Iterative Design Algorithm Research Articles

Robust fixed-order [formula omitted] controller synthesis for multi-phase converters with input voltage feed-forward

Generated DC voltages of batteries, fuel cells, or supercapacitors as power sources connected in series with DC-DC converters change according to operating conditions. In such cases, the output voltage stability of the multi-phase DC-DC converters is an issue, particularly being subject to noises in the input voltage and load current disturbances. Input voltage feed-forward (IVFF) compensation approaches can improve the output voltage stability of the DC-DC converters in such scenarios. This paper proposes a robust fixed-order H∞ based IVFF control framework by considering sensor noise, input voltage, and load current disturbances to improve output voltage stability and reference tracking performance. The mismatched disturbance transfer functions depending on parasitic resistances are derived to include in the controller synthesis process. An iterative convex-concave optimization-based robust fixed-order controller design algorithm is proposed to optimize the feedback and feed-forward controllers under the stability and performance constraints. This algorithm allows the designing of the controllers simultaneously. The feed-forward controller, whose structure is independent of the system transfer functions, is optimized without any model inversion or extra design steps. The performance of the proposed controller is compared with an integral linear quadratic regulator controller, a sliding mode PID controller augmented with a nonlinear disturbance observer, and a fixed-order robust controller designed with non-smooth techniques. Real-time implementation of the fixed-order control systems is performed on a 2-leg 200 W interleaved boost converter. The performances are discussed under external disturbance effects to show the validity of the proposed control approach.

Iterative Design Algorithm for Robust Disturbance-Rejection Control

An iterative design algorithm is developed for robust disturbance–rejection control of uncertain systems with time-varying parameter perturbations in this paper. For more design degrees of freedom, a generalized equivalent-input-disturbance estimator is adopted to approximate the effect of both disturbances and uncertainties. By the bound real lemma, the H∞ norm is used to evaluate the robust disturbance–rejection performance of the closed-loop uncertain system. To avoid the constraints introduced by the widely used commutative condition, the control gains are divided into two groups and calculated by steps. Further, two robust quadratic stability conditions are derived, and an iterative design algorithm is developed to optimize the robust H∞ disturbance–rejection performance. Finally, the effectiveness and advantages of the developed method are demonstrated by a case study of a suspension system of modern vehicles.

Reduced-Order Static -Gain Control of Rotorcraft with Guaranteed Rotor–Fuselage Stability

A reduced-order control design method for rotorcraft is proposed in this work. Explicit constraints on the closed-loop fuselage–rotor stability are considered in the fuselage dynamics-based control design, aiming for the -gain performance for the fuselage dynamics under pole placement constraints and the guaranteed fuselage–rotor stability. Sufficient conditions for the control design problem are presented, formulated as a feasibility problem based on bilinear matrix inequalities. Such an NP-hard problem is approximated using convex relaxation techniques and the cone complementary linearization method, leading to an iterative control design algorithm. An altitude–velocity vector tracking concept is also proposed in this work, integrated with the proposed control design method to design rotorcraft trajectory controllers. Performance of the resultant rotorcraft trajectory controllers is examined via numerical simulations against representative maneuvers from the ADS-33E specification.

User Grouping, Precoding Design, and Power Allocation for MIMO-NOMA Systems

In this paper, we study user grouping, precoding design, and power allocation for multiple-input multiple-output (MIMO) nonorthogonal multiple access (NOMA) systems. An optimization problem is formulated to the maximize the sum rate under a transmit power constraint at a base station and rate constraints on users, which are nonconvex and combinatorial and thus very challenging to solve. To tackle this problem, we carry out the optimization in two steps. In the first step, exploiting the machine learning technique, we develop an efficient iterative algorithm for user grouping and precoding design. In the second step, we develop a power-allocation scheme in closed form by recasting the original problem into a useful and tractable convex form. The numerical results demonstrate that the proposed joint scheme, including user grouping, precoding design, and power allocation, considerably outperforms the existing schemes in terms of sum rate maximization, which increases the sum-rate up to 8–18%. In addition, the results show the larger the number of antennas or users, the bigger the performance gap, at the cost of less computational complexity.

Energy-efficient full-duplex UAV relaying with trajectory optimization and power control in maritime communication environments

This paper solves an energy-efficient optimization problem of a fixed-wing unmanned aerial vehicle (UAV) assisted full-duplex mobile relaying in maritime communication environments. Taking the speed and the acceleration of the UAV and the information-causality constraints into consideration, the energy-efficiency of the system under investigation is maximized by jointly optimizing the UAV's trajectory and the individual transmit power levels of the source and the UAV relay nodes. The optimization problem is non-convex and thus cannot be solved directly. Therefore, it is decoupled into two subproblems. One sub-problem is for the transmit power control at the source and the UAV relay nodes, and the other aims at optimizing the UAV's flight trajectory. By using the Lagrangian dual and Dinkelbach methods, the two sub-problems are solved, leading to an iterative algorithm for the joint design of transmit power control and trajectory optimization. Computer simulations demonstrated that by conducting the proposed algorithm, the flight trajectory of the UAV and the individual transmit power levels of the nodes can be flexibly adjusted according to the system conditions, and the proposed algorithm can achieve significantly higher energy efficiency as compared with the other benchmark schemes.

Newton-series-based observer–predictor control for disturbed input-delayed discrete-time systems

This paper deals with the problem of predicting the future state of discrete-time input-delayed systems in the presence of unknown disturbances that can affect both the state and the output equations of the plant. Since the disturbance is unknown, computing an exact prediction of the future plant states is not possible. To circumvent this problem, we propose using a high-order extended Luenberger-type observer for the plant states, disturbances, and their finite difference variables, combined with a new equation for computing the prediction based on Newton’s series from the calculus of finite differences. Detailed performance analysis is carried out to show that, under certain assumptions, both enhanced prediction and improved attenuation of the unknown disturbances are achieved. Linear matrix inequalities (LMIs) are employed for the observer design to minimize the prediction errors. A stabilization procedure based on an iterative design algorithm is also presented for the case where the plant is affected by time-varying uncertainties. Examples from the literature illustrate the advantages of the scheme.

Robust Downlink Precoding for LEO Satellite Systems With Per-Antenna Power Constraints

Downlink precoding has been widely accepted as a crucial technique in low-earth-orbit (LEO) satellite communications due to its advantages in improving the space division multiplexing capability and enhancing spectrum efficiency. Because of the strong spatial directivity in the satellite channels, the downlink precoder can be efficiently designed based on the angle of departure (AoD)-based channel state information (CSI). However, acquiring perfect AoD-based CSI is challenging due to the angle estimation error and attitude jitter of the LEO satellites. In light of this, this paper investigates the precoder design for the LEO satellite downlinks with imperfect AoD-based CSI. For robust design, we treat the angle deviations of each user as random variables, which are supposed to exhibit a particular distribution, then integrate them within a certain range to achieve the robustness of the proposed downlink precoder against the angle mismatch. Instead of using the sum-power constraint on the transmit antennas, we adopt a more realistic per-antenna power constraint (PAPC). Accordingly, we formulate the problem to maximize the ergodic sum rate of the system. By leveraging the corresponding Lagrangian formulation and identifying the optimal precoder structure as the solution to a generalized eigenvalue problem, an iterative algorithm for the robust precoder design is derived. To reduce the computational complexity, a low-complexity version of the proposed algorithm is implemented based on deep learning techniques to meet the high demand for real-time applications in LEO satellite systems. Simulation results verify the effectiveness of the proposed precoding approach.

Analysis of iterative design algorithms for achromatic deterministic diffusers.

Almost achromatic off-axis deterministic diffusers are flexible micro-optical elements that can produce arbitrary light distributions. They can, for example, increase the efficiency of projection systems when used as screens. These elements still pose a challenge in both design and manufacturing due to the often occurring necessity of a tessellated surface. We estimate the element depth necessary to achieve achromatic behavior using a blazed grating as a substitute model. We then show the design of a tessellated, deep diffuser using an algorithm published by Brenner [Opt. Lett.25, 31 (2000)OPLEDP0146-959210.1364/OL.25.000031]. To further enhance the diffraction efficiency under manufacturing constraints, we introduce a modification that preserves the tessellation edges during design. We show that the modified algorithm yields an increase in efficiency and demonstrate the quasi-achromatic behavior of the resulting height profile.

Capacitated Air/Rail Hub Location Problem With Uncertainty: A Model, Efficient Solution Algorithm, and Case Study

Well-designed multi-modal transportation networks are crucial for our connected world. For instance, the excessive construction of railway tracks in China, at speeds up to 350 km/h, makes it necessary to consider the interaction of rail with air transportation for network design. In this study, we propose a model for an air/rail multi-modal, multiple allocation hub location problem with uncertainty on travel demands. Our model is unique in that it integrates features from the existing literature on multi-modal hub location problem (including hub-level capacities, link capacities, direct links, travel cost and time, transit costs and uncertainty), which have not been considered simultaneously, given its high computational complexity. We formulate this model with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n^{4})$ </tex-math></inline-formula> variables and show that the implementation of a Benders decomposition algorithm is inherently hard, because of the cubic number of variables in the master problem. Furthermore, we derive an iterative network design algorithm and additional improvement strategies: MMHUBBI which resolves a restricted problem by the solver CPLEX and MMHUBBI-DIRECT which re-designs the transportation network by a heuristic. Our evaluation on real-world dataset for Chinese domestic transportation shows that MMHUBBI provides a significant speed-up on all instances, compared to using CPLEX, while obtaining near-optimal solutions. MMHUBBI-DIRECT further reduces the runtime/memory usage but provides solutions with worse quality. We believe that our study contributes towards the design of more realistic multi-modal hub location problems.

Stochastic event-based LQG control: An analysis on strict consistency

This paper studies the discrete-time linear–quadratic-Gaussian problem under partial-information-based sensors and event-based transmissions. By constructing a special form of stochastic event-based protocols, the optimal control policy is obtained, which minimizes an average quadratic cost function under a fixed transmission protocol. Then, analytical expressions of the corresponding transmission and control performance are derived. To evaluate the efficiency of event-based protocols, the concept of strict consistency is introduced, which requires that the control performance of event-based protocols be strictly better than that of the periodic protocol under the same transmission rate. Based on the obtained analytical expressions, the strict consistency is proved by showing that the periodic protocol corresponds to a local maximum point in the design space of parameters. To further improve the control performance of stochastic event-based protocols, an iterative parameter design algorithm (with finite iterations) is proposed for ensuring a strict reduction in cost functions after each iteration. Finally, numerical simulations are provided to illustrate the theoretical results.

Static Linear Anti-Windup Design With Sign-Indefinite Quadratic Forms

We design static anti-windup gains to mitigate the effect of input saturation in linear output feedback closed loops. The design is conducted with the help of a non-quadratic Lyapunov function involving sign-indefinite quadratic forms, which allows for additional degrees of freedom to be exploited, for the anti-windup gain design. Synthesis conditions, combining the use of sign-indefinite quadratic forms and several sector bound properties are stated in the form of bilinear matrix inequalities ensuring global exponential stability of the closed-loop system. An iterative design algorithm is then devised, based on the resolution of a sequence of semidefinite programs. The superiority of the proposed technique over classical quadratic constructions is illustrated on an example borrowed from the literature, where quadratic positive definite functions are ineffective.

User Association and Hybrid Beamforming Designs for Cooperative mmWave MIMO Systems

Hybrid analog and digital beamforming has emerged as a key enabling technology for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) communication systems since it can balance the trade-off between system performance and hardware efficiency. Owing to the strong ability of central control, cooperative networks show great potential to enhance the spectral efficiency of mmWave communications. In this paper, we consider cooperative mmWave MIMO systems and propose user association and hybrid beamforming design algorithms for three typical hybrid beamforming architectures. The central processing unit (CPU) of the cooperative networks first matches the service pairs of base stations (BSs) and users. Then, an iterative hybrid beamforming design algorithm is proposed to maximize the weighted achievable sum-rate performance of the mmWave MIMO system with fully connected hybrid beamforming architecture. Moreover, a heuristic analog beamforming design algorithm is introduced for the fixed subarray hybrid beamforming architecture. In an effort to further exploit multiple-antenna diversities, we also consider the dynamic subarray architecture and propose a novel antenna design algorithm for the analog beamforming design. Simulation results illustrate that the proposed hybrid beamforming algorithms achieve a significant performance improvement than other existing approaches and the dynamic subarray architecture has great advantages of improving the energy efficiency (EE) performance.

An iterative two-stage heuristic algorithm for a bilevel service network design and pricing model

Building upon earlier research, we revisit a bilevel formulation of service design and pricing for freight networks, with the aim of investigating its algorithmic aspects. The model adds substantial computational challenges to the existing literature, as it deals with general integer network design variables. An iterative heuristic algorithm is introduced, based on the concepts of inverse optimization and neighbourhood search. The procedure alternates between two versions of restricted formulations of the model while inducing promising changes into the service assignments. The approach has proven a high performance for all of the considered real-world instances. Its efficiency rests on its ability to deliver results within a close proximity to those obtained by the exact solver in terms of quality, yet within a significantly smaller amount of time, and to land feasible solutions for the large-sized instances that could not be previously solved. In line with the sustainable transport goals, a deeper observation of the transport management side highlights the strategy of the algorithm favouring freight consolidation and achieving high load factors.

Evolutionary Design Method for a Conducting Solid Cooled by Combined Free Convection and Radiation

Abstract An iterative design algorithm is used to adjust the shape of a conducting solid body that is subjected to a surface heat flux and cooled simultaneously by free convection and radiation in order to reduce the overall thermal resistance. Parametric simulations are carried out over a range of domain dimensions and emissivity values to determine the sensitivity of (i) the predicted solid shape and (ii) the overall thermal resistance to the relative strength of convection or radiation. Results show that, for the conditions considered, surface radiation has a significant influence on the predicted optimal solid geometry and overall thermal resistance.

Energy Aware Trajectory Optimization for Aerial Base Stations

By fully exploiting the mobility of unmanned aerial vehicles (UAVs), UAV-based aerial base stations (BSs) can move closer to ground users to achieve better communication conditions. In this paper, we consider a scenario where an aerial BS is dispatched for satisfying the data request of a maximum number of ground users, weighted according to their data demand, before exhausting its on-board energy resources. The resulting trajectory optimization problem is a mixed integer non-linear problem (MINLP) which is challenging solve. Specifically, there are coupling constraints which cannot be solved directly. We exploit a penalty decomposition method to reformulate the optimization formulation into a new form and use block coordinate descent technique to decompose the problem into sub-problems. Then, successive convex approximation technique is applied to tackle non-convex constraints. Finally, we propose a double-loop iterative algorithm for the UAV trajectory design. In addition, to achieve a better coverage performance, the problem of designing the initial trajectory for the UAV trajectory is considered. In the results section, UAV trajectories with the proposed algorithm are shown. Numerical results show the coverage performance with the proposed schemes compared to the benchmarks.

Sum-Throughput Maximization in NOMA-Based WPCN: A Cluster-Specific Beamforming Approach

Wireless power transfer is a promising solution for wireless networks composed of Internet-of-Things (IoT) devices that may suffer from insufficient battery capacity. A wireless powered communication network (WPCN) is a framework to design energy-constrained networks such as IoT networks. In this article, we consider WPCNs consisting of a hybrid access point (H-AP) and energy harvesting users, all equipped with multiple antennas. A H-AP transfers energy to the users using energy beamforming in the downlink, and the users transmit information using the harvested energy in the uplink, where nonorthogonal multiple access (NOMA) transmission is employed. For the uplink NOMA transmission, users are grouped into multiple clusters by cluster-specific beamforming. In particular, signal alignment is exploited for the beamforming so that the channels of users in a cluster are aligned in the same direction. By signal alignment, the number of messages decoded by successive interference cancellation (SIC) is reduced, which can be effective at lowering the decoding complexity and SIC error propagation. Due to the difficulty of jointly optimizing cluster-specific beamforming and time/energy resources for sum-throughput maximization, we determine the beamforming relying on signal alignment first, and then the resources are optimized for given beamforming. To be more specific, we propose a novel iterative algorithm for cluster-specific beamforming design followed by the sum-throughput maximization algorithm. Numerical results show the sum-throughput performance of the proposed scheme and its robustness toward SIC error propagation compared to existing schemes.

Optimize the Communication Cost of 5G Internet of Vehicles through Coherent Beamforming Technology

Edge computing, which sinks a large number of complex calculations into edge servers, can effectively meet the requirement of low latency and bandwidth efficiency and can be conducive to the development of the Internet of Vehicles (IoV). However, a large number of edge servers mean a big cost, especially for the 5G scenario in IoV, because of the small coverage of 5G base stations. Fortunately, coherent beamforming (CB) technology enables fast and long‐distance transmission, which gives us a possibility to reduce the number of 5G base stations without losing the whole network performance. In this paper, we try to adopt the CB technology on the IoV 5G scenario. We suppose we can arrange roadside nodes for helping transferring tasks of vehicles to the base station based on the CB technology. We first give the mathematical model and prove that it is a NP‐hard model that cannot be solved directly. Therefore, we design a heuristic algorithm for an Iterative Coherent Beamforming Node Design (ICBND) algorithm to obtain the approximate optimal solution. Simulation results show that this algorithm can greatly reduce the cost of communication network infrastructure.

Design Considerations of Ultracapacitor Stack for Optimal Sizing of Energy Storage Systems in Contingency Applications

Ultracapacitors (UCs) today are playing an increasing role in the energy storage systems (ESSs) of several power conversion applications. In this article, a design method for optimal sizing of a UC stack is proposed, which minimizes the overall cost of the ESS. The problem is cast with normalized cost parameters of the ESS subsystem, using which the discharge ratio of the stack is optimized for the given ESS specifications. An iterative design algorithm is proposed which systematically engineers the stack parameters of unit cell capacitance, the number of series cells and parallel strings along with the initial and final stack voltage levels that meet the specified ESS requirements while also supplying the equivalent series resistance (ESR) loss. The iterative sizing process also factors in the maximum power transfer constraint imposed by the ESR to guarantee stable operation of the cost-optimal design even as the stack voltage varies during discharge. The design considerations, optimization framework, ESR effects, and the proposed sizing algorithm are synthesized in a generalized formulation in order to capture the variations in cost and subsystem parameter values that typically vary based on scaling requirements as well as the UC and power converter technology. Experimental results on a 4 kWmin UC stack validate the proposed cost-optimal design algorithm and the ESR induced voltage collapse.

Hybrid Beamforming With Dynamic Subarrays and Low-Resolution PSs for mmWave MU-MISO Systems

Analog/digital hybrid beamforming architectures with large-scale antenna arrays have been widely considered in millimeter wave (mmWave) communication systems because they can address the tradeoff between performance and hardware efficiency compared with traditional fully-digital beamforming. Most of the prior work on hybrid beamforming focused on fully-connected architecture or partially-connected scheme with fixed-subarrays, in which the analog beamformers are usually realized by infinite-resolution phase shifters (PSs). In this paper, we introduce a novel hybrid beamforming architecture with dynamic subarrays and hardware-efficient low-resolution PSs for mmWave multiuser multiple-input single-output (MU-MISO) systems. By dynamically connecting each RF chain to a non-overlap subarray via a switch network and PSs, we can exploit multiple-antenna and multiuser diversities to mitigate the performance loss due to the use of practical low-resolution PSs. An iterative hybrid beamformer design algorithm is first proposed based on fractional programming (FP), aiming at maximizing the sum-rate performance of the MU-MISO system. In an effort to reduce the complexity, we also present a simple heuristic hybrid beamformer design algorithm for the dynamic subarray scheme. Extensive simulation results demonstrate the advantages of the proposed hybrid beamforming architecture with dynamic subarrays and low-resolution PSs compared to existing fixed-subarray schemes.

A separable augmented Lagrangian algorithm for optimal structural design

We propose an iterative separable augmented Lagrangian algorithm (SALA) for optimal structural design, with SALA being a subset of the alternating directions of multiplier method (ADMM)–type algorithms. Our algorithm solves a sequence of separable quadratic-like programs, able to capture reciprocal- and exponential-like behavior, which is desirable in structural optimization. A salient feature of the algorithm is that the primal and dual variable updates are all updated using closed-form expressions. Since algorithms in the ADMM class are known to be very sensitive to scaling, we propose a scaling method inspired by the well-known ALGENCAN algorithm. Comparative results for SALA, ALGENCAN, and the Galahad LSQP solver are presented for selected test problems. Finally, although we do not exploit this feature herein, the primal and dual updates are both embarrassingly parallel, which makes the algorithm suitable for implementation on massively parallel computational devices, including general purpose graphical processor units (GPGPUs).