Showing papers on "Control theory published in 2018"
Posted Content•
TL;DR: Efficient Neural Architecture Search is a fast and inexpensive approach for automatic model design that establishes a new state-of-the-art among all methods without post-training processing and delivers strong empirical performances using much fewer GPU-hours.
Abstract: We propose Efficient Neural Architecture Search (ENAS), a fast and inexpensive approach for automatic model design. In ENAS, a controller learns to discover neural network architectures by searching for an optimal subgraph within a large computational graph. The controller is trained with policy gradient to select a subgraph that maximizes the expected reward on the validation set. Meanwhile the model corresponding to the selected subgraph is trained to minimize a canonical cross entropy loss. Thanks to parameter sharing between child models, ENAS is fast: it delivers strong empirical performances using much fewer GPU-hours than all existing automatic model design approaches, and notably, 1000x less expensive than standard Neural Architecture Search. On the Penn Treebank dataset, ENAS discovers a novel architecture that achieves a test perplexity of 55.8, establishing a new state-of-the-art among all methods without post-training processing. On the CIFAR-10 dataset, ENAS designs novel architectures that achieve a test error of 2.89%, which is on par with NASNet (Zoph et al., 2018), whose test error is 2.65%.
579 citations
TL;DR: A new fairly comprehensive system model, semi-Markov jump system with singular perturbations, which is more general than Markov jump model is employed to describe the phenomena of random abrupt changes in structure and parameters of the systems.
Abstract: The slow state variables feedback stabilization problem for semi-Markov jump discrete-time systems with slow sampling singular perturbations is discussed in this work. A new fairly comprehensive system model, semi-Markov jump system with singular perturbations, which is more general than Markov jump model, is employed to describe the phenomena of random abrupt changes in structure and parameters of the systems. Based on a slow state variables feedback control scheme, a novel technique to design the desired controller is presented and the allowed maximum of singular perturbation parameter can be calculated. With the help of the discrete-time semi-Markov kernel approach, some sojourn-time-dependent and less-conservative sufficient conditions are established via a novel matrix decoupling technique to ensure the solvability of the problem to be addressed. Finally, an illustrative example is given to show the superiority and usefulness of the proposed method.
400 citations
TL;DR: This paper is concerned with the security control problem with quadratic cost criterion for a class of discrete-time stochastic nonlinear systems subject to deception attacks, and proposes an easy-solution version on above inequalities to obtain both the controller gain and the upper bound.
Abstract: This paper is concerned with the security control problem with quadratic cost criterion for a class of discrete-time stochastic nonlinear systems subject to deception attacks A definition of security in probability is adopted to account for the transient dynamics of controlled systems The purpose of the problem under consideration is to design a dynamic output feedback controller such that the prescribed security in probability is guaranteed while obtaining an upper bound of the quadratic cost criterion First of all, some sufficient conditions with the form of matrix inequalities are established in the framework of the input-to-state stability in probability Then, an easy-solution version on above inequalities is proposed by carrying out the well-known matrix inverse lemma to obtain both the controller gain and the upper bound Furthermore, the main results are shown to be extendable to the case of discrete-time stochastic linear systems Finally, two simulation examples are utilized to illustrate the usefulness of the proposed controller design scheme
364 citations
TL;DR: The aim of this paper is to synthesize a controller via an event-triggered communication scheme such that not only the resulting closed-loop system is finite-time bounded and satisfies a prescribed performance level, but also the communication burden is reduced.
Abstract: This paper investigates the finite-time event-triggered $\mathcal{H}_{\infty }$ control problem for Takagi–Sugeno Markov jump fuzzy systems. Because of the sampling behaviors and the effect of network environment, the premise variables considered in this paper are subject to asynchronous constraints. The aim of this paper is to synthesize a controller via an event-triggered communication scheme such that not only the resulting closed-loop system is finite-time bounded and satisfies a prescribed $\mathcal{H}_{\infty }$ performance level, but also the communication burden is reduced. First, a sufficient condition is established for the finite-time bounded $\mathcal{H} _{\infty }$ performance analysis of the closed-loop fuzzy system with fully considering the asynchronous premises. Then, based on the derived condition, the method of the desired controller design is presented. Two illustrative examples are finally presented to demonstrate the practicability and efficacy of the proposed method.
337 citations
TL;DR: A novel adaptive fuzzy control scheme is proposed by a backstepping technique that can guarantee that the tracking error converges to a small neighborhood of the origin in a finite time, and the other closed-loop signals remain bounded.
Abstract: This paper addresses the finite-time tracking problem of nonlinear pure-feedback systems. Unlike the literature on traditional finite-time stabilization, in this paper the nonlinear system functions, including the bounding functions, are all totally unknown. Fuzzy logic systems are used to model those unknown functions. To present a finite-time control strategy, a criterion of semiglobal practical stability in finite time is first developed. Based on this criterion, a novel adaptive fuzzy control scheme is proposed by a backstepping technique. It is shown that the presented controller can guarantee that the tracking error converges to a small neighborhood of the origin in a finite time, and the other closed-loop signals remain bounded. Finally, two examples are used to test the effectiveness of proposed control strategy.
335 citations
TL;DR: This paper is concerned with the event-triggered finite-time control problem for networked switched linear systems by using an asynchronous switching scheme, and sufficient conditions are established to guarantee theevent-based asynchronous closed-loop systems are both finite-Time bounded and input-output finite- time stable.
Abstract: This paper is concerned with the event-triggered finite-time control problem for networked switched linear systems by using an asynchronous switching scheme. Not only the problem of finite-time boundedness, but also the problem of input-output finite-time stability is considered in this paper. Compared with the existing event-triggered results of the switched systems, a new type of event-triggered condition is proposed. Sufficient conditions are established to guarantee the event-based asynchronous closed-loop systems are both finite-time bounded and input-output finite-time stable. A set of event-triggered finite-time bounded and input-output finite-time stabilizing controllers are designed under the asynchronous control scheme. It is revealed that the triggered thresholds determine the number of sampling points transmitted to the controller, and the smaller triggered parameters indicate the less-sampled data needed to be transmitted to the controller under the event-triggered scheme. Finally, a boost converter circuit is applied to bring out the advantages of the proposed control scheme.
299 citations
TL;DR: It is shown that the proposed adaptive fuzzy decentralized optimal control approach can guarantee that all the signals in the closed-loop system are bounded, and the tracking errors converge to a small neighborhood of zero.
Abstract: In this paper, the problem of adaptive fuzzy decentralized optimal control is investigated for a class of nonlinear large-scale systems in strict-feedback form. The considered nonlinear large-scale systems contain the unknown nonlinear functions and unmeasured states. By utilizing the fuzzy logic systems to approximate the unknown nonlinear functions and cost functions, a fuzzy state observer is established to estimate the unmeasured states. The control design is divided into two phases. First, by using the state observer and the backstepping design technique, a feedforward decentralized controller with parameters adaptive laws is designed, by which the original controlled strict-feedback nonlinear large-scale system is transformed into an equivalent affine nonlinear large-scale system. Second, by using adaptive dynamic programming theory, a feedback decentralized optimal controller is developed for the equivalent affine nonlinear system. The whole adaptive fuzzy decentralized optimal control scheme consists of a feedforward decentralized controller and a feedback decentralized optimal controller. It is shown that the proposed adaptive fuzzy decentralized optimal control approach can guarantee that all the signals in the closed-loop system are bounded, and the tracking errors converge to a small neighborhood of zero. In addition, the proposed control approach can guarantee that the cost functions are minimized. Simulation results are given to demonstrate the effectiveness of the proposed control approach.
285 citations
TL;DR: A coordinated control strategy is proposed to regulate the charge/discharge of BESs using a combination of the local droop-based control method and a distributed control scheme which ensures the voltages of feeder remain within allowed limits.
Abstract: The voltage rise problem in low voltage distribution networks with high penetration of photovoltaic (PV) resources is one of the most important challenges in the development of these renewable resources since it may prevent the maximum PV penetration considering the reliability and security issues of distribution networks. In this paper, the battery energy storage (BES) systems are used in order to solve the voltage rise during the peak PV generation as well as the voltage drop while meeting the peak load. A coordinated control strategy is proposed to regulate the charge/discharge of BESs using a combination of the local droop-based control method and a distributed control scheme which ensures the voltages of feeder remain within allowed limits. Therefore, two different consensus algorithms are used: the first algorithm determines the BESs participation in voltage regulation in terms of their installed capacity whereas the second one modifies the BESs performance in terms of their state of charge to prevent the excessive saturation or depletion of batteries. The proposed controller enables the effective use of storage capacity in different conditions. Finally, the simulation results based upon real data of a radial distribution feeder validate the effectiveness of this approach.
283 citations
TL;DR: An adaptive event- triggering LFC scheme is presented, where the event-triggering threshold can be dynamically adjusted to save more limited network resources, while preserving the desired control performance.
Abstract: Load frequency control (LFC) is a very important method to keep the power systems stable and secure. However, due to the introduction of communication networks in multi-area power systems, the traditional LFC method is not effective again. This motivates us to investigate an adaptive event-triggering ${H}_{\infty }$ LFC scheme for multi-area power systems. Compared with the existing time-invariant event-triggering communication scheme, an adaptive event-triggering communication scheme is presented, where the event-triggering threshold can be dynamically adjusted to save more limited network resources, while preserving the desired control performance. Compared with the existing emulation-based method, where the controller must be known a priori , the stability and stabilization criteria derived in this work can provide a tradeoff to balance the required communication resources and the desired control performance. The effectiveness of the proposed method is verified by two numerical examples.
276 citations
TL;DR: This paper is concerned with the input-to-state stabilizing control problem for cyber-physical systems (CPSs) with multiple transmission channels under denial-of-service (DoS) attacks, and the proposed LMI-based method is more flexible.
Abstract: This paper is concerned with the input-to-state stabilizing control problem for cyber-physical systems (CPSs) with multiple transmission channels under denial-of-service (DoS) attacks. Under the data update policy with bounded update interval, a new control scheme that discards the outdated information is proposed, and the stability analysis of CPSs under DoS attacks is transformed into analyzing the stability of the system under a switched controller with the help of a class of linear matrix inequalities (LMIs). Then, inspired by the techniques for switched systems, sufficient conditions on the duration and frequency of the DoS attacks, under which the stability of the closed-loop systems is still guaranteed, are proposed. Compared with the existing method for the single-channel case, the considered multiple-channel case is more challenging, and the proposed LMI-based method is more flexible.
273 citations
TL;DR: This brief addresses the trajectory tracking control problem of a fully actuated surface vessel subjected to asymmetrically constrained input and output with the proposed control, which will never be violated during operation, and all system states are bounded.
Abstract: This brief addresses the trajectory tracking control problem of a fully actuated surface vessel subjected to asymmetrically constrained input and output. The controller design process is based on the backstepping technique. An asymmetric time-varying barrier Lyapunov function is proposed to address the output constraint. To overcome the difficulty of nondifferentiable input saturation, a smooth hyperbolic tangent function is employed to approximate the asymmetric saturation function. A Nussbaum function is introduced to compensate for the saturation approximation and ensure the system stability. The command filters and auxiliary systems are integrated with the control law to avoid the complicated calculation of the derivative of the virtual control in backstepping. In addition, the bounds of uncertainties and disturbances are estimated and compensated with an adaptive algorithm. With the proposed control, the constraints will never be violated during operation, and all system states are bounded. Simulation results and comparisons with standard method illustrate the effectiveness and advantages of the proposed controller.
Proceedings Article•
01 Jan 2018TL;DR: In this paper, the authors use model predictive control (MPC) to learn the cost and dynamics of a controller via end-to-end learning, and demonstrate that MPC policies are significantly more data-efficient than a generic neural network and that their method is superior to traditional system identification.
Abstract: We present foundations for using Model Predictive Control (MPC) as a differentiable policy class for reinforcement learning. This provides one way of leveraging and combining the advantages of model-free and model-based approaches. Specifically, we differentiate through MPC by using the KKT conditions of the convex approximation at a fixed point of the controller. Using this strategy, we are able to learn the cost and dynamics of a controller via end-to-end learning. Our experiments focus on imitation learning in the pendulum and cartpole domains, where we learn the cost and dynamics terms of an MPC policy class. We show that our MPC policies are significantly more data-efficient than a generic neural network and that our method is superior to traditional system identification in a setting where the expert is unrealizable.
TL;DR: A learning model predictive controller for iterative tasks is presented in this article, where a safe set and a terminal cost function are used in order to guarantee recursive feasibility and non-decreasing performance at each iteration.
Abstract: A learning model predictive controller for iterative tasks is presented. The controller is reference-free and is able to improve its performance by learning from previous iterations. A safe set and a terminal cost function are used in order to guarantee recursive feasibility and nondecreasing performance at each iteration. This paper presents the control design approach, and shows how to recursively construct terminal set and terminal cost from state and input trajectories of previous iterations. Simulation results show the effectiveness of the proposed control logic.
TL;DR: A novel event-triggered scheme is proposed to improve the transmission efficiency at each sampling instance and sufficient conditions for the resulting fuzzy Markovian jump systems are established in terms of coupled linear matrix inequalities.
Abstract: This paper investigates the problem of event-triggered control for a class of fuzzy Markov jump systems with general switching policies. A novel event-triggered scheme is proposed to improve the transmission efficiency at each sampling instance. Each transition rate allows to be unknown, known, or only its uncertain domains value is known. With the help of a tailored technique to bind the uncertain terms and an asynchronous operation approach to tackle the fuzzy system and fuzzy controller, sufficient conditions for the resulting fuzzy Markovian jump systems are established in terms of coupled linear matrix inequalities. Finally, an example is given to illustrate the validity of the developed technique.
TL;DR: In this paper, an adaptive fault-tolerant controller is derived by incorporating backstepping control, the barrier Lyapunov function, and Nussbaum gains, which is able to guarantee the satisfaction of the prespecified constraints on the transformed errors, as well as the boundedness of all other closed-loop signals, without resorting to a judicious selection of the control parameters.
Abstract: The science objectives of a spacecraft mission place stringent performance requirements on the spacecraft attitude control system. However, it remains an open problem how to guarantee consistent control performance necessary to meet these requirements, especially in the event of actuator faults and input saturation. Motivated by this fact, in this paper, we address the problem of attitude tracking control with prescribed performance guarantees for a rigid spacecraft subject to unknown but constant inertia parameters, unexpected disturbances, actuator faults, and input saturation. First, certain performance functions specified a priori by the designer are adopted to impose desired performance metrics on the attitude tracking errors. Then, the original attitude tracking error dynamics with performance constraints is transformed into an equivalent “state-constrained” one whose robust stabilization is shown to be sufficient to solve the stated problem via a novel error transformation. Subsequently, based on the transformed system, an adaptive fault-tolerant controller is derived by incorporating backstepping control, the barrier Lyapunov function, and Nussbaum gains. It is proved that the designed controller is able to guarantee the satisfaction of the prespecified constraints on the transformed errors, as well as the boundedness of all other closed-loop signals, without resorting to a judicious selection of the control parameters. Finally, the effectiveness of the proposed control scheme is evaluated by means of simulation experiments carried out on a microsatellite.
TL;DR: It is shown through a benchmark example that compared with the unmanned marine vehicle without control, the designed dynamic output feedback controllers can attenuate the oscillation amplitudes of the yAW velocity error and the yaw angle much smaller than a proportional–integral controller.
TL;DR: A novel nonlinear sliding mode control approach dealing with the formation control of under-actuated ships is presented and a distributed controller is designed for individual under-Actuated ship to achieve the given formation pattern within a finite time.
Abstract: A novel nonlinear sliding mode control approach dealing with the formation control of under-actuated ships is presented in this paper To avoid the singularity problem, state space of the system is partitioned into two regions, with one region bounded for terminal sliding mode control and its complement singular for that And a linear auxiliary sliding mode controller is designed for system trajectories starting from the complement region With the application of nonlinear sliding mode control approach and finite-time stability theory, a distributed controller is designed for individual under-actuated ship to achieve the given formation pattern within a finite time Finally, two simulation examples are provided to verify the effectiveness and performance of the proposed approach
TL;DR: The concept of Robust-CBF that, when combined with existing ISS-CLFs, produces controllers for constrained nonlinear systems with disturbances is introduced, considered an extension of the CLF-based point-wise minimum norm controller.
TL;DR: In this article, possible approaches to control the semiconductor junction temperature are discussed along with the implementation in several emerging applications, and the modification of the control variables at different levels (modulation, control, and system) to alter the loss generation or distribution is analyzed.
Abstract: The thermal stress of power electronic components is one of the most important causes of their failure. Proper thermal management plays an important role for more reliable and cost-effective energy conversion. As one of the most vulnerable and expensive components, power semiconductor components are the focus of this paper. Possible approaches to control the semiconductor junction temperature are discussed in this paper, along with the implementation in several emerging applications. The modification of the control variables at different levels (modulation, control, and system) to alter the loss generation or distribution is analyzed. Some of the control solutions presented in the literature, which showed experimentally that the thermal stress can be effectively reduced, are reviewed in detail. These results are often mission-profile dependent and the controller needs to be tuned to reach the desired cost-benefit tradeoff. This paper analyzes also the many open questions of this research area. Among them, it is worth highlighting that a verification of the actual lifetime extension is still missing.
TL;DR: In the proposed method, the feedback signals and the NN weights are aperiodically updated only when the event-triggered condition is violated, and the number of transmissions can be significantly reduced.
Abstract: This paper is concerned with the adaptive event-triggered control problem of nonlinear continuous-time systems in strict-feedback form. By using the event-sampled neural network (NN) to approximate the unknown nonlinear function, an adaptive model and an associated event-triggered controller are designed by exploiting the backstepping method. In the proposed method, the feedback signals and the NN weights are aperiodically updated only when the event-triggered condition is violated. A positive lower bound on the minimum intersample time is guaranteed to avoid accumulation point. The closed-loop stability of the resulting nonlinear impulsive dynamical system is rigorously proved via Lyapunov analysis under an adaptive event sampling condition. In comparing with the traditional adaptive backstepping design with a fixed sample period, the event-triggered method samples the state and updates the NN weights only when it is necessary. Therefore, the number of transmissions can be significantly reduced. Finally, two simulation examples are presented to show the effectiveness of the proposed control method.
TL;DR: It is proved that the system output is driven to track the reference signal to a bounded compact set, all the signals in the closed-loop system are guaranteed to be bounded, and all the states do not transgress their constrained sets.
Abstract: In the paper, the adaptive observer and controller designs based fuzzy approximation are studied for a class of uncertain nonlinear systems in strict feedback. The main properties of the considered systems are that all the state variables are not available for measurement and at the same time, they are required to limit in each constraint set. Due to the properties of systems, it will be a difficult task for designing the controller and the stability analysis. Based on the structure of the considered systems, a fuzzy state observer is framed to estimate the unmeasured states. To ensure that all the states do not violate their constraint bounds, the Barrier type of functions will be employed in the controller and the adaptation laws. In the stability analysis, the effect caused by the constraints for all the states can be overcome by using the Barrier Lyapunov functions. Based on the proposed control approach, it is proved that the system output is driven to track the reference signal to a bounded compact set, all the signals in the closed-loop system are guaranteed to be bounded, and all the states do not transgress their constrained sets. The effectiveness of the proposed control approach can be verified by setting a simulation example.
TL;DR: A distributed impulsive controller is proposed and bounded synchronization, caused by false data injection is studied, and several mean-square bounded synchronization conditions are derived and the error bound is given.
TL;DR: Some novel sufficient conditions are obtained to guarantee that the closed-loop system reaches a specified cost value under the designed jumping state feedback control law in terms of linear matrix inequalities.
Abstract: This paper is concerned with the guaranteed cost control problem for a class of Markov jump discrete-time neural networks (NNs) with event-triggered mechanism, asynchronous jumping, and fading channels. The Markov jump NNs are introduced to be close to reality, where the modes of the NNs and guaranteed cost controller are determined by two mutually independent Markov chains. The asynchronous phenomenon is considered, which increases the difficulty of designing required mode-dependent controller. The event-triggered mechanism is designed by comparing the relative measurement error with the last triggered state at the process of data transmission, which is used to eliminate dispensable transmission and reduce the networked energy consumption. In addition, the signal fading is considered for the effect of signal reflection and shadow in wireless networks, which is modeled by the novel Rice fading models. Some novel sufficient conditions are obtained to guarantee that the closed-loop system reaches a specified cost value under the designed jumping state feedback control law in terms of linear matrix inequalities. Finally, some simulation results are provided to illustrate the effectiveness of the proposed method.
TL;DR: The stability and stabilization criteria are derived by taking into consideration an asynchronous difference between the normalized membership function of the T–S fuzzy DPS and that of the controller, which can stabilize states of the UMV.
Abstract: This paper is concerned with a Takagi–Sugeno (T–S) fuzzy dynamic positioning controller design for an unmanned marine vehicle (UMV) in network environments. Network-based T–S fuzzy dynamic positioning system (DPS) models for the UMV are first established. Then, stability and stabilization criteria are derived by taking into consideration an asynchronous difference between the normalized membership function of the T–S fuzzy DPS and that of the controller. The proposed stabilization criteria can stabilize states of the UMV. The dynamic positioning performance analysis verifies the effectiveness of the networked modeling and the controller design.
TL;DR: A game theory-based lane-changing model, which mimics human behavior by interacting with surrounding drivers using the turn signal and lateral moves, and which outperforms fixed rule-based controllers in both Simulink and dSPACE.
Abstract: Lane changing is a critical task for autonomous driving, especially in heavy traffic. Numerous automatic lane-changing algorithms have been proposed. However, surrounding vehicles are usually treated as moving obstacles without considering the interaction between vehicles/drivers. This paper presents a game theory-based lane-changing model, which mimics human behavior by interacting with surrounding drivers using the turn signal and lateral moves. The aggressiveness of the surrounding vehicles/drivers is estimated based on their reactions. With this model, the controller is capable of extracting information and learning from the interaction in real time. As such, the optimal timing and acceleration for changing lanes with respect to a variety of aggressiveness in target lane vehicle behavior are found accordingly. The game theory-based controller was tested in Simulink and dSPACE. Scenarios were designed so that a vehicle controlled by a game theory-based controller could interact with vehicles controlled by both robot and human drivers. Test results show that the game theory-based controller is capable of changing lanes in a human-like manner and outperforms fixed rule-based controllers.
TL;DR: This paper is concerned with the problem of passivity-based asynchronous sliding mode control for a class of uncertain singular Markovian jump systems with time delay and nonlinear perturbations.
Abstract: This paper is concerned with the problem of passivity-based asynchronous sliding mode control for a class of uncertain singular Markovian jump systems with time delay and nonlinear perturbations. The asynchronous control strategy is employed due to the nonsynchronization between the controller and the system modes. Considering the singularity of the system, a novel integral-type sliding surface is constructed, and then the asynchronous sliding controller is synthesized to ensure that the sliding mode dynamics satisfy the reaching condition. Sufficient conditions are presented such that the corresponding sliding mode dynamics are admissible and robustly passive. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed technique.
TL;DR: It is shown that under the proposed sliding mode controller, the resulting closed-loop system can achieve the uniformly ultimate boundedness and simulation examples are presented to show the merit and applicability of the proposed fuzzy sliding mode control method.
Abstract: This paper investigates the problem of adaptive integral sliding mode control for general Takagi–Sugeno fuzzy systems with matched uncertainties and its applications. Different control input matrices are allowed in fuzzy systems. The matched uncertainty is modeled in a unified form, which can be handled by the adaptive methodology. A fuzzy integral-type sliding surface is utilized and the parameter matrices can be determined according to user's requirement. Based on the designed sliding surface, a new sliding mode controller is proposed, and the structure of the controller depends on the difference between the disturbance input matrices and the control input matrices. It is shown that under the proposed sliding mode controller, the resulting closed-loop system can achieve the uniformly ultimate boundedness. Furthermore, simulation examples are presented to show the merit and applicability of the proposed fuzzy sliding mode control method.
TL;DR: Comparisons of the approximation performance between radial basis function NN, RNN, and DLRNN show that theDLRNN can accurately estimate the unknown dynamics with a fast speed while the internal states of DLRnn are more stable.
Abstract: In this paper, an adaptive sliding mode control system using a double loop recurrent neural network (DLRNN) structure is proposed for a class of nonlinear dynamic systems. A new three-layer RNN is proposed to approximate unknown dynamics with two different kinds of feedback loops where the firing weights and output signal calculated in the last step are stored and used as the feedback signals in each feedback loop. Since the new structure has combined the advantages of internal feedback NN and external feedback NN, it can acquire the internal state information while the output signal is also captured, thus the new designed DLRNN can achieve better approximation performance compared with the regular NNs without feedback loops or the regular RNNs with a single feedback loop. The new proposed DLRNN structure is employed in an equivalent controller to approximate the unknown nonlinear system dynamics, and the parameters of the DLRNN are updated online by adaptive laws to get favorable approximation performance. To investigate the effectiveness of the proposed controller, the designed adaptive sliding mode controller with the DLRNN is applied to a $z$ -axis microelectromechanical system gyroscope to control the vibrating dynamics of the proof mass. Simulation results demonstrate that the proposed methodology can achieve good tracking property, and the comparisons of the approximation performance between radial basis function NN, RNN, and DLRNN show that the DLRNN can accurately estimate the unknown dynamics with a fast speed while the internal states of DLRNN are more stable.
TL;DR: In this paper, a dual-loop primary controller is proposed to regulate primary-side power and current, which allows sequential and timely activation of segmented primary coils; it controls the primary coil current at the reference value under no-load and loaded conditions, compensates for power transfer reduction caused by vehicle lateral misalignment (LTM), and prevents primary overloading.
Abstract: Dynamic wireless charging of electric vehicles (EVs) can significantly extend the EVs’ driving range and consequently, the prospect of electrified transportation. In this paper, a comprehensive study is conducted to elaborate the constraints of real driving conditions and propose a solution that could cope with misalignment problem and the dynamics imposed by the charging process and by EVs passing over road-embedded charging pads. A dual-loop primary controller is proposed to regulate primary-side power and current. The controller allows sequential and timely activation of segmented primary coils; it controls the primary coil current at the reference value under no-load and loaded conditions, compensates for power transfer reduction caused by the vehicle lateral misalignment (LTM), and prevents primary overloading. The primary of the dynamic wireless charger is modeled using the generalized state-space averaging method and the model is verified through simulations and experiments. After that, a controller has been designed and implemented and its operation is evaluated through simulations and experimental tests. A 25-kW charging system with two primary coils is built and tested in a real environment. The measured energy efficiency is 86% for the laterally aligned vehicle, with the possibility to be increased over 90% using enhanced schemes for coils’ activation and deactivation. The system is delivering an equal amount of energy for all LTMs in the range of ±15 cm, which improves the expected value of transferred energy by more than 30%.
TL;DR: This paper presents an admittance controller framework and elaborate control scheme that can be used for controller design and development, and presents seven design guidelines for achieving high-performance admittance controlled devices that can render low inertia, while aspiring coupled stability and proper disturbance rejection.
Abstract: This paper presents an overview of admittance control as a method of physical interaction control between machines and humans. We present an admittance controller framework and elaborate control scheme that can be used for controller design and development. Within this framework, we analyze the influence of feed-forward control, post-sensor inertia compensation, force signal filtering, additional phase lead on the motion reference, internal robot flexibility, which also relates to series elastic control, motion loop bandwidth, and the addition of virtual damping on the stability, passivity, and performance of minimal inertia rendering admittance control. We present seven design guidelines for achieving high-performance admittance controlled devices that can render low inertia, while aspiring coupled stability and proper disturbance rejection.