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In this paper, the adaptive fuzzy control of switched stochastic nonlinear systems with set-time prescribed performance based on event-driven mechanism is studied. The creative part of this paper is that based on the set-time performance function, a modified event-triggered strategy that considers asynchronous switching to deteriorate system performance without strict assumptions is presented, which avoids Zeno behavior and saves communication resources. Then, by using backstepping recursive design technique, It $\hat {o}$ ’s differential lemma and mode-dependent average dwell time (MDADT) method, a novel adaptive performance control scheme is proposed, which can ensure that all the variables in the system are semiglobally uniformly ultimately bounded (SGUUB) in probability and the tracking error gets into prescribed boundary no later than an arbitrarily adjusted setting time. Finally, the proposed algorithm is applied to a RLC circuit and its practicability is verified via simulation results. [ABSTRACT FROM AUTHOR]

For a class of nontriangular nonlinear systems in presence of unknown disturbances, we propose a predictor-based neural dynamic surface control (PNDSC) strategy in this paper. This nontriangular system is transformed via the mean value theorem, and a predictor is then constructed. To avoid an algebraic loop problem, partial state vectors are employed as input signals of neural networks (NNs) for approximating unknown dynamics, and compensation items are designed to compensate for approximation errors from NNs. Different from the traditional NDSC, the PNDSC in this paper utilizes prediction errors to update learning parameters for improving NNs’ learning behaviors with overlarge adaptive gains. On the basis of improved NNs’ approximation behaviors, a predictor-based NNs disturbance observer (PNNDO) is constructed for compensation for external disturbances and approximation errors from NNs. Furthermore, with predictors, a normalization method of weights is developed to reduce the number of online learning parameters. On the basis of the aforementioned result, measurement noises are taken into account in our predictor-based neural control strategy. We employ predictor states, rather than measurement information paralyzed by noises, in design of our control strategy. This reduces high-frequency oscillations in control input. A Lyapunov-based stability analysis shows that all signals are ultimately bounded in the closed-loop system. Finally, the effectiveness of the proposed control strategy is verified by a numerical example and a permanent magnet brushless DC motor system. [ABSTRACT FROM AUTHOR]

In this paper, based on sampled-data control method, we address the stabilization problem for a general class of linear continuous-time fractional systems whose solution contains the Mittag-Leffler function that does not obey the basic exponentiation identity. By considering the infinite memory and hereditary characteristics of fractional-order calculus, we propose a sampled-data controller that guarantees the resulting closed-loop system to be asymptotically stable. Simulation examples are presented to demonstrate the effectiveness of the proposed controller and verify the established results. [ABSTRACT FROM AUTHOR]

This paper focuses on the distributed consensus seeking of multi-agent systems (MASs) with discrete-time control updating and intermittent communications among agents. Compared with existing linearly coupled protocols, a nonlinear coupled Zeno-free event-triggered controller is first proposed, which is further to project the static and dynamic triggering mechanisms exploited by using the deadband control method. Then, the node-based nonlinear coupled adaptive event-triggered controller with online self-tuning of time-varying coupling weight and its corresponding to static and dynamic deadband-based event-triggered mechanisms are designed, respectively. The exploited adaptive event-triggered controller does not rely on any global information of interaction structure and is implemented in a fully distributed fashion. In addition, two dynamic proposals not only cover existing static strategies as special cases, but also show that the minimal inter-execution time of dynamic one is not smaller than that of static one. Theoretical analysis shows that the proposed static and dynamic deadband-based event-triggered mechanisms can not only ensure the average consensus with Zeno-freeness, but also achieve the data reduction of communication and control. Finally, the proposed algorithms applied to circuit implementation are corroborated to prove its practical merits and validity. [ABSTRACT FROM AUTHOR]

In this paper, global event-triggered adaptive prescribed tracking output feedback control is investigated for a class of nonlinear uncertain systems with unknown control direction. Different from the existing works, the design of the tracking error-dependent normalized function and prescribed boundary is based on our uniformly defined blow-up function rather than a specific function, and the asymmetric constraint requirements on tracking error can be achieved by appropriately selecting the blow-up function. By utilizing the Kalman filter ($K$ -filter) and dynamic gain technique, a new reduce-order observer is proposed, which can make the estimated error exponentially converge to zero. In addition, by introducing a dynamic signal into the trigger condition, a novel event-triggered mechanism is proposed, which can extend the trigger time interval and completely counteract the event-triggered errors in terms of asymptotic stability by sacrificing part of the system transient performance. Furthermore, an adaptive controller is designed based on the backstepping method, which ensures the boundedness of the state of the closed-loop system, while regulating the tracking error meets the global prescribed performance requirements and approaches to zero asymptotically. Two examples are given to illustrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper designs a novel nonsingular terminal sliding mode control (TSMC) scheme for a class of nonlinear systems under binary encoding transmission. To further ease the communication overheads between the plant and the controller, a dynamic event-triggered mechanism is also introduced to the TSMC design. Sufficient conditions are proposed, respectively, for guaranteeing the reachability to a practical sliding mode and the ultimate boundedness of the closed-loop system, in which the effects from the binary encoding and the dynamic event-triggered protocol are quantified clearly. The Zeno phenomena for the developed dynamic event-triggered mechanism is excluded via an explicit analysis. Finally, the feasibility and effectiveness of the proposed scheme are demonstrated by a numerical example and a real experiment on permanent magnet synchronous motor speed regulation system. [ABSTRACT FROM AUTHOR]

In this paper, a class of flexible riser systems modeled by partial differential equations (PDEs) with the backlash is considered. The backlash is formulated as the addition of a linear input and a interference-like term, then an new auxiliary item is introduced to compensate for the impact of this backlash. In addition, the constraint problem for the position and the velocity is also taken into consideration. To solve this constrain problem, the logarithmic barrier Lyapunov function is employed. For the flexible riser system, two kinds of adaptive controllers are proposed under the following two cases. One controller is designed when only the parameter of backlash is unknown. On the basis of this result, the other controller is presented when some system parameters cannot be measured through actual measurement. Then, combing the theory of Lyapunov stability, the two controllers can guarantee the boundedness of all signals in the closed-loop flexible riser system. Further, both the position and the velocity satisfy their corresponding constraint condition. Finally, the simulation example verifies that the proposed control method is effective. [ABSTRACT FROM AUTHOR]

In this paper, the observer based neural adaptive finite-time containment control strategy for non-strict feedback nonlinear multi-agent systems is studied. The finite-time command filter is used to overcome the explosion of complexity problem and the established fractional power based error compensation signal is applied to compensate the filtering error caused by the filter. The distributed finite-time command filtered backstepping control method combines with the neural adaptive control technology and state observer is given, which ensures the containment control errors reach to the desired neighborhood of the origin in finite-time in the presence of uncertain dynamics and unmeasurable states in the system. The given numerical simulations show the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper investigates the secure consensus tracking problem for continuous-time nonlinear multiagent systems with sensor attacks. By designing a secure data selector, the unattacked output data is extracted from a group of output measurements under sparse sensor attacks. Then, by virtue of the obtained data and neural networks, a state observer is constructed to estimate the unavailable system states, where the convex combination theory is introduced to reduce the difficulty of solving observation gains. To utilize the limited communication resources reasonably, a novel saturated threshold event-triggered control strategy is proposed to reduce control updates, and then each update is encoded into a binary signal (0 or 1) to further reduce the occupation of communication bandwidth. The designed control scheme ensures that all closed-loop signals are semi-globally uniformly ultimately bounded, and its effectiveness is verified via a simulation of attitude control of unmanned aerial vehicles. [ABSTRACT FROM AUTHOR]

This paper investigates the exponential synchronization (ES) problem for complex networks (CNs) under an intermittent adaptive event-triggered control (IAE-TC) strategy which is based on dynamic IAE-TC during the control activation intervals. In this control mechanism, the intermittent controller has adaptability to the evolution results of the controlled networks and is activated only when the event-triggered condition is violated. Then, by employing this kind of control strategy and the Lyapunov method, some sufficient conditions are proposed to achieve the ES of CNs and it is proven that the Zeno behavior can be eliminated. Besides, an application about the islanded microgrid system and some numerical simulations are provided to verify the effectiveness of the derived theoretical results. Moreover, in the numerical simulations, it is shown that the dynamic IAE-TC strategy can decrease the number of event-triggered instants more availably than the static one. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive admittance control scheme is developed for robots to interact with time-varying environments. Admittance control is adopted to achieve a compliant physical robot–environment interaction, and the uncertain environment with time-varying dynamics is defined as a linear system. A critic learning method is used to obtain the desired admittance parameters based on the cost function composed of interaction force and trajectory tracking without the knowledge of the environmental dynamics. To deal with dynamic uncertainties in the control system, a neural-network (NN)-based adaptive controller with a dynamic learning framework is developed to guarantee the trajectory tracking performance. Experiments are conducted and the results have verified the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In-SRAM Computation improves the throughput and energy-efficiency of data-intensive applications by utilizing parallelism and reducing the data transfers. However, when multiple wordlines are accessed simultaneously, a short-circuit path will likely incur dynamic read disturbance and generate extra direct current in 6T Compute SRAM (CSRAM). In order to mitigate this issue, existing works either degrade the access speed, use area-hungry bitcells, or incur architecture-level overheads. In this paper, we first perform a comprehensive circuit-level analysis of the dynamic read disturbance issues of 6T SRAM for the first time and find that such disturbance can be efficiently avoided by maintaining the bitline voltage at a high level. Second, we propose a novel energy-efficient, reconfigurable sense amplifier design that is able to achieve fast and reliable sensing when the bitline voltage level is high for the compute access. Third, we propose an adaptive wordline control scheme that keeps the bitline voltage at a high level to eliminate the dynamic read disturbance and the sneaky direct current pathway. Both the new sense amplifier and adaptive wordline control are also optimized to support the normal read access efficiently. We have validated our design in a 55nm CMOS technology. Experimental results show that our design not only reliably addresses the read disturbance and the extra direct current, but also operates 19% faster than the state-of-the-art design using an advanced 28nm FDSOI technology. [ABSTRACT FROM AUTHOR]

In the paper, synchronization of nonlinear dynamical complex networks with stochastic coupling under adaptive PI control is considered. In light of the relative states information of the network nodes, node- and edge-based adaptive PI synchronization protocols are proposed and several sufficient conditions are derived. By employing stochastic analysis techniques combined with a version of the LaSalle’s invariant principles applied to stochastic process and Lyapunov function approaches, synchronization of complex networks with nonlinear dynamics and stochastic coupling reaches in mean square via these two types of adaptive PI control protocols are proved. The proposed adaptive PI control protocols for synchronization of complex dynamical networks are useful for future realistic engineering systems design. Numerical examples are finally simulated to validate the theoretical analysis. [ABSTRACT FROM AUTHOR]

In this paper, four control strategies for DC-DC buck converters are proposed, compared and analyzed: a single-loop adaptive control strategy (SA), a double-loop adaptive control strategy (DA), a single-loop disturbance observer-based control strategy (SDOB) and a double-loop disturbance observer-based control strategy (DDOB). First, the nominal system without considering the parametric uncertainties of the DC-DC buck converter is built to help develop the SA and DA. The SA is built by adaptive and backstepping control approaches, and the DA is set up by adaptive and sliding mode control approaches. Additionally, a model considering parametric uncertainties is introduced, giving the opportunity to develop the SDOB and DDOB. The SDOB is developed using a designed disturbance observer and backstepping control technique, and the DDOB is synthesized using a designed disturbance observer and sliding mode control method. Finally, the advantages and disadvantages of the four proposed control strategies are compared and analyzed through experiments. [ABSTRACT FROM AUTHOR]

This paper considers the problem of designing adaptive feedforward control (AFC) systems for uncertain single-input single-output linear systems perturbed by multisinusoidal disturbances of known frequencies. The proposed approach removes the longstanding assumption that either the sign of the real part or the imaginary part of the transfer function of a stable plant at the frequency of excitation is known for AFC to be applicable, which is referred to in this paper as a strictly positive real (SPR)-like condition. Notable features of the solution are that persistence of excitation is not required, and stability analysis tools based on averaging are avoided; hence, the requirement of an exponentially stable equilibrium for the closed-loop system is circumvented. [ABSTRACT FROM AUTHOR]

Without assuming that the global connectivity information of the interaction graph is available to any agent, fully distributed saturated controller design for output consensus of heterogeneous multi-agent systems (MASs) under directed leader-follower communication topologies is addressed in this paper. The main difficulty of the problem lies in the coupling of the nonlinearities caused by the input saturation and the adaptive feedback control. To decouple these two types of nonlinearities, a distributed adaptive observer is first designed for each follower to realize asymptotical estimation of leader’s state, where the adaptive coupling gain is embedded to estimate the graph connectivity, and then an extra tracking error observer is introduced to tackle input saturation constraint. The controller is then constructed based upon these two observers, which is fully distributed with only the information of the distributed adaptive observer transmitted between neighboring agents and the local output information of each agent involved. To reduce the order of the controller, an in-depth analysis of the proposed controller is conducted, and the novel controller is designed by removing the tracking error observer and modifying the multi-level saturation control parameters for each follower. Furthermore, to remove the limitation of using absolute output information of each agent, novel controller is proposed with tracking error estimation designed in a distributed manner, which can achieve output consensus tracking for heterogeneous non-introspective MASs with undirected subgraph among followers. [ABSTRACT FROM AUTHOR]

In the paper, the periodic event-triggered adaptive controller is proposed for attitude stabilization of a rigid spacecraft with model uncertainties, external disturbances, and input saturation. We adopt the periodic state measurement instead of the continuous one, and it is known that the former is more economical and practical than the latter due to the relaxation of continuous measurements. Moreover, new event-triggered adaptation update laws and the periodic event-triggering strategy are proposed for adaptive sliding mode control (SMC) to guarantee the states in the practical sliding mode. It is illustrated that the states of the attitude control system are ultimately bounded. Further, it is proved that the presented periodic event-triggered strategy is without $Zeno$ phenomenon and there exists a uniform lower bound for each interevent time. Some numerical examples are given to demonstrate the validity of the derived control strategy. [ABSTRACT FROM AUTHOR]

This paper studies an adaptive neural network (NN) tracking control method for a class of uncertain nonlinear strict-feedback systems with time-varying full-state constraints. As we all know, the states are inevitably constrained in the actual systems because of the safety and performance factors. The main contributions of this paper are that: 1) in order to ensure that the states do not violate the asymmetric time-varying constraint regions, an adaptive NN controller is constructed by introducing the asymmetric time-varying barrier Lyapunov function (TVBLF) and 2) the amount of the learning parameters is reduced by introducing a TVBLF at each step of the backstepping. Based on the Lyapunov stability analysis, it can be proven that all the signals in the closed-loop system are the semiglobal ultimately uniformly bounded and the time-varying full-state constraints are never violated. Finally, a numerical simulation is given, and the effectiveness of this adaptive control method can be verified. [ABSTRACT FROM AUTHOR]

This paper proposes a new concurrent learning-based adaptive control algorithm. The main objective behind our proposition is to relax the persistent excitation requirement for the stability guarantee, while providing the ability to identify time-varying parameters. To achieve the objective, this paper designs a directional forgetting algorithm, which is then integrated with the adaptive law. The theoretical stability analysis shows that the tracking and parameter estimation error is exponentially stable with the signal only finitely excited, not persistently excited. The analysis also shows that the proposed algorithm can guarantee the stability under time-varying parameters. Moreover, the necessary and sufficient conditions for the stability given the time-varying parameters are derived. The results of numerical simulations confirm the validity of the theoretical analysis results and demonstrate the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the adaptive neural network (NN) tracking control problem is addressed for robot manipulators subject to dead-zone input. The control objective is to design an adaptive NN controller to guarantee the stability of the systems and obtain good performance. Different from the existing results, which used NN to approximate the nonlinearities directly, NNs are employed to identify the originally designed virtual control signals with unknown nonlinear items in this paper. Moreover, a sequence of virtual control signals and real controller are designed. The adaptive backstepping control method and Lyapunov stability theory are used to prove the proposed controller can ensure all the signals in the systems are semiglobally uniformly ultimately bounded, and the output of the systems can track the reference signal closely. Finally, the proposed adaptive control strategy is applied to the Puma 560 robot manipulator to demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, the main issues of model-based control methods are first reviewed, followed by the motivations and the state of the art of the model-free adaptive control (MFAC). MFAC is a novel data-driven control method for a class of unknown nonaffine nonlinear discrete-time systems since neither explicit physical model nor Lyapunov stability theory or key technical lemma is used in the controller design and theoretical analysis except only for the input/output (I/O) measurement data. The basis of MFAC is the dynamic linearization data modeling method at each operating point of the closed-loop system. The established dynamic linearization data model is a virtual equivalent data relationship in the I/O sense to the original nonlinear plant by means of a novel concept called pseudo-partial derivative (PPD) or pseudo-gradient (PG) vector. Based on this virtual equivalent dynamic linearization data model and the time-varying PPD or PG estimation algorithm designed merely using the I/O measurements of a controlled plant, the MFAC system is constructed. The main contribution of this paper is that the theoretical analysis of the bounded-input bounded-output stability, the monotonic convergence of the tracking error dynamics, and the internal stability of the full form dynamic linearization based MFAC scheme are rigorously presented by the contraction mapping principle; the well known PID control and the traditional adaptive control for linear time-invariant systems are explicitly shown as the special cases of this MFAC. The simulation results verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper addresses the problems of pinning synchronization control and regulation and its circuit realization of a class of perturbed coupled Chua’s circuit networks. Robust constrained control strategies for pinned circuits and adaptive regulation schemes for couplings are developed to eliminate the impact of bounded time-varying perturbations and nonlinearities. Bounded synchronization results of the closed-loop Chua’s circuit networks are obtained based on Lyapunov stability theory. Furthermore, the control strategies and adaptive regulation schemes are implemented physically by circuits to construct the closed-loop Chua’s circuit networks reality. Comparison results based on MULTISIM and MATLAB softwares are given to verify the effectiveness of the proposed control and regulation circuits. [ABSTRACT FROM AUTHOR]

This paper presents a novel scheme of controller design for a cross-coupled multi-input multi-output (MIMO) two link robot manipulator (TLRM) system having time varying uncertainties, disturbances, and nonlinearity by employing fuzzy parallel distributed compensation (PDC) type ${L}_{1}$ adaptive controller. The combination of linear consequence-based fuzzy subsystems are utilized to represent the nonlinear TLRM system. The components of $ {L}_{1}$ adaptive control framework are also realized by combining linear consequence-based fuzzy systems to incorporate the zone-wise controlling of the TLRM system. A fuzzy PDC control has also been augmented to achieve robust stable transient and steady state performances. The stability of each fuzzy systems as well as the overall proposed control scheme is guaranteed by utilizing fuzzy Lyapunov theory-based analysis. The experimental results demonstrate the usefulness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper studies the distributed average tracking (DAT) problem for multiple reference signals described by the second-order nonlinear dynamical systems. Leveraging the state-dependent gain design and the adaptive control approaches, a couple of DAT algorithms are developed in this paper, which are named finite-time and adaptive-gain DAT algorithms. Based on the finite-time one, the states of the physical agents in this paper can track the average of the time-varying reference signals within a finite settling time. Furthermore, the finite settling time is also estimated by considering a well-designed Lyapunov function in this paper. Compared with asymptotical DAT algorithms, the proposed finite-time algorithm not only solve finite-time DAT problems but also ensure states of physical agents to achieve an accurate average of the multiple signals. Then, an adaptive-gain DAT algorithm is designed. Based on the adaptive-gain one, the DAT problem is solved without global information. Thus, it is fully distributed. Finally, numerical simulations show the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper proposes adaptive iterative learning control (ILC) schemes for continuous-time parametric nonlinear systems with iteration lengths that randomly vary. As opposed to the existing ILC works that feature nonuniform trial lengths, this paper is applicable to nonlinear systems that do not satisfy the globally Lipschitz continuous condition. In addition, this paper introduces a novel composite energy function based on newly defined virtual tracking error information for proving the asymptotical convergence. Both an original update algorithm and a projection-based update algorithm for estimating the unknown parameters are proposed. Extensions to cases with unknown input gains, iteration-varying tracking references, nonparametric uncertainty, high-order nonlinear systems, and multi-input-multi-output systems are all elaborated upon. Illustrative simulations are provided to verify the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the receding horizon near-optimal tracking control problem about a class of continuous-time nonlinear systems with fully unknown dynamics is considered. The main challenges of this problem lie in two aspects: 1) most existing systems only restrict their considerations to the state feedback part while the input channel parameters are assumed to be known. This paper considers fully unknown system dynamics in both the state feedback channel and the input channel and 2) the optimal control of nonlinear systems requires the solution of nonlinear Hamilton–Jacobi–Bellman equations. Up to date, there are no systematic approaches in the existing literature to solve it accurately. A novel model-free adaptive near-optimal control method is proposed to solve this problem via utilizing the Taylor expansion-based problem relaxation, the universal approximation property of sigmoid neural networks, and the concept of sliding mode control. By making approximation for the performance index, it is first relaxed to a quadratic program, and then, a linear algebraic equation with unknown terms. An auxiliary system is designed to reconstruct the input-to-output property of the control systems with unknown dynamics, so as to tackle the difficulty caused by the unknown terms. Then, by considering the property of the sliding-mode surface, an explicit adaptive near-optimal control law is derived from the linear algebraic equation. Theoretical analysis shows that the auxiliary system is convergent, the resultant closed-loop system is asymptotically stable, and the performance index asymptomatically converges to optimal. An illustrative example and experimental results are presented, which substantiate the efficacy of the proposed method and verify the theoretical results. [ABSTRACT FROM AUTHOR]

This paper aims to analyze the problem of adaptive neural network (NN) tracking control for a class of switched stochastic nonlinear pure-feedback systems with unknown direction hysteresis. In the light of recent studies on the hysteresis phenomenon in the field of nonlinear switched systems, this paper focuses on Bouc–Wen hysteresis model with unknown parameters and direction conditions. To simplify the control design, the following procedure is applied. Prior to tackling the unknown direction hysteresis problem based on the Nussbaum function and the backstepping techniques, the pure-feedback structure difficulty is governed by the mean value theorem. Furthermore, an optimized adaptation method is utilized to cope with computational burden. Universal approximation capability of radial basis function NNs and Lyapunov function method is synthesized to develop an adaptive NN tracking control scheme. It is demonstrated that under arbitrary deterministic switching, the presented controller can guarantee that all signals in the closed-loop system are semiglobally uniformly ultimately bounded in probability and the tracking error converges to a neighborhood of the origin. Finally, two simulation examples are given to illustrate the advantages of the proposed control design approach. [ABSTRACT FROM AUTHOR]

Mismatches among frequency control elements in digitally-controlled oscillators can be a significant source of phase error in digital phase-locked loops (PLLs). This paper presents a multi-rate dynamic element matching technique and an adaptive mismatch-noise cancellation (MNC) technique that work together to address this problem. The two techniques operate in background during normal PLL operation, and the MNC technique has typical cold start convergence times of a few seconds. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of adaptive exponential synchronization in mean square for a new neural network model with the following features: 1) the noise is characterized by the Lévy process and the parameters of the model change in line with the Markovian process; 2) the master system is also disturbed by the same Lévy noise; and 3) there are multiple slave systems, and the state matrix of each slave system is an affine function of the state matrices of all slave systems. Based on the Lyapunov functional theory, the generalized Itô’s formula, $M$ -matrix method, and the adaptive control technique, some criteria are established to ensure the adaptive exponential synchronization in the mean square of the master system and each slave system. Moreover, the update law of the control gain and the dynamic variation of the parameters of the slave systems are provided. Finally, the effectiveness of the synchronization criteria proposed in this paper is verified by a practical example. [ABSTRACT FROM PUBLISHER]

This paper presents a novel ultra-low power maximum power point tracking (MPPT) technique with a wide tracking range. An indirect, non-interrupting approach using a novel timing-based algorithm is proposed. The proposed MPPT technique is self-adaptive and applicable to the various types of photovoltaic cells without external reconfiguration or the change of passive components. In addition, a variable gain is employed in the one-hot barrel shift register to reduce the transient response time. A test chip was fabricated in 65-nm CMOS technology. The test chip can harvest energy with the input voltage range of 0.4 V to 1.7 V and the step response time of less than 100 ms at the minimum supply voltage of 0.8 V. The tracking efficiency is up to 96.2% when supplied by a photovoltaic micro-cell array using an irradiation range of 200 lux to 1000 lux. [ABSTRACT FROM PUBLISHER]

By considering the under-actuated characteristics of the quadcopter suspension transportation system and the external disturbance, the effective tracking control of the quadrotor transportation system faces great difficulties and challenges. An adaptive hierarchical sliding mode control (HSMC) scheme based on fixed-time sliding mode disturbance observer (FTSMDO) is developed for the anti-disturbance tracking control of under-actuated quadcopter suspension transportation system (UQSTS). The HSMC is used to deal with the under-actuated characteristics of the UQSTS, and the external disturbance is estimated by designing the FTSMDO. Synchronously, the disturbance estimation error is processed by designing the adaptive law. The stability analysis based on Lyapunov is applied to verify the uniform ultimate boundedness of the closed-loop system. Finally, the comparison of physical experiment upshots shows the effectiveness and potential of the proposed new control techniques. [ABSTRACT FROM AUTHOR]

In this paper, we study mixed traffic flow in which some cars are under car-following control and others are under bilateral control. We also provide the necessary (modular string) stability condition for this type of mixed traffic, which can be viewed as an extension of the condition for pure car-following control-based traffic. This necessary stability condition provides some indication of how the introduction of self-driving cars (under bilateral control) will affect today's traffic. [ABSTRACT FROM AUTHOR]

In this paper, a new adaptive output-feedback controller for a class of multi-input multi-output linear plant models with relative degree three or higher is developed. The adaptive controller includes a baseline design based on observers and parameter adaptation based on a modified closed-loop reference model. The overall design guarantees stability and tracking performance in the presence of large parametric uncertainties that are common in aircraft models with actuator dynamics. The requisite controller has a computational complexity that is an order of magnitude lower than that of a classical model reference adaptive controller. The proposed adaptive controller is evaluated using a model of a very flexible aircraft with second-order actuator dynamics and parametric uncertainties, and demonstrates stable command tracking behavior in scenarios that cause the failure of an linear quadratic regulator controller and an adaptive controller with a first-order actuator model. [ABSTRACT FROM AUTHOR]

The paper extends a recently proposed indirect, certainty-equivalence, event-triggered adaptive control scheme to the case of nonobservable parameters. The extension is achieved by using a novel batch least-squares identifier (BaLSI), which is activated at the time of the events. BaLSI guarantees the finite-time asymptotic constancy of the parameter estimates and the fact that the trajectories of the closed-loop system follow the trajectories of the nominal closed-loop system (nominal in the sense of the asymptotic parameter estimate, not in the sense of the true unknown parameter). Thus, if the nominal feedback guarantees global asymptotic stability and local exponential stability, then unlike conventional adaptive control, the newly proposed event-triggered adaptive scheme guarantees global asymptotic regulation with a uniform exponential convergence rate. The developed adaptive scheme is tested to a well known control problem—the state regulation of the wing-rock model. Comparisons with other adaptive schemes are also provided for this particular problem. [ABSTRACT FROM AUTHOR]

Modern applications of robotics typically involve a robot control system with an inner proportional–integral (PI) or proportional–integral–derivative (PID) control loop and an outer user-specified control loop for specifying the velocity (or position) command. Most existing outer loop controllers, however, do not take into consideration the dynamic effects of robots and their effectiveness relies on the ad hoc assumption that the inner PI or PID control loop is fast enough, and other torque-based controllers cannot be implemented in robots with closed architecture. This paper investigates the adaptive control of robotic systems with an inner/outer loop structure, taking into account the effects of the dynamics and uncertainties, and both the task-space control and joint-space control are considered. We propose a dynamic modularity approach to resolve this issue, and a class of adaptive outer loop control schemes is proposed and their role is to dynamically generate the joint velocity (or position) command for the low-level joint servoing loop. Without relying on the ad hoc assumption that the joint servoing loop is fast enough or modification of the low-level joint controller structure, we show that the proposed outer loop controllers can ensure stability and convergence of the closed-loop system. The formulated dynamic modularity approach is validated by the experimental results using different generations of the UR10 robots. [ABSTRACT FROM AUTHOR]

This paper concentrates on the adaptive state-feedback quantized control problem for a class of multiple-input–multiple-output (MIMO) switched nonlinear systems with unknown asymmetric actuator dead-zone. In this study, we employ different quantizers for different subsystem inputs. The main challenge of this study is to deal with the coupling between the quantizers and the dead-zone nonlinearities. To solve this problem, a novel approximation model for the coupling between quantizer and dead-zone is proposed. Then, the corresponding robust adaptive law is designed to eliminate this nonlinear term asymptotically. A direct neural control scheme is employed to reduce the number of adaptive laws significantly. The backstepping-based adaptive control scheme is also presented to guarantee the system performance. Finally, two simulation examples are presented to show the effectiveness of our control scheme. [ABSTRACT FROM AUTHOR]

In this paper, the complex problems of internal forces and position control are studied simultaneously and a disturbance observer-based radial basis function neural network (RBFNN) control scheme is proposed to: 1) estimate the unknown parameters accurately; 2) approximate the disturbance experienced by the system due to input saturation; and 3) simultaneously improve the robustness of the system. More specifically, the proposed scheme utilizes disturbance observers, neural network (NN) collaborative control with an adaptive law, and full state feedback. Utilizing Lyapunov stability principles, it is shown that semiglobally uniformly bounded stability is guaranteed for all controlled signals of the closed-loop system. The effectiveness of the proposed controller as predicted by the theoretical analysis is verified by comparative experimental studies. [ABSTRACT FROM AUTHOR]

In controlling nonlinear uncertain systems, compensating for rate-dependent hysteresis nonlinearity is an important, yet challenging problem in adaptive control. In fact, it can be illustrated through simulation examples that instability is observed when existing control methods in canceling hysteresis nonlinearities are applied to the networked control systems (NCSs). One control difficulty that obstructs these methods is the design conflict between the quantized networked control signal and the rate-dependent hysteresis characteristics. So far, there is still no solution to this problem. In this paper, we consider the event-triggered control for NCSs subject to actuator rate-dependent hysteresis and failures. A new second-order filter is proposed to overcome the design conflict and used for control design. With the incorporation of the filter, a novel adaptive control strategy is developed from a neural network technique and a modified backstepping recursive design. It is proved that all the control signals are semiglobally uniformly ultimately bounded and the tracking error will converge to a tunable residual around zero. [ABSTRACT FROM AUTHOR]

This paper investigates an adaptive finite-time control (FTC) problem for a class of strict-feedback nonlinear systems with both time-delays and quantized input from a new point of view. First, a new concept, called preassigned finite-time performance function (PFTF), is defined. Then, another novel notion, called practically preassigned finite-time stability (PPFTS), is introduced. With PFTF and PPFTS in hand, a novel sufficient condition of the FTC is given by using the neural network (NN) control and direct adaptive backstepping technique, which is different from the existing results. In addition, a modified barrier function is first introduced in this work. Moreover, this work is first to focus on the FTC for the situation that the time-delay and quantized input simultaneously exist in the nonlinear systems. Finally, simulation results are carried out to illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper investigates an adaptive sliding-mode controller design problem for a class of Markov jump systems with actuator faults. First, a more extensive faults model, which includes actuator loss of effectiveness, outage and stuck, is established. Moreover, a novel sliding surface function is designed and sufficient conditions are established to ensure stochastic stability of the Markov jump systems with known transition probability. Second, based on the adaptive sliding-mode algorithm and the fault compensation algorithm, a comprehensive control law is synthesized not only to overcome the boundary restrictions of actuator loss effectiveness and external disturbances, but also to ensure the properties of stochastic stability and the reachability of the sliding-mode dynamics. Third, an improved stochastically stability criterion, in the case of Markov jump system with uncertain transition probability, is derived based on the homogeneous polynomial matrix technique. Finally, two simulation examples are provided to support the feasibility and effectiveness of the proposed control algorithms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The cooperative control problem of nonlinear multiagent systems is studied in this paper. The followers in the communication network are subject to unmodeled dynamics. A fully distributed neural-networks-based adaptive control strategy is designed to guarantee that all the followers are asymptotically synchronized to the leader, and the synchronization errors are within a prescribed level, where some global information, such as minimum and maximum singular value of graph adjacency matrix, is not necessarily to be known. Based on the Lyapunov stability theory and algebraic graph theory, the stability analysis of the resulting closed-loop system is provided. Finally, an numerical example illustrates the effectiveness and potential of the proposed new design techniques. [ABSTRACT FROM AUTHOR]

As a type of indispensable oceanic transportation tools, ship-mounted crane systems are widely employed to transport cargoes and containers on vessels due to their extraordinary flexibility. However, various working requirements and the oceanic environment may cause some uncertain and unfavorable factors for ship-mounted crane control. In particular, to accomplish different control tasks, some plant parameters (e.g., boom lengths, payload masses, and so on) frequently change; hence, most existing model-based controllers cannot ensure satisfactory control performance any longer. For example, inaccurate gravity compensation may result in positioning errors. Additionally, due to ship roll motions caused by sea waves, residual payload swing generally exists, which may result in safety risks in practice. To solve the above-mentioned issues, this paper designs a neural network-based adaptive control method that can provide effective control for both actuated and unactuated state variables based on the original nonlinear ship-mounted crane dynamics without any linearizing operations. In particular, the proposed update law availably compensates parameter/structure uncertainties for ship-mounted crane systems. Based on a 2-D sliding surface, the boom and rope can arrive at their preset positions in finite time, and the payload swing can be completely suppressed. Furthermore, the problem of nonlinear input dead zones is also taken into account. The stability of the equilibrium point of all state variables in ship-mounted crane systems is theoretically proven by a rigorous Lyapunov-based analysis. The hardware experimental results verify the practicability and robustness of the presented control approach. [ABSTRACT FROM AUTHOR]

The simultaneous presence of parameter variations, time-varying disturbances, and delay in the control loop of an $n{\text{th}}$ -order (possibly unstable) dynamical system makes formal tracking control design difficult. Existing model-based control methodologies can handle only a subset of the above-mentioned complexities, and hence, become insufficient to accurately track the desired trajectory. In this paper, a prediction-based adaptive robust control framework is proposed for high performance control of dynamical systems subject to the above-mentioned complexities, which involves the following. First, prediction-based projection-type adaptation laws with model compensation to reduce the effect of parameter uncertainties under delay and time-varying disturbances. Second, a robust prediction scheme that factors in both unknown parameters and disturbance uncertainties under delay to handle the resulting unmatched disturbances. Third, prediction-based continuous robust feedback to attenuate the cumulative effect of disturbance terms due to uncertain prediction. The controller guarantees semiglobal, exponential convergence of the tracking error with an ultimate error bound proportional to delay, disturbance, and controller gain. The controller effectiveness is demonstrated with an illustrative flight control example and compared with the baseline adaptive robust controller. [ABSTRACT FROM AUTHOR]

This paper studies the online adaptive optimal controller design for a class of nonlinear systems through a novel policy iteration (PI) algorithm. By using the technique of neural network linear differential inclusion (LDI) to linearize the nonlinear terms in each iteration, the optimal law for controller design can be solved through the relevant algebraic Riccati equation (ARE) without using the system internal parameters. Based on PI approach, the adaptive optimal control algorithm is developed with the online linearization and the two-step iteration, i.e., policy evaluation and policy improvement. The convergence of the proposed PI algorithm is also proved. Finally, two numerical examples are given to illustrate the effectiveness and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, an event-based distributed cooperative learning (DCL) law is proposed for a group of adaptive neural control systems. The plants to be controlled have identical structures, but reference signals for each plant are different. During control process, each agent intermittently broadcasts its neural network (NN) weight estimation to its neighboring agents under an event-triggered condition that is only based on its own estimated NN weights. If communication topology is connected and undirected, the NN weights of all neural control systems can converge to a small neighborhood of their optimal values. The generalization ability of NNs is guaranteed in the event-triggered context, that is, the approximation domain of each NN is the union of all system trajectories. Furthermore, a strictly positive lower bound on the interevent intervals is also guaranteed to avoid the Zeno behavior. Finally, a numerical example is given to illustrate the effectiveness of the proposed learning law. [ABSTRACT FROM AUTHOR]

This paper develops a new partial-state feedback model reference adaptive control (MRAC) scheme, which has full capability to deal with plant uncertainties for output tracking and desired flexibility to combine the advantages of full-state feedback MRAC and output feedback MRAC. For partial-state feedback MRAC, plant-model matching is achievable as with full-state feedback control, while the controller structure enjoys less complexity as compared with an output feedback MRAC design. Adaptive partial-state feedback control designs are developed for relative-degree-one plants and for general plants. Both adaptive control designs ensure closed-loop system stability and asymptotic output tracking. New results are presented for plant-model matching, error model, adaptive law, and stability analysis. New features of partial-state feedback MRAC are addressed, including its design flexibility. Simulation studies are conducted whose results verify the effectiveness of partial-state feedback MRAC. [ABSTRACT FROM AUTHOR]

Nearly all adaptive control techniques require that the control directions of dynamical systems are known in advance. In this paper, for a class of pure-feedback nonaffine discrete-time systems with unknown control directions (UCDs), a high-order neural network (HONN)-based adaptive iterative learning control (ILC) approach is presented to address a repetitive tracking control issue. The implicit function theorem is adopted to cope with the difficulty resulting from the nonaffine structure of control input. Employing a discrete Nussbaum-type function in the neural network weight adaptation law to suit the UCD, an HONN is used to iteratively estimate the ideal control signals. In addition, a novel dead-zone method is developed in the HONN-based adaptive ILC algorithm to enhance its robustness against nonrepetitive desired trajectories and random uncertainties in iterative initial errors and external disturbance. Consequently, the system output, except at the initial $n$ time instants, is demonstrated to asymptotically converge to an adjustable range of the desired trajectory along the iteration axis, while all of the system signals remain bounded during the entire ILC process. Two simulation examples show the feasibility of the adaptive ILC approach. [ABSTRACT FROM AUTHOR]

This paper concerns the adaptive state-feedback control for a class of high-order stochastic nonlinear systems with uncertainties including time-varying delay, unknown control gain, and parameter perturbation. The commonly used growth assumptions on system nonlinearities are removed, and the adaptive control technique is combined with the sign function to deal with the unknown control gain. Then, with the help of the radial basis function neural network approximation approach and Lyapunov–Krasovskii functional, an adaptive state-feedback controller is obtained through the backstepping design procedure. It is verified that the constructed controller can render the closed-loop system semiglobally uniformly ultimately bounded. Finally, both the practical and numerical examples are presented to validate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper proposes an approximate/adaptive optimal control (AOC) design for completely unknown continuous-time linear time invariant systems, without requiring the restrictive persistence of excitation (PE) condition for parameter convergence. The proposed AOC algorithm utilizes two layers of filtering—the first layer filters strategically eliminate the need for state derivative information, while the second layer filters provide suitable algebraic relations for iteratively obtaining the optimal policy under a milder online-verifiable initial excitation assumption. Unlike previous AOC algorithms, the proposed method does not require finite window integrals, intelligent data-storage, and the restrictive PE assumption. Further, the proposed method relaxes the sufficient condition required for obtaining successive stabilizing control policies. The intermediate policies are proved to be stabilizing and converging to the optimal policy. Simulation results validate the efficacy of the proposed adaptive/approximate linear quadratic regulator algorithm. [ABSTRACT FROM AUTHOR]