Sequence Of Control Signals Research Articles

CuMONITOR: Continuous Monitoring of Microarchitecture for Software Task Identification and Classification

The interactions between software and hardware are increasingly important to computer system security. This research collected microprocessor control signal sequences to develop machine learning models that identify software tasks. In contrast with prior work that relies on hardware performance counters to collect data for task identification, this research is based on creating additional digital logic to record sequences of control signals inside a processor’s microarchitecture. The proposed approach considers software task identification in hardware as a general problem, with attacks treated as a subset of software tasks. Three lines of effort are presented. First, a data collection approach is described to extract sequences of control signals labeled by task identity during actual (i.e., non-simulated) system operation. Second, experimental design selects hardware and software configurations to train and evaluate machine learning models. The machine learning models significantly outperform a naïve classifier based on Euclidean distances from class means. Various experiment configurations produced a range of balanced accuracy scores. Third, task classification is addressed using decision boundaries defined with thresholds chosen by an optimization strategy to develop non-neural network classifiers. When implemented in hardware, the non-neural network classifiers could require less digital logic to implement compared to neural network models.

Active control strategy for switched systems against asynchronous DoS attacks

This paper is dedicated to the switched systems suffering asynchronous denial-of-service (DoS) attacks that compromise sensor–controller and controller–actuator channels independently. By taking into account the energy constraints, the duration of DoS attacks has different maximum bounds for different channels. To eliminate the negative effect of DoS attacks, an active control strategy consisting of a predictor mechanism and a buffer mechanism is devised. In such a strategy, the controller sends a sequence of control signals to the actuator to guarantee the system energy does not increase too fast, even if the sensor–controller and the controller–actuator channels are attacked simultaneously. Furthermore, the mismatch of the controller and the subsystem is addressed due to the presence of event-triggered scheme and DoS attacks. It is shown that the systems’ states allow a bounded divergency over mismatched interval. To this end, the switching signal is designed in connection with the properties of DoS attacks, event-triggered scheme and system parameters. Finally, an example is carried out to verify the effectiveness of the theoretical results.

Low-frequency learning quantized control for MEMS gyroscopes accounting for full-state constraints

In this paper, a low-frequency learning quantized control scheme for micro-electro-mechanical systems (MEMS) gyroscopes subject to full-state constraints is proposed. In order to cope with asymmetric time-varying constraints imposed on MEMS gyroscope states, a nonlinear state-dependent transformation scheme is introduced via converting the constrained system into an equivalent formulation free from constraints, which entirely removes the feasibility condition enforced on the virtual control signal, and avoids the nontrivial optimization of design parameters. A low-frequency learning architecture capable of recovering uncertainties in the transformed system without incurring high-frequency transient chattering is proposed with the aid of a low-pass filter, a state estimator and a minimum-learning-parameter neural network (MLPNN), which has the following striking features: one is that the estimation error rather than tracking error is utilized in the weight update law, allowing for better transient estimation behaviors in the presence of a high adaptive gain. Another is that the high-frequency component of disturbances can be filtered out via inserting a filtering error-based modification term. Then, a logarithm quantizer (LQ) is employed to generate control signal sequences within finite digital sets for MEMS gyroscopes, remarkably saving communication and actuating resources. Eventually, via Lyapunov stability synthesis and substantial simulation results, all closed-loop error signals are proven to be ultimately uniformly bounded, and the effectiveness along with superiority of proposed algorithm are illustrated.

Trans_Proc

A reconfigurable transform processor is proposed and implemented here. Firstly, a brief study of processors implementing different transformations is presented. We have categorized the transform processor as the one which can implement a number of linear transforms using reconfigurability. The theoretical suitability regarding the architecture of the processor is proved by graph theory method. Then the complete architecture of the overall processor and the processing element is presented and implemented using VHDL. The complete instruction set suitable to the processor is designed. The instructions are mapped to the sequence of control signals. Generating sequence of control signals for every type of instructions would finally create a hardwired control unit for the processor which was also presented. Next the processor is fed with data to simulate it. A three-phase simulation is carried out to prove the correctness of the design. Finally the same processor with a data bus width of 32 to 512 is implemented and compared in terms of speed and size.

Guest Editorial Model Predictive Control in Energy Conversion Systems

The papers in this special section focus on model predictive control (MPC) in energy conversion systems. MPC refers to a broad range of control strategies that make explicit use of a model of the system/device to be controlled optimally. In order to obtain the optimal control signal (or sequence of control signals), MPC optimizes a certain cost function at regular intervals. Due to its unique capabilities to deal with constraints on actuators and system states as well as its theoretical basis, MPC has been widely received and successfully used for many decades, mostly for control of slow industrial plants. However, with continuous advances of control theory and increasing computational capabilities of modern microprocessors, this control strategy has recently became a technically feasible solution for control of energy conversion systems that operate at much faster times scales.

Modeling of the dynamic hysteresis in DEAP actuator using an empirical mode decomposition based long-short term memory network

As a smart material-based actuator, the dielectric electro-active polymer (DEAP) actuator is widely considered to be a potential driving mechanism for many applications, especially in intelligent bio-inspired robotics. However, the DEAP actuator demonstrates rate-dependent and asymmetrical hysteresis phenomenon which leads to great tracking inaccuracy and even oscillatory response, severely limiting its further development. Feedforward Neural Network (FNN) model has already become a widely used method to describe this kind of strong hysteresis nonlinearity in recent years. However, the FNN has no ability to remember the historical state of long period of time which is also a very important factor to restrict hysteresis phenomenon. In this paper, a novel hybrid model, Long-Short Term Memory (LSTM) network combined with Empirical Mode Decomposition (EMD), is proposed to model the dynamic hysteresis nonlinearity in DEAP actuator. At first, the original control signal sequence is preprocessed into a series of sub-sequence by the EMD method and is reshaped by one-sided dead-zone operator. Then the input space of LSTM is conducted using the original control signal, the sub-sequence, and reshaped signal. Finally, the input space and the displacement signal are applied to train the long-short term memory network. In order to verify the performance of the proposed model, the traditional artificial back propagation neural network (BPNN) model, rate-dependent Prandtl-Ishlinskii (RPI) model, and nonlinear electromechanical (NEM) model are compared from prediction accuracy. The results demonstrate that: (1) the proposed model has a higher prediction accuracy than the traditional artificial BPNN, RPI, and NEM model; and (2) the prediction accuracy of LSTM network is significantly improved by using EMD. Therefore, the long-short term memory network combined with empirical mode decomposition is a competitive method compared to the existing state-of-the-art approach.

Математическое описание функций управления двухфазным вентильным двигателем с двухсекционной фазной обмоткой

The mathematical description of a digital control device for a modern electric drive based on a switched two-phase motor with two-sectional phase windings is presented. For all possible winding sec-tion connection schemes and their basic usage methods involving the application of a four-arm or four-switch power amplifier, the cyclic sequences of control signals are determined and analytical expressions for them are derived that take into account the motor rotor rotation in both directions. Compact versions of representing them in form of Zhegalkin's polynomial or in the disjunctive form are obtained. Analytical dependencies of the rotor position digital signals received from Hall sensors are obtained, and their in-terrelation with the base vectors and number of pole pairs on the rotor magnet is revealed. Combined methods for using the phase winding sections are elaborated, which make it possible to obtain the base vectors uniformly distributed over the rotation circumference with their amplitudes minimally differing from each other. The derived mathematical expressions can be used in designing a digital electric drive and implementing high-speed algorithms based on programmable logic circuits and systems on a chip.

An Echo State Gaussian Process-Based Nonlinear Model Predictive Control for Pneumatic Muscle Actuators

Pneumatic muscle actuators (PMAs), a kind of soft/compliant actuators, have been attracted a great deal of attention in the studies of rehabilitation robots. However, the nonlinearities, uncertainties, hysteresis, and time-varying features of PMAs bring a lot of difficulties in their high-precision trajectory tracking tasks. In this paper, an echo state Gaussian process-based nonlinear model predictive control (ESGP-NMPC) is designed for the PMAs. The proposed strategy is comprised of an ESGP, which is suitable for modeling unknown nonlinear systems as well as measuring their uncertainties, and a gradient descent optimization algorithm for calculating the control signal sequences. Based on the Lyapunov theorem, characteristics of the closed-loop system are analyzed to guarantee the asymptotical stability. Both simulations and physical experiments are carried out to illustrate the validity of the proposed control strategy. Compared with other conventional methods, the ESGP-NMPC can achieve a better model fitting for the PMA and control performance for the high-precision tracking tasks. Note to Practitioners —High-precision control of pneumatic muscle actuators (PMAs) is a vital problem when PMAs are utilized as actuators of rehabilitation robots since the patient’s safety and the performance of rehabilitation tasks are largely dependent on the accuracy of the actuators. Conventional model-based control approaches usually require relatively accurate identification of system parameters, which is difficult for the PMA, owing to its strong nonlinear and time-varying characteristics. This paper proposes a new model predictive control method based on an echo state Gaussian process that can describe the unknown dynamics of a PMA due to its universal approximation property. Through the optimization method, the controller can be efficiently realized and presents better performances than some comparatives. By applying this approach, it is possible to achieve not only high-precision control of PMAs but also a certain degree of robustness to the load.

High-precision tracking differentiator via generalized discrete-time optimal control

An enhanced discrete-time tracking differentiator (TD) with high precision based on discrete-time optimal control (DTOC) law is proposed. This law takes the form of state feedback for a double-integral system that adopts the Isochronic Region approach. There, the control signal sequence is determined by a linearized criterion based on the position of the initial state point on the phase plane. The proposed control law can be easily extended to the TD design problem by combining the first-state variable of the double-integral system with the desired trajectory. To improve the precision of the discretization model, we introduced a zero-order hold on the control signal. We also discuss the general form of DTOC law by analysing the relationship between boundary transformations and boundary characteristic points. After comparing the simulation results from three different TDs, we determined that this new TD achieves better performance and higher precision in signal-tracking filtering and differentiation acquisition than do existing TDs. Also the comparisons of the computational complexities between the proposed DTOC law and normal one are demonstrated. For confirmation of its utility, we processed raw phasor measurement units data via the proposed TD. In the absence of complex power system modelling and historical data, it was verified that the proposed TD is suitable for applications of real-time synchrophasor estimations, especially when the states are corrupted by noise.

Closed‐form solution of discrete‐time optimal control and its convergence

The convergence of a new closed-form solution for the discrete-time optimal control is presented. First, a new time optimal control law with simple structure is constructed in the form of the state feedback for a discrete-time double-integral system by using the state backstepping approach. The control signal sequence in this approach is determined by the linearised criterion according to the position of the initial state point on the phase plane. This closed-form non-linear state feedback control law clearly shows that time optimal control in discrete time is not necessarily the bang-bang control. Second, the convergence of the time optimal control law is proved by demonstrating the convergence path of the state point sequence driven by the corresponding control signal sequence. Finally, numerical simulation results demonstrate the effectiveness of this new discrete-time optimal control law.

Automatic Correction of Dynamic Power Management Architecture in Modern Processors

The increasing demand for lower power forces designers to use sophisticated power management strategies such as multivoltage and power gating which are often accompanied with many design bugs. Correcting such bugs can be a time-consuming process that requires considerable manual efforts. In this paper, we propose a scalable automated method for correcting dynamic power management architectures by an incremental SAT-based mechanism. First, an initial counterexample (CEX) is generated by checking the equivalency between the specification model of the processor and its buggy implementation model. Then, we find two candidate solutions instead of one to satisfy this CEX. If two solutions are not equivalent, we generate new CEX in an iterative process which effectively converges into the final solution. The proposed method enables designers to correct multiple bugs such as missing isolation cells between two power domains, disordering in the sequence of control signals, error in the data restoring or saving, and powering off in always-on domains which are not addressed by existing methods. We have shown the effectiveness of our method on modern processors supporting complex power management mechanisms. The results confirm that our proposed method, respectively, reduces symbolic simulation steps and runtime by 2.33× and 47.93× compared to the state-of-the-art methods.

Robust Tracking Control of Networked Control Systems: Application to a Networked DC Motor

This paper investigates robust H2 and H∞ step tracking control methods for networked control systems subject to random time delays modeled by Markov chains. To make full use of the delay information, the proposed two-mode dependent output feedback controller depends on both sensor-to-controller and controller-to-actuator delays. To actively compensate for the controller-to-actuator delays, we propose the “send all, apply one” scheme: Sending a sequence of control signals, then at the actuator/plant node, applying the appropriate control signal according to the actual controller-to-actuator delay. Using the augmentation method, the resulting closed-loop system can be formulated as a discrete-time Markovian jump linear system. The H2 and H∞ step tracking problems are tackled by solving a set of linear matrix inequalities with nonconvex constraints. Both numerical simulations and experiments on a networked dc motor system are conducted to illustrate the effectiveness of the proposed methods.

Observer-based tracking controller design for networked predictive control systems with uncertain Markov delays

This paper is concerned with a tracking controller design problem for discrete-time networked predictive control systems. The control law used here is a combined state-feedback control and integral control. Since not all the states are available in practice, a local Luenberger observer is utilised to estimate the state vector. The measured output and estimated state vector are packed together and transmitted to the tracking controller via a communication channel with a limited capacity. Meanwhile, the control signal is also transmitted through a communication network.Network-induced delays on both links are considered for the signal transmission and modelled by Markov chains. Moreover, it is assumed that the elements in Markov transition matrices are subject to uncertainties. In order to fully compensate for network-induced delays, the controller generates a sequence of control signals which are dependent on each possible delay in the feedforward channel. By taking the augmentation twice, we obtain delay-free stochastic closed-loop systems and the controlled output is chosen as the tracking error. Sufficient conditions are provided for the energy-to-peak performance of the closed-loop systems. The feedback gains of the controller can be derived by solving a minimisation problem. Two examples are illustrated to demonstrate the effectiveness of the proposed design method.

Low Transition Test Pattern Generator Architecture for Built-in-Self-Test

Problem statement: In Built-In Self-Test (BIST), test patterns are generated and applied to the Circuit-Under-Test (CUT) by on-chip hardware; minimizing hardware overhead is a major concern of BIST implementation. In pseudorandom BIST architectures, the test patterns are generated in random nature by linear feedback shift registers. This normally requires more number of test patterns for testing the architectures which need long test time. Approach: This study presents a novel test pattern generation technique called Low-Transition Generalized Linear Feedback Shift Register (LT-GLFSR) with bipartite (half fixed) and bit insertion (either 0 or 1) techniques. Intermediate patterns (by bipartite and bit (either 0 or 1) insertion technique) inserted in between consecutive test patterns generated by GLFSR which is enabled by a non overlapping clock scheme. This process is performed by finite state machine generate sequence of control signals. Low-Transition Generalized Linear Feedback Shift Registers (LT-GLFSR), are used in a circuit under test to reduce the average and peak power during transitions. LT-GLFSR patterns high degree of randomness and correlation between consecutive patterns. LT-GLFSR does not depend on circuit under test and hence it is used for both BIST and scan-based BIST architectures. Results and Conclusion: Simulation results prove that this technique has reduction in power consumption and high fault coverage with minimum number of test patterns. The results also show that it reduces the peak and average power consumption during test for ISCAS’89 bench mark circuits.

Specific features of constructional optimization of the parameters of CMOS memory devices

The constructional features of the design of individual units and their mutual arrangement in static CMOS memory devices are reviewed. These features have a consolidated effect upon the functionality, operating speed, and reliability of memory devices over a wide region of specified technological parameters of the manufacturing process. The principles of the architecture of static CMOS memory devices and the related problems of designing the circuits for the formation of a sequence of control signals by individual units are discussed.

Design of a Packet-Based Control Framework for Networked Control Systems

A packet-based control framework is proposed for networked control systems (NCSs). This framework takes advantage of the characteristic of the packet-based transmission in a networked control environment, which enables a sequence of control signals to be sent over the network simultaneously, thus making it possible to actively compensate for the communication constraints in NCSs. Under this control framework and a deriving delay-dependent feedback gain scheme, a novel model for NCSs is proposed which can deal with network-induced delay, data packet dropout and data packet disorder in NCSs simultaneously and a receding horizon controller is also designed to implement the packet-based control approach. This approach is then verified by a numerical example and furthermore an Internet-based test rig which illustrates the effectiveness of the proposed approach.

SYMBOLIC APPROACHES FOR FINDING CONTROL STRATEGIES IN BOOLEAN NETWORKS

We present an exact algorithm, based on techniques from the field of Model Checking, for finding control policies for Boolean Networks (BN) with control nodes. Given a BN, a set of starting states, I, a set of goal states, F, and a target time, t, our algorithm automatically finds a sequence of control signals that deterministically drives the BN from I to F at, or before time t, or else guarantees that no such policy exists. Despite recent hardness-results for finding control policies for BNs, we show that, in practice, our algorithm runs in seconds to minutes on over 13,400 BNs of varying sizes and topologies, including a BN model of embryogenesis in Drosophila melanogaster with 15,360 Boolean variables. We then extend our method to automatically identify a set of Boolean transfer functions that reproduce the qualitative behavior of gene regulatory networks. Specifically, we automatically learn a BN model of D. melanogaster embryogenesis in 5.3 seconds, from a space containing 6.9 x 10(10) possible models.

Optimal Compensation for Temporal Uncertainty in Movement Planning

Motor control requires the generation of a precise temporal sequence of control signals sent to the skeletal musculature. We describe an experiment that, for good performance, requires human subjects to plan movements taking into account uncertainty in their movement duration and the increase in that uncertainty with increasing movement duration. We do this by rewarding movements performed within a specified time window, and penalizing slower movements in some conditions and faster movements in others. Our results indicate that subjects compensated for their natural duration-dependent temporal uncertainty as well as an overall increase in temporal uncertainty that was imposed experimentally. Their compensation for temporal uncertainty, both the natural duration-dependent and imposed overall components, was nearly optimal in the sense of maximizing expected gain in the task. The motor system is able to model its temporal uncertainty and compensate for that uncertainty so as to optimize the consequences of movement.

Modeling and Controlling Parallel Tasks in Droplet-Based Microfluidic Systems

This paper presents general hardware-independent models and algorithms to automate the operation of droplet-based microfluidic systems. In these systems, discrete liquid volumes of typically less than 1 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$muhboxl$</tex> are transported across a planar array by dielectrophoretic or electrowetting effects for biochemical analysis. Unlike in systems based on continuous flow through channels, valves, and pumps, the droplet paths can be reconfigured on demand and even in real time. Algorithms that generate efficient sequences of control signals for moving one or many droplets from start to goal positions, subject to constraints such as specific features and obstacles on the array surface or limitations in the control circuitry, are developed. In addition, an approach toward automatic mapping of a biochemical analysis task onto a DMFS is investigated. Achieving optimality in these algorithms can be prohibitive for large-scale configurations because of the high asymptotic complexity of coordinating multiple moving droplets. Instead, these algorithms achieve a compromise between high runtime efficiency and a more limited nonglobal optimality in the generated control sequences.

Vežimėlio judesio valdymo modelis

The problem is to calculate the path trajectory and sequence of control signals for a vehicle following a wire in the floor. Motion of the vehicle is determined by system of differential equ­ations. Fourth-order Runge–Kutta adaptive method was used for solving of the obtained system of the differential equations.