Distributed Control in Multiple Dimensions: A Structure Preserving Computational Technique

Substantial simplification of the H/sub infinity / controller synthesis procedure in the case of open-loop stable plant and weight selection is shown to result from the use of a classical cascade controller feedback structure. In this case the solution to the second associated algebraic Riccati equation reduces identically to a null matrix 0/sub n/, and this part of the controller computation can be dispensed with. The system structure allows effective tradeoff between significant closed-loop transfer functions.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this work, we consider a simplified version of the distributed control synthesis problem where the system is modeled as a Boolean network and specifications are given as propositional formulae on environment and system variables. In this relatively static Boolean setting, (centralized) reactive control synthesis problem reduces to a quantified Boolean satisfiability problem which can be solved by off-the-shelf SAT solvers. For the distributed control synthesis, we propose a recursive algorithm that starts from a global assume-guarantee specification and maps it to local assume-guarantee specifications for individual subsystems. Finding these local assume- guarantee specifications leads to a modular design framework. For instance, local controllers for subsystems can be synthesized independently via SAT or a local subsystem can be replaced with another subsystem that satisfies the same local assume-guarantee specifications. We also investigate how the interconnection structure of the network affects the performance of this algorithm. In particular, we show that if the interconnection structure is a multi-tree and the global assumptions and guarantees satisfy certain independence conditions, the algorithm is complete.

Symbolic control design for monotone systems with directed specifications

Reducing traffic accidents and associated casualties is a growing concern for modern human society. The secondary or even chain collisions for an unstable vehicle after an initial impact can result in more hazards and fatalities. Passive safety systems such as airbags and seat belts only provide limited level of protection for vehicle occupants, but cannot prevent collision accidents, while active safety systems usually work before the initial collision. Therefore, it is of great significance to develop dedicated post-impact stability control systems to help vehicles quickly restore stability to mitigate and/or avoid secondary collisions. However, the loss of original nonholonomic constraint property and the nonlinearity and saturation of tire forces due to post-impact sideslip, over-spinning, and drifting motions pose great challenges in controller design. Moreover, how to simulate and analyze the collision process and to further construct a simulation environment is the primary problem to solve for enabling controller development. Also, exploring repeatable, effective and low-cost experiment methods lays the foundation for controller verification. This paper aims to provide an overview of the latest technological advancements in collision modeling, control synthesis, and experimental procedures for post-impact stability control. The advantages and disadvantages of different modeling, control and experimental approaches are compared in succession. Finally, the paper discusses the challenges encountered in existing research and the prospects for post-impact active safety control systems.

Self-Recovery of Spaceborne Digital Circuits Using Corrective Control

Discrete-Time LPV Controller Synthesis Using Dilated LMIs with Application to an Arm-Driven Inverted Pendulum

A numerical procedure is presented for efficient solution of linearly coupled sets of Lyapunov equations. Such equations arise in the iterative solution of (nonlinearly) coupled sets of modified Lyapunov and Riccati equations in the design of robust and/or low order control systems.

The minimal controller synthesis (MCS) algorithm is an extension of the class of model reference adaptive control (MRAC) algorithms that requires neither plant model identification nor linear controller synthesis. Various theoretical and experimental studies have shown it to possess the stability and robustness features essential to any successful adaptive control scheme. However, due to its high responsiveness to plant parameter variations and external disturbances, it can occasionally suffer from the long-term effects of measurement errors resulting in gain wind-up, closed-loop signal aliasing and hence instability. To counteract this effect, we propose an algorithm that extends MCS in such a way as to confine its adaptive gain evolutions by using automatically adjusting bounding corridors. The gain corridors MCS (GCMCS) algorithm that results does not suffer from error-boundedness or gain decay problems. Its name describes the controller's graphic functionality. In this paper, we extend the normal Popov criterion based stability proofs of MCS by including the consideration of plants with measurement or state errors, which had hitherto not been included. By working through this proof for single input single output (SISO) phase-variable plant structures, we show that the new algorithm retains the asymptotic properties of basic MCS and satisfies Popov's inequality under the assumed conditions of slower plant parameter variations relative to the controller bandwidth.

The paper is concerned with Kalman filtering for wireless sensor network (WSN) with multiple multiplicative noises. Two measurements which are received through two different channels are considered in this system, and some packets are lost independently. Kalman filter for the above system is presented by solving a Lyapunov equation and a Riccati equation. Moreover, the optimal filter for the case of the system with multiple multiplicative noises is also presented. Furthermore, some results for the Lyapunov equation and the Riccati equation are proposed to analyze the performances of the Kalman filters.

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.

The complete tracking control for a permanent magnet synchronous (PMAC) motor, based on two nonlinear design methods is presented in the paper. The motor model, given in the rotor's d-q coordinates, is nonlinear with respect to the state variables and linear in the control. First design method is based on Lyapunov's stability theorem and the second one on input-output linearization approach. In both cases the derived control law implies similar nonlinear feedback structure. The input-output linearization makes it possible to write the motor's model in Brunovsky's decoupled canonical form which allows the synthesis of linear controllers. The proposed control laws allow perfect tracking of smooth references in the case of nominal parameters. In relation to the bounded parameter perturbations the robustness of the input-output linearized feedback system is improved by introducing an additional compensation signal which assures the stability of the perturbed system in Lyapunov's sense. The experimental results of the proposed PMAC servo drive control are presented. >

The paper investigates the input—output energy balances in linear dynamical systems subjected to unmodelled dynamics for which an a priori knowledge is needed in terms of frequency and time-domain specifications. A discussion about the synthesis of linear controllers in the linear and time-invariant case is also given. The controllers have robustness properties, in the sense that the system subjected to unmodelled dynamics has a close behaviour, in an input-output energy sense, to the nominal (perfectly modelled) case.

Homotopy algorithms for both full- and reduced-order LQG controller design problems with an H∞ constraint on disturbance attenuation are developed. The H∞ constraint is enforced by replacing the covariance Lyapunov equation by a Riccati equation whose solution gives an upper bound on H2 performance. The numerical algorithm, based on homotopy theory, solves the necessary conditions for a minimum of the upper bound on H2 performance. The algorithms are based on two minimal parameter formulations: Ly, Bryson and Cannon's 2 × 2 block parametrization and the input normal Riccati form parametrization. An over-parametrization formulation is also proposed. Numerical experiments suggest that the combination of a globally convergent homotopy method and a minimal parameter formulation applied to the upper bound minimization gives excellent results for mixed-norm H2/H∞ synthesis. The non-monotonicity of homotopy zero curves is demonstrated, proving that algorithms more sophisticated than standard continuation are necessary.

A procedure for industrial robot control synthesis based on asymptotic regulator properties is presented. It involves computer linearization of the dynamic manipulator model along a given nominal trajectory and an efficient algorithm for synthesis of a robust linear regulator ensuring the decoupled control of the system.

A general procedure of approximate optimal control synthesis for the socio-ecological-economic model of a region is developed. Program system DSEEmodel 1.0 is created, which involves a cluster computing device to implement parallel algorithms of scenario calculations, optimization and improvement of an approximate optimal control for the socio-ecological-economic model of a region. The program system serves for conducting multi-scenario calculations to design a sustainable development strategy for a region. In general, this is a new approach to the problem of situational control of a region, which employs supercomputers to implement the full-scale socio-ecological-economic model.