Parametric Stability Analysis of Pneumatic Valves Using Convex Optimization

Frequency-based multivariable control design with stability margin constraints: A linear matrix inequality approach

Effective design collaboration during the early design stages in architecture is a condition for effective overall design and construction. This can be facilitated, supported and promoted through visual technological means. Collaborative design protocols implemented within the engineering fields (electrical and mechanical) can be adapted to the built environment design with emerging augmented reality technologies being the enablers for that shift. Architectural design requires a strongly visual approach to communication and means through which multiple disciples can collaborate effectively. Therefore, the aim of the research is to investigate principles and protocols to assist early design stage collaborative design among multidisciplinary teams, through visual and tactile user interfaces. This paper explores the current paradigm in AEC (architecture, engineering and construction), design collaboration and multidisciplinary group dynamics during the early design and conceptual stages in architecture. Afterwards, a design protocol is suggested, literature review regarding collaborative design and design protocols is summarized and further research is proposed on optimization and standardization of early design collaborative design in AEC.

Predictive quantization is a simple and effective method for encoding slowly-varying signals that is widely used in speech and audio coding. It has been known qualitatively that leaving correlation in the encoded samples can lead to improved estimation at the decoder when encoded samples are subject to erasure. However, performance estimation in this case has required Monte Carlo simulation. Provided here is a novel method for efficiently computing the mean-squared error performance of a predictive quantization system with erasures via a convex optimization with linear matrix inequality constraints. The method is based on jump linear system modeling and applies to any autoregressive moving average (ARMA) signal source and any erasure channel described by an aperiodic and irreducible Markov chain. In addition to this quantification for a given encoder filter, a method is presented to design the encoder filter to minimize the reconstruction error. Optimization of the encoder filter is a nonconvex problem, but we are able to parameterize with a single scalar a set of encoder filters that yield low MSE. The design method reduces the prediction gain in the filter, leaving the redundancy in the signal for robustness. This illuminates the basic tradeoff between compression and robustness.

Fixed-Order H∞ Control Design via a Partially Augmented Lagrangian Method

This study estimated availability of an air compression system and a nitrogen generation system in liquefied natural gas—floating production storage and offloading unit (LNG-FPSO) with different design stages to investigate the gap between the availability at the early design stage and that at the late design stage. Although availability estimation in the early design stage is more important than the late design stage, it is difficult to estimate the availability accurately in the early design stage. The design stage was divided into three depending on the design progress. Monte Carlo simulation technique was employed for the availability estimation. The results of the availability estimation showed that there was 0.434% difference between the early and late design stages. This meant that the availability in the early design stage was underestimated due to limited information. A sensitivity analysis was performed to investigate critical factors affecting the results. The investigated factors were failure rate, repair time, redundant equipment, and modified preventive maintenance schedule. The most critical factor was redundant equipment. It increased 0.486% availability.

This paper proposes a new approach to the localization of a perspective camera with respect to a known target. The problem is stated as the dual - in the automatic control sense - of visual servoing: while visual servoing would move a camera from visual data so as to stabilize its relative pose with respect to a target, the aim of localization is to estimate/forecast this relative situation from the knowledge of visual data and of the camera command input. The proposed methodology entails a discrete-time rational state space model of interactions. This model is embedded in a structured norm-bounded linear recursive inclusion, so that the forecast of the relative situation at the next sampling time is computed by robust filtering techniques. The solution is obtained via an optimization problem under linear matrix inequalities constraints. Despite its difficulty of implementation, the proposed set-membership method seems quite promising, for it can handle parametric uncertainties as well as dynamic and measurement noises, and encloses the relative situations compatible with the visual data.

Linear parameter-varying gain-scheduled preview-based robust attitude control design for a staring-mode satellite

ABSTRACTIn this work, synthesis of robust distributed model predictive control (MPC) is presented for a class of linear systems subject to structured time-varying uncertainties. By decomposing a global system into smaller dimensional subsystems, a set of distributed MPC controllers, instead of a centralised controller, are designed. To ensure the robust stability of the closed-loop system with respect to model uncertainties, distributed state feedback laws are obtained by solving a min–max optimisation problem. The design of robust distributed MPC is then transformed into solving a minimisation optimisation problem with linear matrix inequality constraints. An iterative online algorithm with adjustable maximum iteration is proposed to coordinate the distributed controllers to achieve a global performance. The simulation results show the effectiveness of the proposed robust distributed MPC algorithm.

Semidefinite programs derived from the Kalman-Yakubovich-Popov (KYP) lemma are quite common in control and signal processing applications. The programs are often of high dimension which makes them hard or impossible to solve with general-purpose solvers. Here we present a customized preprocessor, KYPD, that utilizes the inherent structure of this particular optimization problem. The key to an efficient implementation is to transform the optimization problem into an equivalent semidefinite program. This equivalent problem has much fewer variables and the matrices in the linear matrix inequality constraints are of low rank. KYPD can use any primal-dual solver for semidefinite programs as an underlying solver.

ABSTRACTThis paper introduces an alternating direction method of multipliers (ADMM) for finding solutions to a class of Sylvester matrix equation AXB = E subject to a linear matrix inequality constraint CXD≥G. Preliminary convergence properties of ADMM are presented. Numerical experiments are performed to illustrate the feasibility and effectiveness of ADMM. In addition, some numerical comparisons with a recent algorithm are also given.

An optimal experiment design for system identification is studied. The main contribution is the development of an adaptive method for the direct design of FIR filters for the input spectrum design problem. The accuracy of the identified model is measured in terms of the closed-loop performance of the system using the controller designed from the model. Under the assumption that the identified parameters are sufficiently close to their true values, we show that this problem may be formulated as a convex optimization problem with linear matrix inequality constraints. Thus, a global solution (if feasible) is guaranteed and the solution may further achieve any demanded accuracy. The problem formulation is particularly suited for a practical implementation, thus the extension of the experiment design problem into an iterative/adaptive identification-experiment design framework is straight forward. The adaptive approach is further studied in a simulation example, where the rapid convergence of the method is noted, and the superior result compared to an arbitrary experiment design is clear. The example support the use of the approximations taken in the theoretical approach.

In this paper, the problem of L2-gain analysis and anti-windup fault-tolerant control is investigated by using the multiple Lyapunov functionals method for a class of saturated switched delayed systems with actuator faults. A sufficient condition for the state to be bounded is given for the closed-loop system and under this condition the disturbance tolerance problem is transformed into a convex optimization problem with linear matrix inequality constraints. Then, the L2-gain analysis of the closed-loop system is carried out. Next, by designing the anti-windup (AW) fault-tolerant controller, the maximum allowable disturbance capability as well as the minimum restricted L2-gain upper bound is obtained. Finally, the numerical examples are given to verified the effectiveness of the proposed method by comparing the state response under the action of the AW fault-tolerant controllers and the standard controllers.

An important problem in systems biology consists of establishing whether an equilibrium point of a genetic regulatory network (GRN) is stable or not. This article investigates this problem for GRNs with SUM or PROD regulatory functions. It is shown that sufficient conditions for global asymptotical stability of an equilibrium point of these networks can be derived in terms of convex optimisations with linear matrix inequality constraints. These conditions are obtained by looking for a Lyapunov function through the use of suitable polynomial relaxations, and do not introduce approximations of the nonlinearities present in the GRNs. The benefit of these conditions is that their conservatism can be decreased by increasing the degree of the introduced polynomial relaxations. Numerical examples illustrate the usefulness of the proposed conditions.

In this paper, we study H∞ control for systems with explicit mechanical constraints and a lack of state information, such as walking robots. This paper proposes an H∞ control design scheme based on solving an optimization problem with linear matrix inequality constraints. Our method is based on the orthogonal decomposition of the state variables and the use of two linear controllers and a Luenberger observer, tuned to achieve the desired properties of the closed-loop system. The method takes into account static linear additive disturbance, which appears due to the uncertainties associated with the mechanical constraints. We propose a dynamics linearization procedure for systems with mechanical constraints, taking into account the inevitable lack of information about the environment; this procedure allows a nonlinear system to be transformed into a form suitable for the application of the proposed control design method. The method is tested on a constrained underactuated three-link robot and a flat quadruped robot, showing the desired behavior in both cases.

It is known that actuator saturation can lead to deterioration in the performance of the control system and may even lead to instability. Theinvariant set and L2-gain problemsof the linear systemssubjectto actuator saturation areconsidered.Anantiwinduptechniqueisusedtoenlargetheinvariantsetwitha guaranteedprespecie ed L2 gain. The design of the antiwindup compensation gain is formulated and solved as an iterative optimization problem with linear matrix inequality constraints. Numerical examples are used to demonstrate the effectiveness of the proposed design technique.