A review of convex approaches for control, observation and safety of linear parameter varying and Takagi-Sugeno systems

This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).Peer ReviewedPostprint (published version

Coordination of passive systems under quantized measurements

In this paper we investigate a passivity approach to collective coordination and synchronization problems in the presence of quantized measurements and show that coordination tasks can be achieved in a practical sense for a large class of passive systems.Comment: 40 pages, 1 figure, submitted to journal, second round of revie

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

A review on analysis and synthesis of nonlinear stochastic systems with randomly occurring incomplete information

Copyright q 2012 Hongli Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In the context of systems and control, incomplete information refers to a dynamical system in which knowledge about the system states is limited due to the difficulties in modeling complexity in a quantitative way. The well-known types of incomplete information include parameter uncertainties and norm-bounded nonlinearities. Recently, in response to the development of network technologies, the phenomenon of randomly occurring incomplete information has become more and more prevalent. Such a phenomenon typically appears in a networked environment. Examples include, but are not limited to, randomly occurring uncertainties, randomly occurring nonlinearities, randomly occurring saturation, randomly missing measurements and randomly occurring quantization. Randomly occurring incomplete information, if not properly handled, would seriously deteriorate the performance of a control system. In this paper, we aim to survey some recent advances on the analysis and synthesis problems for nonlinear stochastic systems with randomly occurring incomplete information. The developments of the filtering, control and fault detection problems are systematically reviewed. Latest results on analysis and synthesis of nonlinear stochastic systems are discussed in great detail. In addition, various distributed filtering technologies over sensor networks are highlighted. Finally, some concluding remarks are given and some possible future research directions are pointed out. © 2012 Hongli Dong et al.This work was supported in part by the National Natural Science Foundation of China under Grants 61273156, 61134009, 61273201, 61021002, and 61004067, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Royal Society of the UK, the National Science Foundation of the USA under Grant No. HRD-1137732, and the Alexander von Humboldt Foundation of German

Event-Triggered Sliding Mode control algorithms for a class of uncertain nonlinear systems: Experimental assessment

An experimental assessment of the recently introduced event-triggered sliding mode control approach is presented in this paper. The major design requirement, in this approach, is to reduce the number of transmissions over the network, while guaranteeing that the sliding mode control is stabilizing with appropriate robustness in front of matched uncertainties. In the present paper a novel Event-Triggered Sliding Mode Control algorithm is first introduced and discussed and then it is compared with two different Model-Based Event-Triggered Sliding Mode Control algorithms. Finally, their experimental assessment is reported, obtaining satisfactory performance consistent with the theoretical treatment and fulfilling all the design requirements

MPC for Robot Manipulators with Integral Sliding Modes Generation

This paper deals with the design of a robust hierarchical multiloop control scheme to solve motion control problems for robot manipulators. The key elements of the proposed control approach are the inverse dynamics-based feedback linearized robotic multi-input-multi-output (MIMO) system and the combination of a model predictive control (MPC) module with an integral sliding mode (ISM) controller. The ISM internal control loop has the role to compensate the matched uncertainties due to unmodeled dynamics, which are not rejected by the inverse dynamics approach. The external loop is closed relying on the MPC, which guarantees an optimal evolution of the controlled system while fulfiling state and input constraints. The motivation for using ISM, apart from its property of providing robustness to the scheme with respect to a wide class of uncertainties, is also given by its capability of enforcing sliding modes of the controlled system since the initial time instant, allowing one to solve the MPC optimization problem relying on a set of linearized decoupled single-input-single-output (SISO) systems that are not affected by uncertain terms. The proposal has been verified and validated in simulation, relying on a model of a COMAU Smart3-S2 industrial robot manipulator, identified on the basis of real data