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We demonstrate 128 Gbps/port (8-λ × 16 Gbps/λ) natively error-free transmission across eight optical ports using a 8-port, 8-λ/port WDM remote laser source and a pair of monolithically integrated CMOS optical I/O chiplets with 4.96-5.56 pJ/bit optical Tx+Rx chiplet energy efficiency.

The first 8 Tbps co-packaged FPGA with Silicon-Photonics IO is presented paving the way for co-packaged compute and optical-IO. The Multi-Chip Package integrates Stratix® 10 FPGA with up to five optical IO chiplets. © 2021 The Author(s).

We demonstrate the smallest microring modulator to date, with 1.5 µm radius, record 8.5 THz FSR and 30 GHz/V O-band shift efficiency, using a MOSCAP structure in a new 45 nm monolithic CMOS electronic-photonic process (45CLO). It could enable massively-parallel optical links with low energy and high bandwidth density.

Cryogenic operation, at 4.5K, of a vertical-junction silicon microring modulator in a 45 nm CMOS platform is demonstrated. Record resonance-shift efficiency – 46 GHz/V – enables nonthermal tuning in 140 GHz range and up to 10 Gbps OOK modulation with ≤1Vpp drive, paving the way to cryogenic optical data egress links.

We demonstrate the world's first fully integrated electronic-photonic label-free molecular sensor in a CMOS 45RFSOI commercial process. Real-time kinetics with 100nm lipid nanoparticles, mimicking SARS-CoV-2 particles, are presented utilizing micro-ring resonators integrated with on-chip electronics.

We present a SiGe resonant photodetector fabricated in commercial 45 nm SOI CMOS with a 3-dB bandwidth of 17.8 GHz, the highest reported for such devices. This is achieved by using high doping concentrations to reduce the resistance of the regions accessing the active area.

We demonstrate an electro-optic platform enabling a direct optical I/O interface in an ASIC package. The $5.5\mathrm{x}8.9\mathrm{mm}^{2}$ chiplet uses the Advanced Interface Bus (AIB), a parallel digital interface, to communicate to a host ASIC and integrates high-speed digital/analog circuits, optical modulators, photodetectors, and waveguides. Transmitters and receivers demonstrate data-rates up to 25Gbps at 4.9pJ/bit (Tx+Rx) and −12 BER error-free operation. We show a 32-channel, 512Gbps aggregate (across 4 Tx ports) wavelength-division multiplexed (WDM) transmit demonstration from a TeraPHY chiplet, running at 16Gbps per wavelength and 8 simultaneous wavelengths per port.

The accuracy of conventional ADCs for high-frequency input signals is mainly limited by the sampling clock jitter. To address this issue, this paper demonstrates an ADC that uses low-jitter $( optical pulses to sample the input signal. A prototype two-channel ADC is realized in a 3D integrated platform with 65 nm CMOS and silicon-photonics connected using high-density TOVs. With optical pulses spaced at 250 ps (4 GS/s effective sampling rate), the ADC achieves SNDR of 40 dB near DC and 37 dB at 45 GHz input.

Recent integrated optical phased array architectures, results, and applications will be reviewed. Beam-steering optical phased arrays monolithically integrated with on-chip rare-earth-doped lasers and heterogeneously integrated with CMOS driving electronics will be shown. Passive integrated optical phased arrays that focus radiated light to tightly-confined spots in the near field and that generate quasi-Bessel beams will be discussed. Finally, integrated-phased-array-based visible-light holographic displays will be proposed as a scalable solution towards the next generation of augmented-reality head-mounted displays; passive near-eye holographic displays, visible-light liquid-crystal modulators, and liquid-crystal-based visible-light phased arrays will be presented.

Recent integrated optical phased array architectures, results, and applications will be reviewed, including beam steering for LiDAR and communications, near-field optical manipulation, and holographic displays for augmented reality.

The hydraulic servo actuators (HSA) are often used in the industry in tasks that request great powers, high accuracy and dynamic motion. It is well known that HSA is a highly complex nonlinear system, and that the system parameters cannot be accurately determined due to various uncertainties, inability to measure some parameters, and disturbances. This paper considers control problem of the HSA with unknown dynamics, based on adaptive dynamic programming via output feedback. Due to increasing practical application of the control algorithm, a linear discrete model of HSA is considered and an online learning data-driven controller is used, which is based on measured input and output data instead of unmeasurable states and unknown system parameters. Hence, the ADP based data-driven controller in this paper requires neither the knowledge of the HSA dynamics nor exosystem dynamics. The convergence of the ADP based control algorithm is also theoretically shown. Simulation results verify the feasibility and effectiveness of the proposed approach in solving the optimal control problem of HSA.

In this paper, a novel online mode-free integral reinforcement learning algorithm is proposed to solve the multiplayer non-zero sum games. We first collect and learn the subsystems information of states and inputs; then we use the online learning to compute the corresponding N coupled algebraic Riccati equations. The policy iterative algorithm proposed in this paper can solve the coupled algebraic Riccati equations corresponding to the multiplayer non-zero sum games. Finally, the effectiveness and feasibility of the design method of this paper is proved by simulation example with three players.

Being able to predict the performance of circuits without running expensive simulations is a desired capability that can catalyze automated design. In this paper, we present a supervised pretraining approach to learn circuit representations that can be adapted to new circuit topologies or unseen prediction tasks. We hypothesize that if we train a neural network (NN) that can predict the output DC voltages of a wide range of circuit instances it will be forced to learn generalizable knowledge about the role of each circuit element and how they interact with each other. The dataset for this supervised learning objective can be easily collected at scale since the required DC simulation to get ground truth labels is relatively cheap. This representation would then be helpful for few-shot generalization to unseen circuit metrics that require more time consuming simulations for obtaining the ground-truth labels. To cope with the variable topological structure of different circuits we describe each circuit as a graph and use graph neural networks (GNNs) to learn node embeddings. We show that pretraining GNNs on prediction of output node voltages can encourage learning representations that can be adapted to new unseen topologies or prediction of new circuit level properties with up to 10x more sample efficiency compared to a randomly initialized model. We further show that we can improve sample efficiency of prior SoTA model-based optimization methods by 2x (almost as good as using an oracle model) via fintuning pretrained GNNs as the feature extractor of the learned models.

Convergence of high-performance silicon photonics and electronics, monolithically integrated in state-of-the-art CMOS platforms, is the holy grail for enabling the ultimate efficiencies, performance, and scaling of electronic-photonic systems-on-chip. It requires the emergence of platforms that combine state-of-the-art RF transistors with optimized silicon photonics, and a generation of photonic device technology with ultralow energies, increased operating spectrum, and the elimination of power-hungry thermal tuning. In this paper, in a co-optimized monolithic electronics-photonics platform (GlobalFoundries 45CLO), we turn the metal-oxide-semiconductor (MOS) field-effect transistor’s basic structure into a novel, highly efficient MOS capacitor ring modulator. It has the smallest ring cavity (1.5 μm radius), largest corresponding spur-free free spectral range ( FSR = 8.5 THz ), and record 30 GHz/V shift efficiency in the O-band among silicon modulators demonstrated to date. With 1 V pp RF drive, we show an open optical eye while electro-optically tuning the modulator to track over 400 pm (69 GHz) change in the laser wavelength (using 2.5 V DC range). A 90 GHz maximum electro-optic resonance shift is demonstrated with under 40 nW of power, providing a strong nonthermal tuning mechanism in a CMOS photonics platform. The modulator has a separately optimized body layer but shares the gate device layer and the gate oxide with 45 nm transistors, while meeting all CMOS manufacturability design rules. This type of convergent evolution of electronics and photonics may be the future of platforms for high-performance systems-on-chip.

The next generations of large-scale data-centers and supercomputers demand optical interconnects to migrate to 400G and beyond. Microring modulators in silicon-photonics VLSI chips are promising devices to meet this demand due to their energy efficiency and compatibility with dense wavelength division multiplexed chip-to-chip optical I/O. Higher order pulse amplitude modulation (PAM) schemes can be exploited to mitigate their fundamental energy–bandwidth tradeoff at the system level for high data rates. In this paper, we propose an optical digital-to-analog converter based on a segmented microring resonator, capable of operating at 20 GS/s with improved linearity over conventional optical multi-level generators that can be used in a variety of applications such as optical arbitrary waveform generators and PAM transmitters. Using this technique, we demonstrate a PAM-4 transmitter that directly converts the digital data into optical levels in a commercially available 45-nm SOI CMOS process. We achieved 40-Gb/s PAM-4 transmission at 42-fJ/b modulator and driver energies, and 685-fJ/b total transmitter energy efficiency with an area bandwidth density of 0.67 Tb/s/mm2. The transmitter incorporates a thermal tuning feedback loop to address the thermal and process variations of microrings’ resonance wavelength. This scheme is suitable for system-on-chip applications with a large number of I/O links, such as switches and general-purpose and specialized processors in large-scale computing and storage systems.

The exponential stability problem for impulsive systems subject to double state-dependent delays is studied in this paper, where state-dependent delay (SDD) is involved in both continuous dynamics and discrete dynamics and the boundedness of it with respect to states is prior unknown. According to impulsive control theory, we present some Lyapunov-based sufficient conditions for the exponential stability of the concerned system. It is shown that the stabilizing effect of SDD impulses on an unstable SDD system changes the stability and achieves desired performance. In addition, the destabilizing effect of SDD impulses is also fully considered and the corresponding sufficient conditions are derived, which reveals the fact that a stable SDD system can maintain its performance when it is subject to SDD impulsive disturbance. As an application, the proposed result can be employed to the stability analysis of impulsive genetic regulatory networks (GRNs) with SDD and the corresponding sufficient conditions are proposed in terms of the model transformation technique and the linear matrix inequalities (LMIs) technique. In order to demonstrate the effectiveness and applicability of the derived results, we give two examples including impulsive GRNs with SDD and the impulsive controller design for the nonlinear system with SDD.

System-level driven electronic–photonic codesign is the key to improving the bandwidth density and energy efficiency for high-speed silicon photonic links. In many data-communication scenarios, optical link power is dominated by its transmitter side including the laser source. In this paper, we propose a comprehensive co-optimization framework for high-speed silicon photonic transmitters utilizing compact models and a detailed optical simulation framework. Given technology and link constraints, microring and Mach–Zehnder transmitter designs are optimized and compared based on a unified optical phase shifter model. NRZ and PAM4 modulation schemes are analyzed and compared for microring-based transmitters at 50 Gb/s. Multistage and traveling wave Mach–Zehnder transmitters are optimized and discussed as well. The results show that, for a 50 Gb/s NRZ optical link, an optimized microring transmitter could save more than 60% of the total laser and driver power compared to an optimized Mach–Zehnder transmitter under equivalent photonic technology constraints. For a given datarate and receiver sensitivity, design tradeoffs of silicon photonic processes, devices, and architecture choices are discussed in depth. In addition, this paper introduces a new Simulink toolbox for transient optical simulation. Combined with the proposed optimization engine, it provides an electrooptical co-optimization approach toward truly energy-efficient high-speed silicon photonic links.

We introduce a first principles, end-to-end analysis of opto-electronic communication links which incorporates a thorough model of the receiver circuitry, in addition to the more familiar laser transmitter optimization. In particular, we optimize receiver sensitivity and power by studying their dependence on front-end design as well as follow-on digital sampler requirements. We find that the photo-receiver sensitivity is the most important factor in controlling the overall link power consumption. Our physical model and circuit optimization principles are applied to a heterogenous-integrated photonic+CMOS platform, where we show state-of-the-art performance through this physics-based rapid-design protocol. Incidentally this greatly simplifies the design process. Lastly, we apply this approach to extrapolate future performance trends, platform bottlenecks, and fundamental limits in optical link design while showcasing the potential for sub-1fJ/bit system efficiency at high speeds.

Energy efficient implementation of machine learning applications is crucial for attaining cognition at the edge. State of the art systems with Deep Neural Networks (DNN) are memory and computationally intensive impeding its deployment on resource-constrained embedded systems. Building on a hardware-software codesign paradigm for dedicated accelerators with algorithm, architecture and novel device integration allows for an optimized design framework. In this work, we leverage the inherent sparsity feature of neural networks and static nature of the weight memory in the inference mode, to introduce NEMs-based neural accelerator engine. The design achieves 6x better energy efficiency and 3x lower area than conventional CMOS designs.

This paper presents a characterization and a compact model of microring resonator based photodetector designed in a monolithic silicon-photonic platform. As these detectors are inherently wavelength selective they can potentially enable fully integrated DWDM optical receivers. The detector makes use of resonance enhancement in a microring by integrating 30 interleaved lateral P-N junctions along a 5µm radius microring cavity. The performance measurements reveal a peak responsivity of 0.6 A/W at 1270nm with

A deep neural network (DNN) based stochastic combinatorial optimization framework is presented that can find the optimal sizing of circuits in a sample-efficient manner. This sample efficiency allows us to unify this framework with generator-based tools like Berkeley Analog Generator (BAG) [1] to directly optimize layout, given the high level circuit specifications. We use this tool to design an optical link receiver layout, satisfying high-level design specifications, using post-layout simulations of only 348 design instances. Compared to an evolutionary algorithm without our DNN-based discriminator, our framework improves the sample efficiency and run time by more than 200x.

This paper demonstrates the first fully integrated coherent binary-phase-shift-keying (BPSK) link using microring resonat(MRR) with forwarded laser LO signal. It is enabled by integration of silicon-photonic blocks like optical DAC based modulator, 3-dB coupler, and MRR-based balanced photodetector (PD) in a monolithic zero-change 45nm SOI CMOS. The link operates at 10 Gb/s with transmitter driver consuming 40fJ/bit and receiver with OMA sensitivity of 15.1dBm consuming 450fJ/bit. The laser-forwarded BPSK link improves the laser power budget by $\sim 6$ dB compared to direct detection NRZ link with same components.

Modern sub-28nm CMOS process nodes, namely FinFET and thin-body silicon-on-insulator have front-end layer thicknesses that are too thin to confine an optical mode. Integration of silicon photonics in these nodes necessitates the development of a deposition process that forms the waveguide structures with sufficient geometries after the CMOS front-end processing. As a step toward creating a photonics process module that can be added to these nodes, we demonstrate the integration of deposited polysilicon photonic platform in a low-power 65nm bulk CMOS process node in a 12” wafer foundry. This process module is designed with minimal number of additional masks to control the fabrication costs by optimizing the fabrication steps and reusing original process’s mask set (~5 additional masks among +40 masks required for the state-of-the-art CMOS nodes). The center of the platform is a polysilicon deposition step, which creates the waveguide layer, followed by a low-temperature crystallization process, which does not impact the electronics. All the passive and active photonic devices are fabricated by patterning and doping this layer. Transistor’s source/drain doping implantations are postponed after finishing and doping photonic polysilicon in order to avoid affecting transistors and reusing the implantation masks for doping active photonic devices as well. The waveguide loss ranges from 10-20dB/cm at 1310nm wavelength. To ease the loss optimization at wafer-scale, deep trench isolation has been added in photonic rows to optically isolate photonics from lossy silicon bulk. Grating couplers are used to couple in/out the light into the chip with 5dB loss. Micro-ring depletion-mode modulators achieved Q-factors of >5k and ~1.6THz free spectral range (FSR) enabling 10 channels in DWDM links. Resonant defect-based photodetectors are utilized on the receive side with 10% quantum efficiency at 5V reverse bias. Our first system demonstrations in this platform are O-band wavelength division multiplexed (WDM) optical transceivers using ring-resonators. Chips are designed in a modular fashion with 64 transceiver macros supporting 4 stand-alone transmit and receive WDM rows each with up to 16 individual channels. Each macro contains about 0.5 million transistors including transceiver’s analog custom front-ends, a digital backend, and microrings’ thermal tuners synthesized by original CMOS technology’s IP standard cells. We have used a variety of available transistor types with different oxide-thicknesses and threshold voltages to optimize energy-efficiency of the electronics. We have characterized the transistor performance across the die and wafer by measuring the frequency of the ring-oscillators embedded in each macro, and observed that the normal distribution is consistent with the foundry provided models for the native CMOS process. Electronics are operating using nominal supply voltage of 1.2V. We achieved 10Gb/s transmission with 4.7dB extinction ratio, and bit-error-rates of below 1e-10 at 7Gb/s with -3dBm sensitivity per channel. Total electrical energy-efficiency is about 600fJ/b (100fJ/b for Tx and 500fJ/b for Rx).

The realization of a low-cost and robust optical beam-steering platform is a key enabler for a number of applications, including light detection and ranging (LIDAR) and free-space optical communications (FSO). Optical phased arrays (OPAs) have emerged as a promising solution, due to advancements in photonic integrated circuits (PIC) foundry processes, which have enabled high-precision fabrication of PICs with a large number of components [1]–[3]. In order to meet steering range and directivity requirements in systems such as autonomous vehicles, a clear path to scaling OPAs to millimeter-scale apertures with thousands of tight-pitched antenna elements is critical. As the element count grows, independent phase control for each element becomes crucial since maintaining coherence between elements becomes more difficult due to process variations. Moreover, independent control allows for unique system capabilities, such as multi-beam formation and converging/adaptive beams, which makes OPAs a particularly attractive solid-state beamforming technology.

We investigate the feasibility of single-shot Toffoli- and Fredkin-gate realizations in qubit arrays with Heisenberg-type exchange interactions between adjacent qubits. As follows from the Lie-algebraic criteria of controllability, such an array is rendered completely controllable -- equivalent to allowing universal quantum computation -- by a Zeeman-like control field with two orthogonal components acting on a single "actuator" qubit. Adopting this local-control setting, we start our analysis with piecewise-constant control fields and determine the global maxima of the relevant figure of merit (target-gate fidelity) by combining the multistart-based clustering algorithm and quasi-Newton type local optimization. We subsequently introduce important practical considerations, such as finite frequency bandwidth of realistic fields and their leakage away from the actuator. We find the shortest times required for high-fidelity Toffoli- and Fredkin-gate realizations and provide comparisons to their respective two-qubit counterparts -- controlled-NOT and exponential-SWAP. In particular, the Toffoli-gate time compares much more favorably to that of controlled-NOT than in the standard decomposition-based approach. This study indicates that the use of the single-shot approach can alleviate the burden on control-generating hardware in future experimental realizations of multi-qubit gates., project started in 2016 and completed -- after a long interruption -- in 2018

Intensive research in the field of mathematical modeling of hydraulic servo systems has shown that their mathematical models have many important details which cannot be included in the model. Due to impossibility of direct measurement or calculation of dimensions of certain components, leakage coefficients or friction coefficients, it was supposed that parameters of the hydraulic servo system are random (stochastic nature). On the other side, it has been well known that the hydraulic servo cylinder can be approximated by a linear model with time-varying parameters. An estimation of states and time-varying parameters of linear state space models is of practical importance for fault diagnosis and fault tolerant control. Previous works on this topic consider estimation in Gaussian noise environment, but not in the presence of outliers. The known fact is that the measurements have inconsistent observations with the largest part of the observation population (outliers). They can significantly make worse the properties of linearly recursive algorithms which are designed to work in the presence of Gaussian noises. This paper proposes the strategy of parameter-state robust estimation of linear state space models in presence of non-Gaussian noises. The case of robust estimation of states and parameters of linear systems with parameter faults is considered. Because of its good features in robust filtering, the extended Masreliez-Martin filter represents a cornerstone for realization of the robust algorithm. The good features of the proposed robust algorithm to identification of the hydraulic servo cylinder are illustrated by intensive simulations.

This work investigates the asynchronous fault detection filtering issue for discrete-time piecewise homogeneous Markov jump systems. By resorting to a dual hidden Markov model, an asynchronous fault detection filter is presented which can follow up the system modes. Utilizing the stochastic Lyapunov function approach, sufficient conditions are devised to make the resultant Markov jump systems be stochastically stable and hold a specified H ∞ performance level. Finally, a real-time experiment based on the automotive electronic throttle body is employed to validate the devised asynchronous fault detection filter which can find the faults after the occurrences in the case of no false alarm.

Woodworking industry is increasingly characterized by processing complex spatial forms with high accuracy and high speeds. The use of parallel robot platforms with six degrees of freedom gains more significance. Due to stricter requirements regarding energy consumption, easy maintenance and environmental safety, parallel platforms with pneumatic drives become more and more interesting. However, the high precision tracking control of such systems represents a serious challenge for designers. The reason is found in complex dynamics of the mechanical system and strong nonlinearity of the pneumatic system. This paper presents an optimal control design for a pneumatically driven parallel robot platform. The Proportional-Integral-Derivative (PID) algorithm with feedback linearization is used for control. The parameter search method is based on a firefly algorithm due to the empirical evidence of its superiority in solving various nonconvex problems. The simulation results show that the proposed optimal tuned cascade control is effective and efficient. These results clearly demonstrate that the proposed control techniques exhibit significant performance improvement over classical and widely used control techniques.

The identification of ARX models with constrained output variance in the presence of non-Gaussian distribution of measurements is proposed in this paper. In the presence of non-Gaussian noise, the Masreliez–Martin filter (robust Kalman filter) is the natural frame for identification of model parameters. For the purpose of increasing the practical value of the filter, a heuristic modification is performed. Also, an optimal input is obtained by a minimum variance controller with a Gaussian reference signal. A fundamental issue in experiment design is that the optimal input in general depends on system parameters to be estimated. In order to realize obtained optimal input, authors have proposed a two-stage adaptive procedure, where one iterates between parameter estimation, on the one side, and experiment design using the current parameter estimates, on the other. The practical behavior of new approach to optimal input design for robust identification of ARX models is shown by intensive simulations.

Spintronic devices have been spotlighted due to their nonvolatility and potential for low-voltage operation. However, their potential performance and energy efficiency require greater scrutiny. In this paper, a circuit-level energy-performance analysis is used to derive the design requirements for a spintronic magnetic tunnel junction logic device, mLogic , for pipelined logic applications. An analytical equation for the domain wall mobility of mLogic is derived to predict the performance of future designs and used to point to key directions for further device improvement. We show that the energy dissipation of a logic pipeline under delay constraints is a convex function of the write/read-path resistance ratio and the supply voltage. Scaling the supply voltage can reduce the energy dissipation at the expense of switching speed, but is limited by an extrinsic pinning effect and thermal noise. The energy reduction by maximizing the tunnel magnetoresistance (TMR) will be saturated for TMR larger than 100. But maximizing TMR can mitigate the thermal noise limit of scaling the supply voltage. The energy gap between MOSFETs and mLogic gets smaller for more advanced technology nodes. With 32-nm technology, a future mLogic design can be more optimal than MOSFETs in low-power and low-performance applications, such as emerging Internet-of-Things devices.

The microring resonator is critical for dense wavelength division multiplexed (DWDM) chip-to-chip optical I/O, enabling modulation and channel selection at the $\upmu\text{m}$ -scale suitable for a VLSI chip. Microring-based links, however, require active tuning to counteract process and thermo-optic variations. Here, we present a bit-statistical tuner that decouples tracking of optical one- and zero-levels to realize non-dc-balanced data transmission, an “eye-max”-locking controller, and self-heating cancellation without need for a high-speed sensing frontend. We implement the tuner on a 45 nm CMOS-SOI process with monolithically integrated photonic devices and circuits. The tuner consumes 0.74 mW in the logic while achieving a record 524 GHz ( $>$ 50 K temperature) tuning range at $3.8\;\upmu\text{W/GHz}$ heater efficiency. To our knowledge, this is the highest range and heater efficiency reported by an on-chip closed-loop thermal tuner to date. The tuner integrates with a 5 Gb/s 30 fJ/bit monolithic microring transmitter, achieving wavelength-lock and immunity to both tracking failures and self-heating events caused by arbitrary, non-dc-balanced bitstreams. In addition, the tuner provides critical functionality for an 11- $\lambda$ DWDM transmitter macro capable of $11\ \times\ 8$ Gb/s bandwidth on a fiber. Together with the transmitter, a 10 Gb/s on-chip monolithic optical receiver with $10^{-{\textbf{12}}}$ BER sensitivity of $9\;\upmu\text{A}$ at 10 Gb/s enables a sub-pJ/bit 5 Gb/s optical chip-to-chip link, with the bit-statistical tuner providing thermally robust microring operation.

A fully functional Si photonics and 65-nm complementary metal-oxide semiconductor (CMOS) heterogeneous three-dimensional (3-D) integration is demonstrated for the first time in a 300-mm production environment. Direct oxide wafer bonding was developed to eliminate voids between silicon on insulator photonics and bulk Si CMOS wafers. A via-last, Cu through-oxide via 3-D integration was developed for low capacitance electrical connections with no impact on the CMOS performance. The 3-D yield approaching 100% was demonstrated on >20,000 via chains.

This paper presents a cascade load force control design for a parallel robot platform. A parameter search for a proposed cascade controller is difficult because there is no methodology to set the parameters and the search space is broad. A parameter search based on cuckoo search (CS) is suggested to effectively search parameters of the cascade controllers. The control design problem is formulated as an optimization problem under constraints. Typical constraints, such as mechanical limits on positions and maximal velocities of hydraulic actuators as well as on servo-valve positions, are included in the proposed algorithm. The optimal results are compared to the state-of-the-art algorithms for these problem instances (NP-hard and constrained optimization problems). Simulation results also show that applied optimal tuned cascade control algorithm exhibits a significant performance improvement over classical tuning methods.

Summary Intensive research in the field of mathematical modeling of pneumatic servo drives has shown that their mathematical models are nonlinear in which many important details cannot be included in the model. Owing the influence of the combination of heat coefficient, unknown discharge coefficient, and change of temperature, it was supposed that parameters of the pneumatic cylinder are random (stochastic parameters). On the other side, it has been well known that the nonlinear model can be approximated by a linear model with time-varying parameters. Due to the aforementioned reasons, it can be assumed that the pneumatic cylinder model is a linear stochastic model with variable parameters. In practical conditions, in measurements, there are rare, inconsistent observations with the largest part of population of observations (outliers). Therefore, synthesis of robust algorithms is of primary interest. In this paper, the robust recursive algorithm for output error models with time-varying parameters is proposed. The convergence property of the proposed robust algorithm is analyzed using the methodology of an associated ordinary differential equation system. Because ad hoc selection of model orders leads to overparameterization or parsimony problem, the robust Akaike's criterion is proposed to overcome these problems. By determining the least favorable probability density for a given class of probability distribution represents a base for design of the robust version of Akaike's criterion. The behavior of the proposed robust identification algorithm is considered through intensive simulations that demonstrate the superiority of the robust algorithm in relation to the linear algorithms (derived under an assumption that the stochastic disturbance has a Gaussian distribution). The good practical values of the proposed robust algorithm to identification of the pneumatic cylinder are illustrated by experimental results. Copyright © 2016 John Wiley & Sons, Ltd.

In this work we select most probable reactions of alkali metal ion Na+ with dimethoxyethane (DXE) molecule. Appropriate gas phase enthalpies of formation for the products were used to calculate scattering cross section as a function of kinetic energy with Denpoh-Nanbu theory. Calculated cross sections were compared with existing experimental results obtained by guided ion beam tandem mass spectrometry. Three body association reactions of ions with DXE is studied and compared to experimental results. Calculated cross sections were used to obtain transport parameters for alkali metal ion in DXE gas. [Projekat Ministarstva nauke Republike Srbije, br. ON 171037 i br. III 410011]

This paper considers the robust algorithm for identification of OE (output error) model with constrained output in presence of non-Gaussian noises. In practical conditions, in measurements there are rare, inconsistent observations with the largest part of population of observations (outliers). The synthesis of robust algorithms is based on Huber׳s theory of robust statistics. Also, it is known fact that constraints play a very important role in many practical cases. If constraints are not taken into consideration, the control performance can corrupt and safety of a process may be at risk. The practical value of proposed robust algorithm for estimation of OE model parameters with constrained output variance is further increased by using an optimal input design. It is shown that the optimal input can be obtained by a minimum variance controller whose reference is a white noise sequence with known variance. A key problem is that the optimal input depends on system parameters to be identified. In order to be able to implement the proposed optimal input, the adaptive two-stage procedure for generating the input signal is proposed. Theoretical results are illustrated by simulations which show significant increasing of accuracy in parameter estimates of the OE model by using the robust identification procedure in relation to the linear identification algorithm for OE models. Also, it can be seen that the convergence rate of the robust algorithm is further increased by using the optimal input design, which increases the practical value of proposed robust procedure.

Plasma etching represents one of the critical steps in manufacturing of integrated circuits. Further optimization of plasma equipment is needed since new generations in technology require different plasma chemistry. In this paper, we will study the influence of radicals on the plasma characteristics, since it was often neglected in plasma models. The radicals dominate attachment of electrons as the basic etching mixture is weakly electronegative and they also affect the drift velocity through modified momentum balance. We have used numerical solutions to the Boltzmann equation and Monte Carlo simulations (MCS) to determine the transport coefficients of electrons.