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International audience; This paper is concerned with a new control method for path tracking of autonomous ground vehicles. We exploit the fuzzy model-based control framework to deal with the time-varying feature of the vehicle speed and the highly uncertain behaviors of the tire-road forces involved in the nonlinear vehicle dynamics. To avoid using costly vehicle sensors for feedback control while favoring the simplest control structure for real-time implementation, a new fuzzy static output feedback (SOF) scheme is proposed. In particular, though the robust set-invariance property and Lyapunov-based arguments, the physical constraints on the steering input saturation and the vehicle state can be taken into account in the control design to improve the driving safety and comfort. The theoretical development relies on the use of fuzzy Lyapunov functions and the non-parallel distributed compensation control concept to reduce the design conservatism. Exploiting some specific convexification techniques, the control design is reformulated as an optimization problem under linear matrix inequalities with a single line search, which are efficiently solved via semidefinite programming techniques. The proposed fuzzy path tracking controller is evaluated through various dynamic driving tests conducted with high-fidelity CarSim/Matlab co-simulations. Moreover, to emphasize the advantages of the new fuzzy SOF controller, a performance comparison with the CarSim driver model is also performed.

This paper presents a new method to design non-parallel distributed compensation (non-PDC) laws for Takagi- Sugeno fuzzy systems with local nonlinear models subject to actuator saturation. Based on specific congruence transformations, the local stabilization conditions are derived using Lyapunov stability theorem. The local design framework is established through an effective treatment of the sector-bound conditions for the input-saturation phenomenon and the nonlinearities in the consequents of the fuzzy systems. Using a non-quadratic Lyapunov function candidate, the control design is reformulated as an LMI-based optimization problem with a line search over a parameter, which can be effectively solved with convex optimization techniques. In particular, we theoretically prove that the new control method is less conservative compared to that derived from a standard non-PDC controller. Illustrative examples are provided to point out the interests of the proposed control method.

This paper is dedicated to the control of the air-fuel ratio in a gasoline engine. The use of the crank-angle domain handles the variable transport delay, which is the main challenge when controlling air-fuel ratio. The Takagi-Sugeno methodology is considered to deal with the nonlinearities in the model. The control law is obtained using an extended state that includes the delayed variables. Simulations confirm the feasibility of the method and highlight the efficiency of the proposed air-fuel ratio controller.

A verification methodology is described and evaluated to formally determine uncertain linear systems stability in digital controllers with considerations to the implementation aspects. In particular, this methodology is combined with the digital-system verifier (DSVerifier), which is a verification tool that employs Bounded Model Checking based on Satisfiability Modulo Theories to check the stability of digital control systems with uncertainty. DSVerifier determines the control system stability, considering all the plant interval variation set, together with the Finite Word-length (FWL) effects in the digital controller implementation; DSVerifier checks the robust non-fragile stability of a given closed-loop system. The proposed methodology and respective tool are evaluated considering non-fragile control examples from literature. Experimental results show that the approach used in this study is able to foresee fragility problems in robust controllers, which could be overlooked by other existing approaches due to underestimating of FWL effects.

Modeling of time-varying dynamic systems in real time requires the use of streams of sensor data and incremental learning algorithms. This paper introduces an incremental fuzzy modeling approach based on uncertain data streams. By uncertain data we mean data originated from unreliable sensors, imprecise perception, or description of the value of a variable represented as a fuzzy interval. An online incremental learning algorithm is used to develop the antecedent part of functional fuzzy rules and the rule base that assembles the model. A recursive least squares-like algorithm updates the parameters of a discrete state-space representation of the fuzzy rule consequents. Data uncertainty is accounted for using specificity measures of the input data. An illustrative example concerning the Lorenz attractor is given.

The control of multivariable industrial processes is frequently performed using a multiloop configuration, in which several PID controllers are committed to control different channels of the plant. A difficulty with such a strategy arises due to the interaction among the control loops, which may cause the control action in a loop to give rise to significant disturbances in other loops. In some cases, it is mandatory to consider a decoupling control that includes a decoupling precompensator, or decoupler, to guarantee acceptable decoupling among the control loops. This paper presents a new robust decoupling control synthesis procedure for multiloop control systems which aims to decouple the different channels of the multivariable system and to guarantee the tracking response performance. The control problem is stated as a non-convex optimization problem which is formulated directly in the space of the PI/PID controllers and precompensator parameters. Polytopic models represent the system uncertainty. An application example is developed for the control of a quadruple-tank process with emphasis in dealing with the control decoupling when the system is working on a non-minimum phase operating point.

Very often, the control of multivariable industrial processes is performed using multi-loop architectures, in which several SISO PI or PID controllers are committed to control different channels of the plant. A difficulty with such a strategy arises due to the interaction between the different control loops, which may cause the control action in a loop to give rise to significant disturbances in other loops. This paper presents a new robust PI tuning procedure for multi-loop control systems which aims to decouple the different channels of the multivariable system and to guarantee the tracking response performance and measurement noise attenuation, using a reference model approximation scheme. The control problem is formulated as a non-convex multi-objective optimization problem which is formulated directly in the space of PI controller parameters. Polytopic models represent the system uncertainty. An application example is developed for the control of a quadruple-tank process.

Many Takagi-Sugeno (TS) synthesis and analysis conditions can be expressed as positive/negative definiteness conditions of fuzzy summations. In this regard, several sufficient conditions have been proposed in the literature that are asymptotically exact (i.e. necessary and sufficient as their number tends to infinity). However, since these conditions do not take the membership function shapes into account (aside from the fact that they add up to one and are greater than zero), the exactness of these conditions is only true when the membership functions can assume any possible combination of values inside the standard simplex. By making use of the membership functions shapes, in this paper we propose a triangulation (or simplicial partition) based approach that generates asymptotically exact conditions for fuzzy summations with a possibly smaller growing rate of added simplices per iteration than that observed in recently published algorithms. The set of conditions are Linear Matrix Inequalities (LMIs), for which efficient numerical routines are available.

This paper presents a new strategy for robust dynamic output-feedback control synthesis for uncertain continuous-time linear time-invariant systems represented by polytopicmodels. It is considered a multiobjective optimization problem to guarantee tracking response specifications, disturbance rejection, and noise attenuation. The controller design criterion is to minimize the ℌ ∞ -norm of the error between a reference model and the closed-loop transfer function to attain the tracking response specifications. The proposed control synthesis methodology is based on a two step iterative procedure: synthesis, based on a non-linear optimization algorithm, and analysis, based on a branch-and-bound algorithm combined with LMI analysis formulations. A numeric example is presented to illustrate the effectiveness of the proposed approach.

In this paper the incipient fault detection problem in induction machine stator-winding is considered. The problem is solved using a new technique of change point detection in time series, based on a three-step formulation. The technique can detect up to two change points in the time series. The first step consists of a Kohonen neural network classification algorithm that defines the model to be used, one change point or two change points. The second step consists of a fuzzy clustering to transform the initial data, with arbitrary distribution, into a new one that can be approximated by a beta distribution. The fuzzy cluster centers are determined by using the Kohonen neural network classification algorithm used in the first step. The last step consists in using the Metropolis-Hastings algorithm for performing the change point detection in the transformed time series generated by the second step with that known distribution. The incipient faults are detected as long as they characterize change points in such transformed time series. The main contribution of the proposed approach in this paper, related to previous one in the Literature, is to detect up to two change points in the time series considered, besides the enhanced resilience of the new fault detection procedure against false alarms, combined with a good sensitivity that allows the detection of rather small fault signals. Simulation results are presented to illustrate the proposed methodology.

In this paper, we show how stability of an infinite-dimensional linear time-delay system can be assessed by studying the stability of an associated finite-dimensional linear system, constructed after substituting the exponential function in the characteristic equation of the delay-system by a high enough finite power of the bilinear transformation.

This paper presents a procedure for robust PID tuning based on a non-convex optimization problem with H2 and/or H∞ performance criteria and regional pole placement. The uncertain system can be represented by polytopic or affine parameter-dependent models. The efficiency of the proposed tuning procedure is illustrated by two examples.

This paper presents a less conservative approach to compute, with any prescribed accuracy, the H ∞ guaranteed cost of time-delay continuous-time linear time-invariant systems subjected to polytopic uncertainties. The proposed analysis approach is based on a branch-and-bound algorithm that incorporates a recent LMI-based analysis formulation and a new polytope partition strategy. In the branch-and-bound algorithm, the upper bound function is defined as the worst case guaranteed H ∞ disturbance attention level computed for the subpolytopes achieved with successive partitions of the polytope which describes the uncertainty domain. The lower bound function is defined as the worst case H ∞ norm computed in the polytope and subpolytope vertices. The difference between the upper and lower bound functions converges to zero as the initial polytope is split into smaller subpolytopes resulting in the H ∞ guaranteed cost for the whole initial polytope with the required accuracy. It is also presented an algorithm to implement a d−dimensional simplex subdivision technique to be used in the branch-and-bound algorithm.

This paper develops a Linear Matrix Inequality (LMI) approach to the robust /spl Hscr//sub /spl infin// control problem of uncertain continuous-and discrete-time linear time-invariant systems with time-delay in the state vector and control input. The main results provide sufficient delay-dependent conditions for the control problem, where the explicit size of the time delay plays a crucial role for the closed-loop stability. The solutions that are found for the /spl Hscr/;/sub /spl infin// control problem are less conservative, when compared to other approaches.

This paper presents an algorithm for robust/spl Hscr//sub 2///spl Hscr//sub /spl infin// state-feedback control synthesis, with regional pole placement, based on a multiobjective optimization algorithm with non-smooth problem-solving capability. The problem is formulated with the state-feedback matrix coefficients as optimization parameters. The closed-loop performance obtained by means of the proposed strategy is assessed for the whole uncertainty-set through an LMI-based /spl Hscr//sub 2/ and /spl Hscr//sub /spl infin// guaranteed cost computation. The proposed strategy is compared with three former LMI approaches, for systems with polytope-bounded uncertainties, and presents better results.

The mixed /spl Hscr//sub 2///spl Hscr//sub /spl infin// control problem does not have an exact solution, despite much research. This paper develops a methodology for multiobjective problem solution characterization, employing group properties of the Pareto-set. A multiobjective genetic algorithm is built on the basis of these properties. The solutions that are found for the /spl Hscr//sub 2///spl Hscr//sub /spl infin// control problem are both consistent and less conservative, when compared to other algorithms.

The problem of robust H/sub /spl infin// filtering for discrete-time uncertain linear systems with multiple state delays is addressed. The uncertain parameters are supposed to belong to a given convex bounded polyhedral domain. Sufficient conditions for the existence of a stable linear filter assuring stability and a prescribed H/sub /spl infin// performance for the filtering error system for all allowed uncertainties are established in terms of linear matrix inequalities. Efficient numerical algorithms can be used to solve the problem, as illustrated via an example.

This paper proposes alternative LMIs characterization for the /spl Hscr//sub 2/ and the central /spl Hscr//sub /spl infin// state feedback controllers for discrete-time systems, allowing the immediate extension to handle uncertain systems belonging to convex polyhedral domains.

This paper addresses the design of robust non-fragile state feedback controllers, with controller uncertain parameters described as polytope-type convex-bounded uncertainties. The linearizing change of variables Z=KW, that is the basis for a large class of LMI-based robust control design algorithms, is slightly modified. The modified change of variables proposed here leads to LMI-based robust non-fragile control design algorithms that are directly derived from any former algorithm formulated in terms of the traditional change of variables. In this way, the "fragility" issue is solved inside the mainstream of the LMI-based robust control synthesis algorithms that have been developed up to now, in a unified basis.

The problem of robust H ∞ state-feedback control design for a class of continuous-time linear systems with Markovian jumping parameters and multiple time-varying delays at the states, and subject to uncertain parameters is addressed in this paper. It is assumed that the jumping parameters are available and the uncertain parameter belong to given convex bounded domains (polytope type uncertainty). A delay-independent sufficient condition assuring robust mean square stability and a prescribed level of H ∞ disturbance attenuation for the closed-loop system is established in terms of linear matrix inequalities.