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This paper considers the problem of online optimization where the objective function is time-varying. In particular, we extend coordinate descent type algorithms to the online case, where the objective function varies after a finite number of iterations of the algorithm. Instead of solving the problem exactly at each time step, we only apply a finite number of iterations at each time step. Commonly used notions of regret are used to measure the performance of the online algorithm. Moreover, coordinate descent algorithms with different updating rules are considered, including both deterministic and stochastic rules that are developed in the literature of classical offline optimization. A thorough regret analysis is given for each case. Finally, numerical simulations are provided to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

This paper investigates the prescribed-time distributed Nash equilibrium seeking (DNES) problem for multi-agent noncooperation games. Based on the distributed motion-planning method and the gradient search, a class of prescribed-time DNES algorithms are developed for first and second-order multi-agent systems, respectively. They may ensure each player' s action converges to the Nash equilibrium (NE) point at a prescribed settling time in advance. Further, for the case when the velocity information of each agent is not available, an observer-based prescribed-time DNES algorithm is extended. Compared with existing works, the convergence time of proposed prescribed-time DNES algorithms in this paper can be assigned in advance by users according to task requirements. Besides, the designed DNES algorithms are sampled without continuous measurements, which means players can update their actions at each sampling moment to avoid the increasing cost brought by continuous information interaction among players. Finally, the effectiveness of algorithms proposed in this paper is verified by some numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, a randomized parallel algorithm is proposed to solve the distributed optimal consensus problem of multi-agent systems. Involving both the transient response and the final consensus state, the problem is described as a constrained non-separable optimization problem. Inspired by the randomized Jacobi proximal alternating direction method of multipliers, the proposed algorithm makes it possible for only a fraction of agents to solve their private subproblems in parallel at each iteration, which greatly saves computational resources and enhances running efficiency. The convergence analysis of the algorithm gives fully distributed convergence conditions. A trade-off between the convergence speed and resource savings is then obtained, where the convergence rate is estimated to be at least O 1 t . Furthermore, the algorithm can be accelerated to enjoy a convergence rate of O 1 t 2 by adaptively adjusting the auxiliary parameters properly. Numerical simulations demonstrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, a general framework is proposed for the analysis and characterization of observability and diagnosability of finite state systems. Observability corresponds to the reconstruction of the system’s discrete state, while diagnosability corresponds to the possibility of determining the past occurrence of some particular states, for example faulty states. A unifying framework is proposed where observability and diagnosability properties are defined with respect to a critical set, i.e. a set of discrete states representing a set of faults, or more generally a set of interest. These properties are characterized and the involved conditions provide an estimation of the delay required for the detection of a critical state, of the precision of the delay estimation and of the duration of a possible initial transient where the diagnosis is not possible or not required. Our framework makes it possible to precisely compare some of the observability and diagnosability notions existing in the literature with the ones introduced in our paper, and this comparison is presented. [ABSTRACT FROM AUTHOR]

This paper addresses the classical problem of determining the set of possible states of a linear discrete-time SISO system subject to bounded disturbances, from measurements corrupted by bounded noise. These so-called uncertainty sets evolve with time as new measurements become available. We present two theorems which give a complete description of the relationship between uncertainty sets at two successive time instants, and this yields an efficient algorithm for recursively updating uncertainty sets. Numerical simulations demonstrate performance improvements over existing exact methods. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed receding horizon control (RHC) problem of a vehicle platoon with nonlinear dynamics and subject to system constraints, where each vehicle can communicate with its immediate predecessor and follower. A novel optimization problem and detailed distributed RHC algorithm are designed in order to keep a platoon formation, and further to ensure neighbor γ -gain stability (which is a new notion proposed in this paper and generalizes the string stability). The sufficient conditions on ensuring closed-loop stability and neighbor γ -gain stability are established, respectively. Finally, simulation studies are provided to verify the theoretical results. It is shown that it is possible to achieve certain control performance (i.e., γ -gain stability) and keep a formation simultaneously for the nonlinear vehicle platoon using distributed RHC. [ABSTRACT FROM AUTHOR]

In this paper, distance-based formation control laws for the multi-agent systems are proposed when the reference velocity is unknown to the followers. Especially, not only the shape but the orientation of formation is also controlled. Each agent measures only the relative positions of neighbors with respect to their own local coordinate system. The local coordinate systems in each agent are not aligned and unknown to the other agents; thus the control laws are completely decentralized. Using the measured local information, the unknown reference velocity is estimated by applying an adaptive method. The shape and orientation of formation are controlled based on the estimated reference velocity. The stability and convergence of the system are analyzed mathematically and verified through numerical simulation. [ABSTRACT FROM AUTHOR]

Many recently developed data-driven fault estimation methods are restricted to minimum-phase systems so that their practical applications are limited. In this paper, the data-driven fault estimation for non-minimum phase (NMP) systems is studied, for which the main difficulty is that the unstable zeros of an NMP system will result in a growing fault-estimation error. To deal with this problem, the inverse of an NMP system is equivalently formulated as a mixed causal and anti-causal system, and the proposed fault estimator is the sum of a stable causal filter and a stable anti-causal filter. The proposed fault estimator is shown to be asymptotically unbiased and its performance is demonstrated by numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper we introduce a global differentiator based on higher-order sliding modes (HOSM) and dynamic gains to solve the problem of trajectory tracking via output-feedback for a class of uncertain nonlinear plants with arbitrary relative degree and disturbances. Norm observers for the unmeasured state are employed to dominate the disturbances as well as to adapt the gains of the proposed differentiator since the nonlinearities may be state-dependent and time-varying. Uniform global stability and robust exact tracking are guaranteed employing the proposed HOSM based exact differentiator. The obtained results are not restricted to first-order sliding mode control feedback, but applies for second order sliding mode algorithms (twisting, super-twisting and variable gain super-twisting) as well as quasi-continuous HOSM finite-time controllers. Simulations with an aircraft pitch-control application illustrate the claimed properties, even in the presence of measurement noise. [ABSTRACT FROM AUTHOR]

This paper studies the distributed average tracking problem for multiple time-varying signals generated by linear dynamics, whose reference inputs are nonzero and not available to any agent in the network. In the edge-based framework, a pair of continuous algorithms with, respectively, static and adaptive coupling strengths is designed. Based on the boundary layer concept, the proposed continuous algorithm with static coupling strengths can asymptotically track the average of multiple reference signals without the chattering phenomenon. Furthermore, for the case of algorithms with adaptive coupling strengths, average tracking errors are uniformly ultimately bounded and exponentially converge to a small adjustable bounded set. Finally, a simulation example is presented to show the validity of theoretical results. [ABSTRACT FROM AUTHOR]

A class of timed discrete event systems can be modeled by using Timed-Event Graphs, a class of timed Petri nets that can have its firing dynamic described by using an algebra called “Max-plus algebra”. For this kind of systems it may be desirable to enforce some timing constraints in steady state. In this paper, this problem is called a “max-plus regulation problem”. In this context we show a necessary condition for solving these regulation problems and in addition that this condition is sufficient for a large class of problems. The obtained controller is a simple linear static state feedback and can be computed using efficient pseudo-polynomial algorithms. Simulation results will illustrate the applicability of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper studies a distributed state estimation problem for a network of linear dynamic systems (called nodes), which evolve autonomously, but their measurements are coupled through neighborhood interactions. Power networks are typical networked systems obeying such features, with other examples including traffic networks, sensor networks and many multi-agent systems. We develop a new distributed state estimation approach, for each node to update its local state. The core of this distributed approach is a distributed maximum a posteriori (MAP) estimation technique, which delivers a globally optimal estimate under certain assumptions. We apply the distributed approach to an IEEE 118-bus system, and compare it with a centralized approach, which provides the optimal state estimate using all the measurements, and with a local state estimation approach, which uses only local measurements to estimate local states. Simulation results show that under different scenarios including normal operation, bad measurements and sudden load change, the distributed approach is clearly more accurate than the local state estimation approach and distributed static state estimation approach. Although the result is a bit less accurate than that by a centralized algorithm, the distributed algorithm enjoys low computational complexity and communication load, and is scalable to large power networks. [ABSTRACT FROM AUTHOR]

Time-varying formation analysis and design problems for multi-agent systems with general linear dynamics and switching directed interaction topologies are investigated. Different from the previous results, the formation in this paper can be defined by specified piecewise continuously differentiable vectors and the switching topologies are directed. Firstly, necessary and sufficient conditions for general linear multi-agent systems with switching directed topologies to achieve time-varying formations are proposed, where a description of the feasible time-varying formation set and approaches to expand the feasible formation set are given. Then an explicit expression of the time-varying formation reference function is derived to describe the macroscopic movement of the whole formation. An approach to assign the motion modes of the formation reference is provided. Moreover, an algorithm consisting of four steps to design the formation protocol is presented. In the case where the given time-varying formation belongs to the feasible formation set, it is proven that by designing the formation protocol using the proposed algorithm, time-varying formation can be achieved by multi-agent systems with general linear dynamics and switching directed topologies if the dwell time is larger than a positive threshold. Finally, numerical simulations are presented to demonstrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper proposes a novel distributed event-triggered algorithmic solution to the multi-agent average consensus problem for networks whose communication topology is described by weight-balanced, strongly connected digraphs. The proposed event-triggered communication and control strategy does not rely on individual agents having continuous or periodic access to information about the state of their neighbors. In addition, it does not require the agents to have a priori knowledge of any global parameter to execute the algorithm. We show that, under the proposed law, events cannot be triggered an infinite number of times in any finite period (i.e., no Zeno behavior), and that the resulting network executions provably converge to the average of the initial agents’ states exponentially fast. We also provide weaker conditions on connectivity under which convergence is guaranteed when the communication topology is switching. Finally, we also propose and analyze a periodic implementation of our algorithm where the relevant triggering functions do not need to be evaluated continuously. Simulations illustrate our results and provide comparisons with other existing algorithms. [ABSTRACT FROM AUTHOR]

In this paper, the Euler’s discretization of the second order sliding mode control system with the twisting algorithm is studied. It shows that, for all values of discretization steps, initial conditions and allowable parameter setting, the system trajectories are always bounded. It also shows that for the uncertain systems, under a mild condition, the system trajectories are also bounded. Further, the periodic behaviors and limit cycles of the system trajectories are explored with conditions for the existence of periodic orbits formulated. Extensive simulation examples are given to show typical trajectories, and comparison between the first order and second order sliding mode control systems. [ABSTRACT FROM AUTHOR]

This paper addresses the collision-free consensus problem in a network of agents with single-integrator dynamics. Distributed algorithms with local interactions are proposed to achieve consensus while guaranteeing collision-free among agents during the evolution of the multi-agent networks. The novelty of the proposed algorithms lies in the definition of neighbors for each agent, which is different from the usual sense that neighbors are selected by the distance between agents in the state space. In the proposed strategies, the neighbor set for each agent is determined by the distance or difference between agents in the index space after ordering and labeling all agents according to certain ordering rules including weighted order and lexicographic order. The consensus analysis of the proposed algorithms is presented with some existing results on algebraic graph theory and matrix analysis. Meanwhile, by realizing the relations between order preservation and collision-free, a systematic analysis framework on order preservation and hence collision-free for agents in arbitrary dimension is provided based on tools from monotone systems theory. Illustrated numerical examples are presented to validate the effectiveness of the proposed strategies. [ABSTRACT FROM AUTHOR]

In this paper we propose a fully distributed dual gradient algorithm for minimizing linearly constrained separable convex problems and analyze its rate of convergence. In particular, we prove that under the assumption of strong convexity and Lipschitz continuity of the gradient of the primal objective function we have a global error bound type property for the dual problem. Using this error bound property we devise a fully distributed dual gradient scheme, i.e. a gradient scheme based on a weighted step size, for which we derive global linear rate of convergence for both dual and primal suboptimality and for primal feasibility violation. Numerical simulations are also provided to confirm our theory. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, finite-time position consensus and collision avoidance problems are investigated for multi-AUV (autonomous underwater vehicle) systems. First, based on the homogeneous control method, finite-time position consensus algorithms are proposed for both leaderless and leader–follower multi-AUV systems without considering collisions between the AUVs. Specifically, in the leader–follower case, a novel distributed finite-time observer is developed for the followers to estimate the leader’s velocity. Second, by constructing collision avoidance and connectivity maintenance functions, modified consensus algorithms containing corresponding gradient terms are presented for multi-AUV systems of both cases, which guarantee collision avoidance, connectivity maintenance, velocity matching, and consensus boundedness. Simulations demonstrate the effectiveness of the proposed control algorithms. [Copyright &y& Elsevier]

Abstract: This paper presents a new regularized kernel-based approach for the estimation of the second order moments of stationary stochastic processes. The proposed estimator is defined by a Tikhonov-type variational problem. It contains few unknown parameters which can be estimated by cross validation solving a sequence of problems whose computational complexity scales linearly with the number of noisy moments (derived from the samples of the process). The correlation functions are assumed to be summable and the hypothesis space is a reproducing kernel Hilbert space induced by the recently introduced stable spline kernel. In this way, information on the decay to zero of the functions to be reconstructed is incorporated in the estimation process. An application to the identification of transfer functions in the case of white noise as input is also presented. Numerical simulations show that the proposed method compares favorably with respect to standard nonparametric estimation algorithms that exploit an oracle-type tuning of the parameters. [Copyright &y& Elsevier]

In this paper we deal with decision-coupled problems involving multiple agents over a network. Each agent has its own local objective function and local constraints, and all agents aim at finding the value of a common decision vector that minimizes the sum of all agents' cost functions and satisfies all local constraints. To this purpose, we introduce a Proximal-Tracking distributed optimization algorithm that integrates dynamic average consensus within the proximal minimization method. Convergence to an optimal consensus solution is guaranteed for any value of a constant penalty parameter, under a convexity assumption only, without requiring differentiability, Lipschitz continuity, or smoothness of the local objective functions. Numerical simulations show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper presents a new circumnavigation pattern — safe convex-circumnavigation, for the agent to enclose the targets closely, which is a pattern with better performance in harsh environments than the traditional circular circumnavigation pattern. With this new pattern, the agent circumnavigates the convex hull of multiple stationary targets with an expected enclosing distance and speed using the agent's known position as well as the bearing-only information of the targets, in addition, no collision with this convex hull happens in the whole time domain. An algorithm containing scalar estimators and a control protocol is proposed to implement this pattern. The estimators are designed to locate the targets. In the control protocol, the velocity of the agent is decomposed to one directly pointing to the convex hull and one orthogonal to its current direction. The agent is forced to circumnavigate the convex hull of the targets' estimated positions and finally circumnavigate the convex hull of the real targets because the estimators converge to the exact positions of the targets. The corresponding convergence analysis is provided subsequently. Numerical simulation and real time experiment are conducted to verify the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: The main purpose of this paper is to adapt the so-called Shubert algorithm for extremum seeking control of general dynamic plants. This algorithm is a good representative of the “sampling optimization methods” that achieve global extremum seeking on compact sets in the presence of local extrema. The algorithm applies to Lipschitz mappings; the model of the system is assumed unknown but the knowledge of its Lipschitz constant is assumed. The controller depends on a design parameter, the “waiting time”, and tuning guidelines that relate the design parameter and the region of convergence and accuracy of the algorithm are presented. The analysis shows that semi-global practical convergence (in the initial states) to the global extremum can be achieved in presence of local extrema if compact sets of inputs are considered. Numerical simulations for global optimization in the presence of local extrema are provided to demonstrate the proposed approach. [Copyright &y& Elsevier]

Abstract: This paper considers the consensus problem of double integrator multi-agent systems where: (i) each agent is subject to input saturations, and (ii) the velocity (second state) of each agent is not available for feedback. We present new consensus algorithms that handle simultaneously the above mentioned situations. Sufficient conditions are derived such that consensus algorithms developed for first- and second-order multi-agent systems in ideal situations can be used to account for input saturations and remove the requirement of velocity measurements. To illustrate the effectiveness of the proposed approach, we propose solutions to two different second-order consensus problems in the case where the input is saturated and the velocity states are not available for feedback and simulation results are provided in each case. [Copyright &y& Elsevier]

Abstract: In this paper, we study the distributed containment control problem for networked Lagrangian systems with multiple dynamic leaders in the presence of parametric uncertainties under a directed graph that characterizes the interaction among the leaders and the followers. We propose a distributed adaptive control algorithm combined with distributed sliding-mode estimators. A necessary and sufficient condition on the directed graph is presented such that all followers converge to the dynamic convex hull spanned by the dynamic leaders asymptotically. As a byproduct, we show a necessary and sufficient condition on leaderless consensus for networked Lagrangian systems under a directed graph. Numerical simulation results are given to show the effectiveness of the proposed control algorithms. [Copyright &y& Elsevier]

Abstract: Limiting the rms value ( norm) of periodic signals can be a necessity in some controllers for power electronic devices. The prevailing existing solution is simply to compute, off-line, the norm and then to multiply the signal by the factor that ensures the desired bound is achieved. This feedforward procedure could yield unsatisfactory results during the transients of the system. In this paper, a feedback-based algorithm that carries out the computations on-line is proposed. Besides the on-line computation of the norm, the second component of the algorithm is a classical proportional–integral loop with an anti-windup mechanism. Although the overall algorithm is described by complicated nonlinear, non-autonomous, delay-differential equations, a detailed stability analysis is carried out, proving its robust performance. Simulation results, that confirm the theoretical results, are presented. [Copyright &y& Elsevier]

Abstract: In this paper, we present an automatic C-code generation strategy for real-time nonlinear model predictive control (NMPC), which is designed for applications with kilohertz sample rates. The corresponding code export module has been implemented within the software package ACADO Toolkit. It is capable of exporting fixed step-size integrators together with their sensitivities as well as a real-time Gauss–Newton method. Here, we employ the symbolic representation of optimal control problems in ACADO in order to auto-generate plain C-code which is optimized for final production. The exported code has been tested for model predictive control scenarios comprising constrained nonlinear dynamic systems with four states and a control horizon of ten samples. The numerical simulations show a promising performance of the exported code being able to provide feedback in much less than a millisecond. [Copyright &y& Elsevier]

Abstract: In this paper, a decentralized control algorithm is proposed for a group of nonholonomic vehicles to form a class of collective circular motion behavior. Without the guidance of a global beacon, the desired collective behavior occurs provided that the multi-agent system is jointly connected. Moreover, a repulsion mechanism is considered to improve the distribution evenness of the agents’ circular motion phases and hence to avoid collision. The effectiveness of the approach is verified through both theoretical analysis and numerical simulation. Moreover, some interesting variations of the circular motion model are investigated to enrich the collective behaviors. [Copyright &y& Elsevier]

Abstract: In this paper, a new range identification technique for a calibrated paracatadioptric system mounted on a moving platform is developed to recover the range information and the three-dimensional (3D) Euclidean coordinates of a static object feature. The position of the moving platform is assumed to be measurable. To identify the unknown range, first, a function of the projected pixel coordinates is related to the unknown 3D Euclidean coordinates of an object feature. This function is nonlinearly parameterized (i.e., the unknown parameters appear nonlinearly in the parameterized model). An adaptive estimator based on a min–max algorithm is then designed to estimate the unknown 3D Euclidean coordinates of an object feature relative to a fixed reference frame which facilitates the identification of range. A Lyapunov-type stability analysis is used to show that the developed estimator provides an estimation of the unknown parameters within a desired precision. Numerical simulation results are presented to illustrate the effectiveness of the proposed range estimation technique. [Copyright &y& Elsevier]

Abstract: This paper presents three novel moving-horizon estimation (MHE) methods for discrete-time partitioned linear systems, i.e., systems decomposed into coupled subsystems with non-overlapping states. The MHE approach is used due to its capability of exploiting physical constraints on states and noise in the estimation process. In the proposed algorithms, each subsystem solves reduced-order MHE problems to estimate its own state and different estimators have different computational complexity, accuracy and transmission requirements among subsystems. In all cases, proper tuning of the design parameters, i.e., the penalties on the states at the beginning of the estimation horizon, guarantees convergence of the estimation error to zero. Numerical simulations demonstrate the viability of the approach. [Copyright &y& Elsevier]

Abstract: This paper considers motion control for a cooperative system of automated passenger vehicles. It develops a cooperative scheme based on a decentralized planning algorithm which considers the vehicles in an initial open chain configuration. In this scheme the trajectories are intersections-free, and each trajectory is planned independently of the others. To ensure the stabilization of each vehicle in the planned trajectory, a fuzzy closed loop motion control is presented, where, based on the properties of the Fuzzy maps, the Lyapunov’s stability of the motion errors is demonstrated for all the vehicles. Based on the ISO 2631-1 standard, the saturation property of the Fuzzy maps guarantees low values of longitudinal and lateral accelerations, to ensure comfort of the human body during the motion. The validity of this control algorithm is supported by simulation experiments. [Copyright &y& Elsevier]

This paper focuses on the distributed consensus problem for multiagent systems subject to high-order integrator dynamics and unknown control and output directions based on switching topologies. Unlike existing works, both control and output directions are unknown in the systems and delay is considered between the switching of candidate K-filters and that of topologies. In these conditions, we produced a fresh distributed consensus algorithm by the correlative output information. By designing asynchronous switched K-filters and classical K-filters, the state variables of the leader and all followers are reconstructed. Based on the backstepping method and the Nussbaum-type functions, we put forward the distributed controller with K-filters for the follower. Then, we scrupulously prove that all agents can acquire consensus driven by the controller if the switching signal satisfies an average dwell time condition. Finally, the correctness of the algorithm is tested based on a numerical simulation. [ABSTRACT FROM AUTHOR]

This paper studies an event-triggered distributed optimal coordination problem for heterogeneous linear multi-agent systems with external disturbances. A new distributed optimal coordination algorithm based on event-triggered communication is proposed to deal with heterogeneous agents having a relative degree two. By utilizing the input-to-state stability guaranteed by the proposed algorithm, a novel distributed event-triggering condition is designed. It is showed that the robust distributed optimal coordination problem can be solved practically without Zeno behavior. Moreover, the relationship between the parameters in event-triggering conditions and the system performance is also studied. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

The growing complexity of modern Cyber–Physical Systems (CPS) and the frequent communication between their components make them vulnerable to malicious attacks. As a result, secure state estimation is a critical requirement for the control of these systems. Many existing secure state estimation methods suffer from combinatorial complexity which grows with the number of states and sensors in the system. This complexity can be mitigated using optimization-based methods that relax the original state estimation problem, although at the cost of optimality as these methods often identify attack-free sensors as attacked. In this paper, we propose a new optimal graph-search algorithm to correctly identify malicious attacks and to securely estimate the states even in large-scale CPS modeled as linear time-invariant systems The graph consists of layers, each one containing two nodes capturing a truth assignment of any given sensor, and directed edges connecting adjacent layers only. Then, our algorithm searches the layers of this graph incrementally, favoring directions at higher layers with more attack-free assignments, while actively managing a repository of nodes to be expanded at later iterations. The proposed search bias and the ability to revisit nodes in the repository and self-correct, allow our graph-search algorithm to reach the optimal assignment faster and tackle larger problems. We show that our algorithm is complete and optimal provided that process and measurement noises do not dominate the attack signal. Moreover, we provide numerical simulations that demonstrate the ability of our algorithm to correctly identify attacked sensors and securely reconstruct the state. Our simulations show that our method outperforms existing algorithms both in terms of optimality and execution time. [ABSTRACT FROM AUTHOR]

In this paper, we study the identification of Finite Impulse Response systems in a particular context: data on the input and the output are obtained with one-bit quantizers, the thresholds of quantizers can be different from zero. A three-step identification algorithm is proposed from these binary-valued measurements. This algorithm is based on the normal distribution of the input and noises. The algorithm is appropriately analyzed: it is shown to be asymptotically unbiased, its asymptotic variance is also expressed. Numerical simulations are provided to demonstrate the effectiveness of the proposed algorithm even in presence of noise and to validate the analysis. [ABSTRACT FROM AUTHOR]

In this paper, we propose an inexact Augmented Lagrangian Method (ALM) for the optimization of convex and nonsmooth objective functions subject to linear equality constraints and box constraints where errors are due to fixed-point data. To prevent data overflow we also introduce a projection operation in the multiplier update. We analyze theoretically the proposed algorithm and provide convergence rate results and bounds on the accuracy of the optimal solution. Since iterative methods are often needed to solve the primal subproblem in ALM, we also propose an early stopping criterion that is simple to implement on embedded platforms, can be used for problems that are not strongly convex, and guarantees the precision of the primal update. To the best of our knowledge, this is the first fixed-point ALM that can handle non-smooth problems, data overflow, and can efficiently and systematically utilize iterative solvers in the primal update. Numerical simulation studies on a logistic regression problem are presented that illustrate the proposed method. [ABSTRACT FROM AUTHOR]