In this paper, a discrete-time embedded model control (EMC) scheme is proposed for a class of disturbed nonlinear systems under model uncertainty. First, by invoking the current state, a linear discrete-time embedded model (EM) is real-timely generated by linearizing and discretizing nominal model of nonlinear system. Then, as for the reference dynamics for our control scheme, a reference generator is designed to generate a group of reference control input and reference state. Meanwhile, as for control dynamics for proposed scheme, an auxiliary system and a predictor are combined to compensate for the error of control input. Based on above, a synthetic controller is derived to stabilize the controlled system by combining the reference dynamics and control dynamics. Thus, the discrete-time Lyapunov stability theory is utilized to analyze the overall closed-loop system, and a sufficient stability condition is proposed to guarantee that all the closed-loop states under the proposed EMC scheme are semi-globally ultimately uniformly bounded (UUB), ensuring the ultimate error bounds to be adjusted into tolerable regions. Finally, as for the nonlinear system of quadrotor UAV, some simulations are conducted to illustrate the effectiveness of the proposed control scheme. Note to Practitioners—The motivation of this paper aims to investigate the control stability issue of a disturbed nonlinear system. Currently, these relevant results proposed in existent paper mainly relied on the complicated continuous-time controllers, which are hard to be implemented in practice. Yet, this paper proposes a control scheme based on a discrete-time embedded model, which can ensure the desired control performance. First, a reference signal generator is designed by generating a linear discrete-time embedded model. Based on this, the control error is compensated by combining an auxiliary system and a predictor. Then, a synthetic controller is derived to stabilize the controlled system. The effectiveness of the overall control scheme is validated using a quadrotor UAV nonlinear system, demonstrating that all signals in the closed-loop system under our control scheme are semi-globally uniformly bounded, and the ultimate error bounds can be adjusted to a tolerable range. In future work, we will investigate the problem on input delay compensation based on the control scheme of this paper.

In this article, an adaptive neural tracking control based on saturation disturbance observer (SDO) and command filter is studied for multiple-input-multiple-output nonlinear systems with time-varying constraints and system uncertainties. By employing neural networks (NNs), the system uncertainties are approximated. The SDO is proposed to estimate the composited disturbances which consist of NN approximation errors and the external bounded disturbances. Compared with the traditional disturbance observer, the SDO can reduce the estimation error to some extent. The control requirements are achieved based on the multiconstraints which contain three layers: 1) prescribed performance functions (PPFs); 2) actual constraints; and 3) virtual constraints. The errors remain within the prescribed small neighborhood of zero by using the PPFs, the error constraints ensure that the time-varying constraints are never violated even if the PPFs are not available, and the virtual constraints are applied in a new time-varying barrier Lyapunov function (TVBLF) to design virtual controllers and controller to solve the singularity problem of the traditional TVBLF. In addition, the command filter is introduced to solve the problem of "explosion of complexity." Finally, a numerical simulation verifies the effectiveness of the proposed scheme for a flight control of unmanned aerial vehicle.

In this article, an event-triggered (ET) fractional-order adaptive tracking control scheme (ATCS) is studied for the uncertain nonlinear system with the output saturation and the external disturbances by using the nonlinear disturbance observer (NDO) and the neural networks (NNs). Based on NNs, the system uncertainties are approximated. An NN-based NDO is designed to estimate the bounded disturbances. Combining the NNs, the output of the designed NDO, the fractional-order theory, and the ET mechanism, an ATCS is proposed under the output saturation. According to the stability analysis, all the closed-loop signals are semiglobally uniformly ultimately bounded based on the investigative ATCS. The simulation results and the comparative experiment verifications are shown to indicate the viability of the developed control scheme.

In this article, an adaptive neural network (NN) tracking control scheme is proposed for uncertain multi-input-multi-output (MIMO) nonlinear system in strict-feedback form subject to system uncertainties, time-varying state constraints, and bounded disturbances. The radial basis function NNs (RBFNNs) are adopted to approximate the system uncertainties. By constructing the intermediate variables, the external disturbances that cannot be directly measured are approximated by the disturbance observers. The time-varying barrier Lyapunov function (TVBLF) is constructed to guarantee the boundedness of the errors lie in the sets. To overcome the potential singularity problem that the denominator of the barrier function term approaches zero in controller design, the adaptive NN tracking control scheme with time-varying state constraints is proposed. Based on the TVBLF, the controller will be designed to guarantee tracking performance without violating the appropriate error constraints. The analysis of TVBLF shows that all closed-loop signals remain semiglobally uniformly ultimately bounded (SGUUB). The simulation results are performed to validate the validity of the proposed scheme.

This article proposes a high-order disturbance observer (HODO) and dynamic surface control (DSC) technique-based adaptive fuzzy control scheme for nonlinear systems subjected to input saturation and external time-varying disturbances. First, based on a Sigmoid function, the saturation input is tackled by utilizing a well-defined nonlinear smooth function. Furthermore, HODO and fuzzy logic systems are used to estimate the external disturbances and to handle the lumped unknown functions, respectively. Then, by using the backstepping method and DSC technique, a HODO-based adaptive fuzzy tracking control scheme is proposed for nonlinear systems with saturation nonlinearity, uncertainties, and external disturbances. The Lyapunov analysis method is used to prove that all signals in the entire system are semiglobally uniformly ultimately bounded (SGUUB). In addition, the tracking error converges to a compact set with a tunable error bound determined by some design parameters. Finally, a numerical simulation of two-stage chemical reactor shows the effectiveness of the developed tracking control strategy.

In this paper, an adaptive neural discrete-time (ANDT) fractional-order tracking control scheme is proposed for an unmanned aerial vehicle system with prescribed performance in the presence of system uncertainties and unknown bounded disturbances based on a discrete-time disturbance observer (DTDO). The system uncertainties are handled using neural network (NN) approximation. To compensate for the adverse effects of unknown disturbances, an NN-based DTDO is designed. On the basis of the NN, the designed DTDO and the backstepping technology, an ANDT fractional-order control scheme with prescribed performance is developed. Then, the tracking errors are convergent under the proposed control scheme. Finally, the effectiveness of the proposed discrete-time control scheme is demonstrated by numerical simulation results.

This paper studies the adaptive neural control (ANC)-based tracking problem for discrete-time nonlinear dynamics of an unmanned aerial vehicle subject to system uncertainties, bounded time-varying disturbances, and input saturation by using a discrete-time disturbance observer (DTDO). Based on the approximation approach of neural network, system uncertainties are tackled approximately. To restrain the negative effects of bounded disturbances, a nonlinear DTDO is designed. Then, a backstepping technique-based ANC strategy is proposed by utilizing a constructed auxiliary system and a discrete-time tracking differentiator. The boundness of all signals is proven in the closed-loop system under the discrete-time Lyapunov analysis. Finally, the feasibility of the proposed ANC technique is further specified based on numerical simulation results.

This paper studies the problem of prescribed performance adaptive neural control for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems in the presence of external disturbances and input saturation based on a disturbance observer. The system uncertainties are tackled by neural network (NN) approximation. To handle unknown disturbances, a Nussbaum disturbance observer is presented. By incorporating the disturbance observer and NNs, an adaptive prescribed performance neural control scheme is further developed. Then, the expected asymptotically convergent tracking errors between system output signals and desired signals are achieved. Numerical simulation results demonstrate the effectiveness of the proposed control scheme.

In this paper we explore compiler techniques for achieving efficient communications on circuit switching interconnection networks. We propose a compilation framework for identifying communication patterns and compiling these patterns as network configuration directives. This has the potential of providing significant performance benefits when connections can be established in the network prior to the actual communications. The framework includes a flexible and powerful communication pattern representation scheme that captures the property of communication patterns and allows manipulation of these patterns. In this way, communication phases can be identified within the application. Additionally, we extend the classification of static and dynamic communications to include persistent communications. Persistent communications are a subclass of dynamic communications that remain unchanged for large segments of the application execution. An experimental compiler has been developed to implement the framework. This compiler is capable of detecting both static and persistent communications within an application. We show that for the NAS Parallel Benchmarks, 100% of the point-to-point communications can be classified as either static or persistent and 100% of the collectives are either static or persistent with the exception of IS. Simulation-based performance analysis demonstrates the benefit of using our compiler techniques for achieving efficient communications in multiprocessor systems.

Enabling circuit switching in multiprocessor systems has the potential to achieve more efficient communication with lower cost compared to packet/wormhole switching. However, in order to accomplish this efficiently, assistance from the compiler is required to reveal the communication pattern in the parallel application. In this paper we present symbolic expression analysis techniques in a MPI parallel compiler. Symbolic expression analysis allows the identification and representation of the communication pattern and also assists in the determination of communication phases in MPI parallel applications at compile-time. We demonstrate that using the compiler analysis based on symbolic expression analysis to determine the communication pattern and phases provides an average of 2.6 times improvement in message delay over a threshold-based runtime system for our benchmarks with a maximum improvement of 9.7 times.

This paper presents a technique to visualize the communication pattern of a parallel application at different points during its execution. Unlike existing tools that show the communication pattern for the entire application, our tool breaks this communication pattern down into components to allow the more detailed study of application execution. These patterns are not merely snapshots or windows of the execution but rather are tied to specific code structures comprised of loops in the application. Our technique leverages our compiler, which adds instructions into the code to record where communications and code artifacts occur during execution. This information is stored into a trace format, which is read by our visualization tool. The visualization tool can graphically represent the communication pattern and message volume to allow a user to analyze and optimize the execution. As an example, we show how this information can be used to optimize the execution time and reduce the message delay of applications executed on a system enhanced with optical circuit switch interconnections.

In this paper we describe a compiler framework which can identify communication patterns for MPI-based parallel applications. This has the potential of providing significant performance benefits when connections can be established in the network prior to the actual communication operation. Our compiler uses a flexible and powerful communication pattern representation scheme that can capture the property of communication patterns and allows manipulations of these patterns. In this way, communication phases can be detected and logically separated within the application. Additionally, we extend the classification of static and dynamic communication patterns and operations to include persistent communications. Persistent communications appear dynamically, however, they remain unchanged for large segments of the application execution. Our compiler is capable of detecting both static and persistent communication patterns within an application. We show that for the NAS parallel benchmarks, 100% of the point-to-point communications can be classified as either static or persistent and, with the exception of IS, 100% of the collective were either static or persistent. By comparison to application trace data, the predicted LBMHD, CG and MG communication patterns have been verified.

The interconnect plays a key role in both the cost and performance of large-scale HPC systems. The cost of future high-bandwidth electronic interconnects mushrooms due to expensive optical transceivers needed between electronic switches. We describe a potentially cheaper and more power-efficient approach to building high-performance interconnects. Through empirical analysis of HPC applications, we find that the bulk of inter-processor communication (barring collectives) is bounded in degree and changes very slowly or never. Thus we propose a two-network interconnect: An Optical Circuit Switching (OCS) network handling long-lived bulk data transfers, using optical switches; and a secondary lower-bandwidth Electronic Packet Switching (EPS) network. An OCS could be significantly cheaper, as it uses fewer optical transceivers than an electronic network. Collectives and transient communication packets traverse the electronic network. We present compiler techniques and dynamic run-time policies, using this two-network interconnect. Simulation results show that our approach provides high performance at low cost.