedback controllers against DoS attacks and a satellite control system is given to demonstrate the effectiveness of the proposed method.
Abstract: This article is concerned with designing resilient state feedback controllers for a class of networked control systems under denial-of-service (DoS) attacks. The sensor samples system states periodically. The DoS attacks usually prevent those sampled signals from being transmitted through a communication network. A logic processor embedded in the controller is introduced to not only receive sampled signals but also capture information on the duration time of each DoS attack. Note that the duration time of DoS attacks is usually both lower and upper bounded. Then the closed-loop system is modeled as an aperiodic sampled-data system closely related to both lower and upper bounds of duration time of DoS attacks. By introducing a novel looped functional, which caters for the $N$ -order canonical Bessel–Legendre inequalities, some $N$ -dependent stability criteria are presented for the resultant closed-loop system. It is worth pointing out that a number of identity formulas are uncovered, which enable us to apply the notable free-weighting matrix approach to derive less conservative stability criteria. A linear-matrix-inequality-based criterion is provided to design stabilizing state-feedback controllers against DoS attacks. A satellite control system is given to demonstrate the effectiveness of the proposed method.

Journal ArticleDOI
TL;DR: A design-oriented transient stability analysis of the grid-forming VSCs is presented, revealing that the PSC and the basic droop control can retain a stable operation as long as there are equilibrium points, due to their noninertial transient responses, while thedroop control with LPFs and the VSG control can be destabilized even if the equilibrium points exist.
Abstract: Driven by the large-scale integration of distributed power resources, grid-connected voltage-source converters (VSCs) are increasingly required to operate as grid-forming units to regulate the system voltage/frequency and emulate the inertia. While various grid-forming control schemes have been reported, their transient behaviors under large-signal disturbances are still not fully explored. This article addresses this issue by presenting a design-oriented transient stability analysis of the grid-forming VSCs. First, four typical grid-forming control schemes, namely, the power-synchronization control (PSC), the basic droop control, the droop control with low-pass filters (LPFs), and the virtual synchronous generator (VSG) control, are systematically reviewed, whose dynamics are characterized by a general large-signal model. Based on this model, a comparative analysis on the transient stabilities of different control schemes is then carried out. It reveals that the PSC and the basic droop control can retain a stable operation as long as there are equilibrium points, due to their noninertial transient responses, while the droop control with LPFs and the VSG control can be destabilized even if the equilibrium points exist, due to the lack of damping on their inertial transient responses. With the phase portrait, the underlying stability mechanism is explicitly elaborated, and the quantitative impacts of the controller gains and the virtual inertia are clearly identified. Subsequently, controller design guidelines are proposed to enhance the system damping as well as the transient stability. Finally, experimental results are provided to verify the theoretical analysis.

Journal ArticleDOI
Xiao-Meng Li1, Qi Zhou1, Panshuo Li1, Hongyi Li1, Renquan Lu1 
TL;DR: The main objective of this article is to design a controller such that, under randomly occurring FDIAs and admissible parameter uncertainties, the MASs achieve consensus by utilizing stochastic analysis method.
Abstract: In this article, the event-triggered security consensus problem is studied for time-varying multiagent systems (MASs) against false data-injection attacks (FDIAs) and parameter uncertainties over a given finite horizon. In the process of information transmission, the malicious attacker tries to inject false signals to destroy consensus by compromising the integrity of measurements and control signals. The randomly occurring stealthy FDIAs on sensors and actuators are modeled by the Bernoulli processes. In order to reduce the unnecessary utilization of communication resources, an event-triggered control mechanism with state-dependent threshold is adopted to update the control input signal. The main objective of this article is to design a controller such that, under randomly occurring FDIAs and admissible parameter uncertainties, the MASs achieve consensus. By utilizing stochastic analysis method, two sufficient criteria are derived to ensure that the prescribed $H_{\infty }$ consensus performance can be achieved. Then, the desired controller gains are derived by solving recursive linear matrix inequalities. Simulation results are presented to illustrate the effectiveness and applicability of the proposed control method.

Journal ArticleDOI
TL;DR: A novel distributed-reference-observer-based fault-tolerant tracking control approach is established, under which the global tracking errors are proved to be asymptotically convergent in the presence of actuator failures.
Abstract: In this paper, for linear leader–follower networks with multiple heterogeneous actuator faults, including partial loss of effectiveness fault and actuator bias fault, a cooperative fault-tolerant control (CFTC) approach is developed. Assume that the interaction network topology among all nodes is a switching directed graph. To address the difficulty of designing the distributed compensation control laws under the time-varying asymmetrical network structure, a novel distributed-reference-observer-based fault-tolerant tracking control approach is established, under which the global tracking errors are proved to be asymptotically convergent in the presence of actuator failures. First, by constructing a group of distributed reference observers based on neighborhood state information, all followers can estimate the leader’s state trajectories directly. Second, a decentralized adaptive fault-tolerant tracking controller via local estimation is designed to achieve the global synchronization. Furthermore, the reliable coordination problem under switching directed topology with intermittent communications is solved by utilizing the presented CFTC approach. Finally, the effectiveness of the proposed coordination control protocol is illustrated by its applications to a networked aircraft system.

Journal ArticleDOI
TL;DR: This paper considers the problem of unknown gains and input quantization, which can be addressed by using a lemma and Nussbaum function in cooperative control, and fuzzy logic systems are proposed to approximate the nonlinear function defined on a compact set.
Abstract: This paper studies the quantized cooperative control problem for multiagent systems with unknown gains in the prescribed performance. Different from the finite-time control, a speed function is designed to realize that the tracking errors converge to a prescribed compact set in a given finite time for multiagent systems. Meanwhile, we consider the problem of unknown gains and input quantization, which can be addressed by using a lemma and Nussbaum function in cooperative control. Moreover, the fuzzy logic systems are proposed to approximate the nonlinear function defined on a compact set. A distributed controller and adaptive laws are constructed based on the Lyapunov stability theory and backstepping method. Finally, the effectiveness of the proposed approach is illustrated by some numerical simulation results.

Journal ArticleDOI
TL;DR: The fuzzy control and adaptive backstepping schemes are applied to construct an improved fault-tolerant controller without requiring the specific knowledge of control gains and actuator faults, including both stuck constant value and loss of effectiveness.
Abstract: This paper addresses the trajectory tracking control problem of a class of nonstrict-feedback nonlinear systems with the actuator faults. The functional relationship in the affine form between the nonlinear functions with whole state and error variables is established by using the structure consistency of intermediate control signals and the variable-partition technique. The fuzzy control and adaptive backstepping schemes are applied to construct an improved fault-tolerant controller without requiring the specific knowledge of control gains and actuator faults, including both stuck constant value and loss of effectiveness. The proposed fault-tolerant controller ensures that all signals in the closed-loop system are semiglobally practically finite-time stable and the tracking error remains in a small neighborhood of the origin after a finite period of time. The developed control method is verified through two numerical examples.

Journal ArticleDOI
TL;DR: This article investigates the switching-like event-triggered control for networked control systems (NCSs) under the malicious denial of service (DoS) attacks and a networked invert pendulum on a cart is conducted to show the effectiveness of the proposed method.
Abstract: This article investigates the switching-like event-triggered control for networked control systems (NCSs) under the malicious denial of service (DoS) attacks. First, by dividing the DoS attacks into S-interval (DoS-free case) and D-interval (DoS case), a switching-like event-triggered communication scheme (SETC) is well designed to deal with intermittent DoS attacks to improve communication efficiency while keeping the desired control performance. Second, by considering the SETC and NCSs into a unified framework, the studied system is transferred into a time-delay system. Then, under the constraint of the number of maximum allowable data dropouts induced by DoS attacks, a stability criterion and a stabilization criterion are derived, which can be used to estimate the event-triggered communication parameters and obtain the security controller gain simultaneously. Moreover, the derived stabilization criterion can also provide a tradeoff to balance communication efficiency and $H_{\infty }$ control performance. At last, a networked invert pendulum on a cart is conducted to show the effectiveness of the proposed method.

Journal ArticleDOI
TL;DR: An adaptive neuro-fuzzy inference system–particle swarm optimization (ANFIS–PSO)-based hybrid MPPT method to acquire rapid and maximal PV power with zero oscillation tracking is introduced.
Abstract: To enhance the photovoltaic (PV) power-generation conversion, maximum power point tracking (MPPT) is the foremost constituent. This article introduces an adaptive neuro-fuzzy inference system–particle swarm optimization (ANFIS–PSO)-based hybrid MPPT method to acquire rapid and maximal PV power with zero oscillation tracking. The inverter control strategy is implemented by a space vector modulation hysteresis current controller to get quality inverter current by tracking accurate reference sine-shaped current. The ANFIS–PSO-based MPPT method has no extra sensor requirement for measurement of irradiance and temperature variables. The employed methodology delivers remarkable driving control to enhance PV potential extraction. An ANFIS–PSO-controlled Zeta converter is also modeled as an impedance matching interface with zero output harmonic agreement and kept between PV modules and load regulator power circuit to perform MPPT action. The attainment of recommended hybrid ANFIS–PSO design is equated with perturb and observe, PSO, ant colony optimization, and artificial bee colony MPPT methods for the PV system. The practical validation of the proposed grid-integrated PV system is done through MATLAB interfaced dSPACE interface and the obtained responses accurately justify the proper design of control algorithms employed with superior performance.

Journal ArticleDOI
TL;DR: A high-order tan-type barrier Lyapunov function (BLF) is constructed to handle the full-state constraints of the control systems and by the BLF and combining a backstepping design technique, an adding a power integrator, and a fuzzy control, the proposed approach can control high- order uncertain nonlinear system with full- state constraints.
Abstract: This paper focuses on the practical output tracking control for a category of high-order uncertain nonlinear systems with full-state constraints. A high-order tan-type barrier Lyapunov function (BLF) is constructed to handle the full-state constraints of the control systems. By the BLF and combining a backstepping design technique, an adding a power integrator, and a fuzzy control, the proposed approach can control high-order uncertain nonlinear system with full-state constraints. A novel controller is designed to ensure that the tracking errors approach to an arbitrarily small neighborhood of zero, and the constraints on system states are not violated. The numerical example demonstrates effectiveness of the proposed control method.

Journal ArticleDOI
TL;DR: It is shown that the proposed results can establish a quantitative relationship among the launching/sleeping periods of the attacks, the event-triggering parameters, the sampling period, and the exponential decay rate.
Abstract: This paper is concerned with the observer-based event-triggered control for a continuous networked linear system subject to denial-of-service (DoS) attacks, where the attacks are launched periodically to block the data transmission in control channels. First, a new observer state-based resilient event-triggering scheme is developed in the presence of DoS attacks. Second, a novel event-based switched system model is established by considering the effect of the event-triggering scheme and DoS attacks simultaneously. By virtue of this new model combined with a piecewise Lyapunov–Krasovskii functional method, the sufficient conditions are derived to guarantee exponential stability of the resulting switched system. It is shown that the proposed results can establish a quantitative relationship among the launching/sleeping periods of the attacks, the event-triggering parameters, the sampling period, and the exponential decay rate. Third, criteria for designing a desired observer-based event-triggered controller are provided and expressed in terms of a set of linear matrix inequalities. Finally, an offshore structure model is presented to illustrate the efficiency of the developed control method.

Journal ArticleDOI
TL;DR: The experimental and simulation results are presented to demonstrate the improved performance in tracking accuracy, steering smoothness, and computational efficiency compared to the MPC and the full-state feedback control.
Abstract: This paper presents a preview steering control algorithm and its closed-loop system analysis and experimental validation for accurate, smooth, and computationally inexpensive path tracking of automated vehicles. The path tracking issue is formulated as an optimal control problem with dynamic disturbance, i.e., the future road curvature. A discrete-time preview controller is then designed on the top of a linear augmented error system, in which the disturbances within a finite preview window are augmented as part of the state vector. The obtained optimal steering control law is in an analytic form and consists of two parts: 1) a feedback control responding to tracking errors and 2) a feedforward control dealing with the future road curvatures. The designed control’s nature, capacity, computation load, and underlying mechanism are revealed by the analysis of system responses in the time domain and the frequency domain, theoretical steady-state error, and comparison with the model predictive control (MPC). The algorithm was implemented on an automated vehicle platform, a hybrid Lincoln MKZ. The experimental and simulation results are then presented to demonstrate the improved performance in tracking accuracy, steering smoothness, and computational efficiency compared to the MPC and the full-state feedback control.

Journal ArticleDOI
TL;DR: This article studies the observer-based output feedback control problem for a class of cyber-physical systems with periodic denial-of-service (DoS) attacks, where the attacks coexist both in the measurement and control channels in the network scenario.
Abstract: This article studies the observer-based output feedback control problem for a class of cyber-physical systems with periodic denial-of-service (DoS) attacks, where the attacks coexist both in the measurement and control channels in the network scenario. The periodic DoS attacks are characterized by a cyclic dwell-time switching strategy, such that the resulting augmented system can be converted into a class of discrete-time cyclic dwell-time switched systems including a stable subsystem and an unstable subsystem. By means of a cyclic piecewise linear Lyapunov function approach, the exponential stability and $l_2$ -gain analysis, and observer-based controller design are carried out for the augmented discrete-time cyclic switched system. Then, the desired observer and controller gains in piecewise linear form are determined simultaneously so as to ensure that the resulting closed-loop system is exponentially stable with a prescribed $\mathcal {H}_{\infty }$ performance index. Finally, a practical application of unmanned ground vehicles under periodic DoS attacks is provided to verify the effectiveness of the developed control approach.

Journal ArticleDOI
TL;DR: It is proved that all states of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB) by utilizing the Lyapunov stability principles.
Abstract: In this article, an admittance-based controller for physical human–robot interaction (pHRI) is presented to perform the coordinated operation in the constrained task space. An admittance model and a soft saturation function are employed to generate a differentiable reference trajectory to ensure that the end-effector motion of the manipulator complies with the human operation and avoids collision with surroundings. Then, an adaptive neural network (NN) controller involving integral barrier Lyapunov function (IBLF) is designed to deal with tracking issues. Meanwhile, the controller can guarantee the end-effector of the manipulator limited in the constrained task space. A learning method based on the radial basis function NN (RBFNN) is involved in controller design to compensate for the dynamic uncertainties and improve tracking performance. The IBLF method is provided to prevent violations of the constrained task space. We prove that all states of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB) by utilizing the Lyapunov stability principles. At last, the effectiveness of the proposed algorithm is verified on a Baxter robot experiment platform. Note to Practitioners —This work is motivated by the neglect of safety in existing controller design in physical human–robot interaction (pHRI), which exists in industry and services, such as assembly and medical care. It is considerably required in the controller design for rigorously handling constraints. Therefore, in this article, we propose a novel admittance-based human–robot interaction controller. The developed controller has the following functionalities: 1) ensuring reference trajectory remaining in the constrained task space: a differentiable reference trajectory is shaped by the desired admittance model and a soft saturation function; 2) solving uncertainties of robotic dynamics: a learning approach based on radial basis function neural network (RBFNN) is involved in controller design; and 3) ensuring the end-effector of the manipulator remaining in the constrained task space: different from other barrier Lyapunov function (BLF), integral BLF (IBLF) is proposed to constrain system output directly rather than tracking error, which may be more convenient for controller designers. The controller can be potentially applied in many areas. First, it can be used in the rehabilitation robot to avoid injuring the patient by limiting the motion. Second, it can ensure the end-effector of the industrial manipulator in a prescribed task region. In some industrial tasks, dangerous or damageable tools are mounted on the end-effector, and it will hurt humans and bring damage to the robot when the end-effector is out of the prescribed task region. Third, it may bring a new idea to the designed controller for avoiding collisions in pHRI when collisions occur in the prescribed trajectory of end-effector.

Journal ArticleDOI
TL;DR: A fault-mode controller is proposed which keeps the voltage-mode characteristics of the grid-forming structure while simultaneously limiting the converter currents to an admissible value and is evaluated in a detailed simulation model and verified through an experimental test setup.
Abstract: With an increasing capacity in the converter-based generation to the modern power system, a growing demand for such systems to be more grid-friendly has emerged. Consequently, grid-forming converters have been proposed as a promising solution as they are compatible with the conventional synchronous-machine-based power system. However, most research focuses on the grid-forming control during normal operating conditions without considering the fundamental distinction between a grid-forming converter and a synchronous machine when considering its short-circuit capability. The current limitation of grid-forming converters during fault conditions is not well described in the available literature and present solutions often aim to switch the control structure to a grid-following structure during the fault. Yet, for a future converter-based power system with no or little integration of synchronous machines, the converters need to preserve their voltage-mode characteristics and be robust toward weak-grid conditions. To address this issue, this article discusses the fundamental issue of grid-forming converter control during grid fault conditions and proposes a fault-mode controller which keeps the voltage-mode characteristics of the grid-forming structure while simultaneously limiting the converter currents to an admissible value. The proposed method is evaluated in a detailed simulation model and verified through an experimental test setup.

Journal ArticleDOI
TL;DR: A fuzzy sliding-mode controller is developed to realize reachability of a predefined switching surface and desirable sliding motion and sufficient conditions for stochastic stability of the obtained sliding mode dynamics is developed in the sense of generally uncertain transition rates.
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.

Journal ArticleDOI
27 Feb 2020
TL;DR: A novel controller which combines feedforward contact forces computed from a kino-dynamic optimizer with impedance control of the center of mass and base orientation is presented, which can regulate complex motions while being robust to environmental uncertainty.
Abstract: We present a new open-source torque-controlled legged robot system, with a low-cost and low-complexity actuator module at its core. It consists of a high-torque brushless DC motor and a low-gear-ratio transmission suitable for impedance and force control. We also present a novel foot contact sensor suitable for legged locomotion with hard impacts. A 2.2 kg quadruped robot with a large range of motion is assembled from eight identical actuator modules and four lower legs with foot contact sensors. Leveraging standard plastic 3D printing and off-the-shelf parts results in a lightweight and inexpensive robot, allowing for rapid distribution and duplication within the research community. We systematically characterize the achieved impedance at the foot in both static and dynamic scenarios, and measure a maximum dimensionless leg stiffness of 10.8 without active damping, which is comparable to the leg stiffness of a running human. Finally, to demonstrate the capabilities of the quadruped, we present a novel controller which combines feedforward contact forces computed from a kino-dynamic optimizer with impedance control of the center of mass and base orientation. The controller can regulate complex motions while being robust to environmental uncertainty.

Journal ArticleDOI
TL;DR: This paper addresses the problem of adaptive neural output-feedback decentralized control for a class of strongly interconnected nonlinear systems suffering stochastic disturbances with an observer-based adaptive backstepping decentralized controller developed.
Abstract: This paper addresses the problem of adaptive neural output-feedback decentralized control for a class of strongly interconnected nonlinear systems suffering stochastic disturbances. An state observer is designed to approximate the unmeasurable state signals. Using the approximation capability of radial basis function neural networks (NNs) and employing classic adaptive control strategy, an observer-based adaptive backstepping decentralized controller is developed. In the control design process, NNs are applied to model the uncertain nonlinear functions, and adaptive control and backstepping are combined to construct the controller. The developed control scheme can guarantee that all signals in the closed-loop systems are semiglobally uniformly ultimately bounded in fourth-moment. The simulation results demonstrate the effectiveness of the presented control scheme.

Journal ArticleDOI
TL;DR: The proposed ESO-based adaptive controller theoretically achieves an excellent asymptotic tracking performance when time-invariant modeling uncertainties exist and preserves the performance results of both control methods while overcoming their practical performance limitations.
Abstract: Velocity signal is difficult to obtain in practical electrohydraulic servomechanisms Even though it can be approximately derived via numerical differentiation on position measurement, the strong noise effect will greatly deteriorate the achievable control performance Hence, how to design a high-performance tracking controller without velocity measurement is of practical significance In this paper, a practical adaptive tracking controller without velocity measurement is proposed for electrohydraulic servomechanisms To estimate the unmeasurable velocity signal, an extended state observer (ESO) that also provides an estimate of the mismatched disturbance is constructed The ESO uses the unknown parameter estimates updated by a novel adaptive law, which only depends on the actual position and desired trajectory Moreover, the matched parametric uncertainty is also handled by online parameter adaptation and the matched disturbance is suppressed via a robust control law The proposed ESO-based adaptive controller theoretically achieves an excellent asymptotic tracking performance when time-invariant modeling uncertainties exist In the presence of time-variant modeling uncertainties, guaranteed transient performance and prescribed final tracking accuracy can also be achieved The proposed control strategy bridges the gap between the adaptive control and disturbance observer-based control without using the velocity signal and preserves the performance results of both control methods while overcoming their practical performance limitations Comparative experiments are performed on an actual servovalve-controlled double-rod hydraulic actuator to verify the superiority of the proposed control strategy

Journal ArticleDOI
TL;DR: With the proposed two-layer distributed hierarchical controller, the leader-following output consensus is achieved and the obtained result is further extended to the formation control problem.
Abstract: In this paper, the leader-following output consensus problem for a class of uncertain nonlinear multiagent systems with unknown control directions is investigated. Each agent system has nonidentical dynamics and is subject to external disturbances and uncertain parameters. The agents are connected through a directed and jointly connected switching network. A novel two-layer distributed hierarchical control scheme is proposed. In the upper layer, to save the communication resources and to handle the switching networks, an event-triggered communication scheme is proposed, and a Zeno-free event-triggered mechanism is designed for each agent to generate the asynchronous triggering time instants. Furthermore, to avoid the continuous monitoring of the system states, a Zeno-free self-triggering algorithm is proposed. In the lower layer, to handle the unknown control directions problem and to achieve the output tracking of the local references generated in the upper layer, the Nussbaum-type function-based technique is combined with internal model principle. With the proposed two-layer distributed hierarchical controller, the leader-following output consensus is achieved. The obtained result is further extended to the formation control problem. Finally, three numerical examples are provided to demonstrate the effectiveness of the proposed theoretical results.