This paper proposes a method for simultaneous estimation of both the driver torque and the sideslip angle within the context of human-machine shared driving control for autonomous ground vehicles. To this end, the driver torque is considered as an unknown input (UI) and the sideslip angle is an unmeasured state of the vehicle dynamics system. For simultaneous estimation purpose, a decoupling-based technique is leveraged to design an unknown input observer (UIO). The UIO design goal is to decouple the effect of the unknown driver torque while minimizing the influence of the modeling uncertainties, considered as unknown exogenous disturbances, from the lateral tires forces and the steering system. Linear parameter-varying (LPV) framework is used to deal with the time-varying nature of the vehicle longitudinal speed. Based on Lyapunov stability theory, we derive sufficient conditions, expressed in terms of linear matrix inequality (LMI) constraints, for LPV unknown input observer design. The simultaneous vehicle estimation is reformulated as a convex optimization problem, where the modeling uncertainty influence can be minimized via the$\ell _{\infty} -$gain performance. Hardware-in-the-loop (HiL) tests are performed with the SHERPA dynamic simulator and a human driver to show the effectiveness of the proposed UIO-based estimation method, especially within the cooperative driving control framework. Note to Practitioners—We present a method to jointly estimate the driver torque and the sideslip angle in the context of human-machine shared driving. To this end, we consider the driver torque as an unknown input and treat the sideslip angle as an unmeasured state of the vehicle dynamics system. The core of our method lies in the application of a decoupling-based technique to design an unknown input observer. The primary objective of this UIO is to effectively decouple the influence of the unknown driver torque while mitigating the impact of modeling uncertainties, considered as unknown exogenous disturbances, on the lateral tire forces and the steering system. Using an LPV framework has allowed the time-varying nature of the vehicle longitudinal velocity to be effectively addressed. Via Lyapunov stability theory, we have established sufficient conditions, expressed in terms of LMI constraints, for the design of the LPV unknown input observer. The proposed simultaneous vehicle estimation method has been reformulated as a convex optimization problem, allowing to minimize the influence of modeling uncertainties. To show the effectiveness of the proposed UIO-based estimation method, we have conducted extensive HiL tests using the SHERPA dynamic simulator with a human driver. The real-time experiments demonstrate the effectiveness of the proposed method, especially with respect to related estimation results in the literature, within the cooperative driving control framework. The proposed LPV estimation method contributes to the advancement of the field of autonomous ground vehicles by providing practitioners with a robust tool for joint estimation of essential variables critical for effective vehicle control and safety in the context of human-machine cooperative driving.

This article addresses the issue of networked lane keeping security control for autonomous vehicles subject to aperiodic controller-targeted denial-of-service (DoS) attacks, taking into account time-varying driving speed and nonlinear tire cornering stiffness. To accurately estimate the incompletely measured states and capture the dynamic behaviors appearing at the end of aperiodic DoS attacks, full state gain adjustable switching observer is established for the fuzzy vehicle-road integrated dynamic systems obtained via the tensor product model transformation method. In order to ensure the control quality and simultaneously save the communication resource, new resilient adaptive event-triggered scheme is proposed with a reinforcement learning-based intelligent optimal threshold regulation mechanism based on observed states. Then, an augmented observer-based fuzzy switching system is constructed using the time delay method. In addition, sufficient conditions are established to guarantee global exponential stability of the closed-loop nonlinear lane keeping system with prescribed H∞ performance using a piecewise Lyapunov functional analysis approach. Subsequently, the gains for controller, observer, and trigger are co-designed and computed by solving certain matrix inequalities. Finally, the effectiveness of the proposed security control method is demonstrated through typical maneuver scenario in terms of reasonable triggered times, better tracking performances and acceptable lateral dynamics.

We investigate the problem of guaranteed state estimation and robust fault detection of uncertain Takagi–Sugeno (TS) fuzzy systems with unmeasured nonlinearities. The effects of both unknown-but-bounded disturbances and faults are taken into account in the state bounding observer design via zonotopic representation of sets, which aims to reduce set operations to simple matrix calculations. Based on the$P\text{-}$radius criterion, the size of the state bounding observers can be minimized via$\mathscr {L}_\infty$performance to mitigate the effect of unknown-but-bounded disturbances. Moreover, for fault detection purposes,$\mathscr {H}_-$performance is taken into account to increase the fault sensibility. The proposed multiobjective$\mathscr {H}_-/\mathscr {L}_\infty$state bounding observer design is reformulated as an optimization problem under linear matrix inequalities (LMIs). As a result, the tradeoff between robustness with respect to uncertainties and fault sensibility can be effectively achieved using standard solvers for LMI constraints. Numerical experiments and suitable comparative studies are performed with a nonlinear autonomous vehicle system to demonstrate the effectiveness and the practical interests of the proposed state bounding TS fuzzy observer design method.

Autonomous lane changing requires balancing safety, comfort, and efficiency while managing complex spatiotemporal vehicle interactions. Current methods often separate risk assessment from trajectory planning, leading to either conservative or unsafe maneuvers. This paper presents a contingency-aware spatiotemporal optimization framework that integrates dynamic risk assessment and trajectory optimization to ensure the autonomous host vehicle (HV) achieve safer, more efficient lane changes. First, the HV uses a dynamic risk field method to assess the collision risk with surrounding vehicles (SVs) in real-time, integrating dynamic obstacle interactions through modified Gaussian distributions. Second, a spatiotemporal safety corridor construction scheme leverages regression-based boundaries to transform spatiotemporal requirements into manageable optimization constraints. Third, the HV adopts a contingency-aware model predictive control framework that incorporates SVs uncertainty for human-like lane changes. The formulated optimization problem is solved using sequential quadratic programming with stability and recursive feasibility. Simulations confirm that our approach ensures safety and comfort of the HV across lane changing scenarios, achieving smoother trajectories, improved stability, and enhanced safety margins, with up to 95% reductions in longitudinal and lateral accelerations and a 27% decrease in lane-changing time.

Accurate and real-time acquisition of vehicular system dynamic states, road surface conditions, and motion states of surrounding participants is crucial for the safety, passenger comfort, and operational efficiency of autonomous vehicles (AVs) and connected automated vehicles (CAVs). In recent years, a significant amount of research has contributed to the field of state estimation for vehicles, roads, and pedestrians. From the systemwide perspective of intelligent transportation systems to a focused view on “vehicle-road-pedestrian”, this survey aims to provide a comprehensive review and summary of recent state estimation techniques for vehicle motion, road surface, and pedestrian motion. A thorough analysis of the reviewed literature, relevant datasets, evaluation metrics, and experimental platforms in this field is also conducted. Finally, existing challenges and future research directions about methods and performance evaluation are further discussed. This survey is expected to contribute to the advancement of research in dynamic state estimation of vehicle-road-pedestrian, thereby facilitating the development of efficient and safe intelligent transportation systems.

This paper proposes a novel sub-polytope integrated approach (sPIA) that features an intrinsic bump-free transition, aiming to reduce conservatism in the design of path tracking control for autonomous vehicles with large range time-varying longitudinal velocity. The approach encapsulates the interdependent time-varying parameters associated with longitudinal velocity as a set of finite-vertex sub-polytopes interconnected via junction points, thereby reducing the conservatism induced by modeling overbounding. The integration of junction points and the formulation of sub-region activation rules provide a theoretical foundation for avoiding abrupt changes in feedback gains, ensuring a bump-free transition between sub-regions. A gain-scheduling state feedback controller is designed, employing parameter-dependent Lyapunov functions to further attenuate design conservatism. The effectiveness of the proposed method in reducing design conservatism is demonstrated by a comparative analysis of the optimal H∞performance indices across various sub-polytope integration schemes. Furthermore, the superiority of the method is exemplified via simulations within real-world driving scenarios, utilizing the high-fidelity CarSim-Simulink platform. The results indicate that the proposed sPIA outperforms traditional polytopic methods in path tracking performance. This improvement, together with the effective avoidance of bumps during sub-regional transitions, confirms the efficacy of the proposed approach. Moreover, the real-time performance of the method is verified by hardware-in-the-loop experiments.

Internet of Things (IoT) devices have been widely deployed to build smart cities. How to efficiently collect data from large-scale IoT devices is a valuable and challenging research topic. Benefiting from agility, flexibility, and deployability, an unmanned aerial vehicle (UAV) has great potential to be an aerial base station. However, given the limited battery capacity, the flight time of a UAV is limited. This article focuses on using multi-UAVs to execute long-distance data collection from large-scale IoT devices. We design a multi-UAVs-assisted large-scale IoT data collection system. The core facilities of this system are the data center and charging stations, which are equipped with a limited number of charging piles to provide charging services for UAVs. To ensure the efficient operation of the system, the problem of deployment and flight planning of UAVs is formulated as a joint optimization problem. To solve the problem, a population-based optimization algorithm with a three-layer structure, namely, EDDE-DPDE, is proposed. It includes two core components: 1) elite-driven differential evolution (EDDE) and 2) differential evolution with a dynamic population (DPDE), which are two variants of differential evolution. Thanks to ideas of reusing elite individuals and historical information, the proposed EDDE-DPDE shows an improvement of at least 11.11% compared with four powerful algorithms in terms of average travel time.

This article focuses on the security tracking control problem of nonlinear autonomous ground vehicles (AGVs) under denial-of-service (DoS) attacks. In order to resist DoS attacks and other external disturbances, a new security control scheme is proposed. First, the Takagi–Sugeno (T-S) fuzzy system is established to represent the nonlinear AGV system under DoS attacks. In particular, in order to improve the flexibility of control and reduce the impact of DoS attacks on tracking control, a novel controller with a specific switching mechanism is designed. The switching mechanism relies on fuzzy membership functions, which can make better use of nonlinear vehicle speed information. Second, the sufficient conditions to ensure global exponential stability of the T-S fuzzy-based switched system are given by using the Lyapunov function method. Finally, the simulation platform built by Carsim and MATLAB/Simulink is used to verify the effectiveness of the proposed control scheme.

Accurate recognition of driver distraction is significant for the design of human-machine cooperation driving systems. Existing studies mainly focus on classifying varied distracted driving behaviors, which depend heavily on the scale and quality of datasets and only detect the discrete distraction categories. Therefore, most data-driven approaches have limited capability of recognizing unseen driving activities and cannot provide a reasonable solution for downstream applications. To address these challenges, this paper develops a vision Transformer-enabled weakly supervised contrastive (W-SupCon) learning framework, in which distracted behaviors are quantified by calculating their distances from the normal driving representation set. The Gaussian mixed model (GMM) is employed for the representation clustering, which centralizes the distribution of the normal driving representation set to better identify distracted behaviors. A novel driver behavior dataset and the other three ones are employed for the evaluation, experimental results demonstrate that our proposed approach has more accurate and robust performance than existing methods in the recognition of unknown driver activities. Furthermore, the rationality of distraction levels for different driving behaviors is evaluated through driver skeleton poses. The constructed dataset and demo videos are available athttps://yanghh.io/Driver-Distraction-Quantification.

We present a reduced-complexity control approach for a class of descriptor nonlinear systems with a nonlinear derivative matrix, possibly singular. To this end, a systematic approach is proposed to obtain anequivalentpolytopic representation of a given nonlinear system within a compact set of the state-space. This modeling approach has two particular features compared to the related Takagi–Sugeno (T–S) fuzzy model-based framework. First, the model complexity only growsproportionally, rather than exponentially, with the number of premise variables. Second, the vertices of the proposed polytopic models can admit aninfinitenumber of representations for the same predefined set of premise variables. Thisnonuniquenessfeature allows introducing some specific slack variables at the modeling step to reduce the control design conservatism. Based on the proposed polytopic representation and Lyapunov stability theory, we derive reduced-complexity admissibility analysis and design conditions, expressed in terms of linear matrix inequalities, for the considered class of descriptor systems. In particular, a new nonlinear control law is proposed for regular descriptor systems to avoid using the extended redundancy form, which may yield numerically complex and conservative results due to the imposed special control structure. Both numerical and physically motivated examples are given to demonstrate the interests of the new control approach with respect to existing T–S fuzzy model-based control results.

We study the stability of a class of discrete-time fuzzy systems with singleton consequents, called Mamdani-like fuzzy systems. The parametric expressions, specific to this class of fuzzy systems, are leveraged to derive stability analysis conditions via Finsler's lemma and Lyapunov stability tools. This allows avoiding the major challenge in dealing with high-dimensional cases, encountered in the related literature when using the classical state-space representation. Moreover, the information of the piecewise region partition can be fully taken into account in the stability analysis with the well-known$S-$procedure to further reduce the stability conservatism. The stability of Mamdani-like fuzzy systems can be checked by solving a set of linear matrix inequalities, that is numerically tractable with a suitable semidefinite programming software. Several numerical and physically motivated examples are provided to illustrate the effectiveness of the proposed stability analysis results.

This paper proposes a multi-input multi-output (MIMO) method for path tracking control of autonomous vehicles under network-induced delays while taking into account the roll dynamics to improve both the driving safety and the passenger comfort. The steering control is directly applied to the front wheels, while the anti-roll moment is exerted by an active suspension. The asynchronous phenomenon caused by the sampling process and the time-varying vehicle speed are explicitly taken into account in the control design using a polytopic linear parameter-varying (LPV) control approach. Moreover, to avoid using costly vehicle sensors and complex control structures, a static output feedback (SOF) control scheme is considered. An effective event-triggering mechanism is also proposed to alleviate the communication burden of the vehicle networked control system. Based on augmented Lyapunov-Krasovskii functional, the control design conditions are derived to guarantee the vehicle closed-loop stability under the effects of transmission delays, event-triggered control signals and time-varying parameters. The design procedure is reformulated as an iterative optimization problem involving linear matrix inequality (LMI) constraints, which can be effectively solved with available numerical solvers. The proposed event-triggered SOF controller is evaluated with the vehicle dynamics simulation software CarSim under several dynamic scenarios. A comparative study with related vehicle control results is performance to emphasize the effectiveness of the control method in terms of path tracking performance, driving safety and comfort, and data communication efficiency of the vehicle networked control system.

The information of the shaft torque and the resistance torque is crucial to develop advanced control and fault diagnosis/detection schemes for electrified powertrain systems. However, reliable physical sensors for torque measurement are not affordable for commercial vehicle applications. This article investigates the simultaneous estimation problem of the state dynamics and the resistance torque for integrated motor-transmission (IMT) systems of electric vehicles. To this end, the IMT system is first reformulated as a nonlinear switched model, where the resistance torque is considered as an unknown input (UI). This modeling reformulation allows taking into account not only the nonlinear nature of IMT dynamics but especially also the intrinsic discontinuity of the gear-shifting process. Then, we propose a nonlinear switched observer (NSO) structure to simultaneously estimate the nonlinear IMT dynamics, thus the shaft torque, and the unknown resistance torque. The observer design does not require any a priori information on the unknown resistance torque as for the classical proportional-integral observer design, nor the well-known matching condition for UI decoupling techniques. Using the Lyapunov stability theory, we derive sufficient conditions, expressed in terms of linear matrix inequality (LMI) constraints, to design an NSO with a guaranteed$\ell _{\infty }$performance to mitigate the negative effect of sensor noises and disturbances. In particular, we propose to incorporate LMI-based bumps limitation conditions in the optimization-based observer design to reduce the impacts of expressive discontinuities at switching instants. Comparative studies are performed between the related estimation methods to show the practical effectiveness of the proposed solution.

The work described in this paper proposes a new dynamic conflict attenuation strategy in driving shared control for intelligent vehicles lane keeping systems (LKS). This strategy takes into account the activity and availability of the driver as well as the external risk and conflict between the driver and the control system in order to manage and adapt the level of assistance in real time. The design of an adaptive shared controller is based on a dynamic multi-objective cost function that changes according to the level of assistance. Based on Lyapunov stability arguments, the global asymptotical stability of the closed-loop control system with the adaptive cost function and the variation in vehicle speed is proven and an LMI optimization is used to formulate the control design. The simulation results, conducted with the SHERPA dynamic car simulator under real-world driving situations, for different scenarios show the importance of adapting the controller in real time in order to decrease the conflict between the driver and the lane keeping system and to ensure the safety of the vehicle as well as to increase the confidence and acceptability of the driver.

This article presents a new approach to design static output-feedback (SOF) controllers for constrained Takagi-Sugeno fuzzy systems with nonlinear consequents. The proposed SOF fuzzy control framework is established via the absolute stability theory with appropriate sector-bounded properties of the local state and input nonlinearities. Moreover, both state and input constraints are explicitly taken into account in the control design using set-invariance arguments. Especially, we include the local sector-bounded nonlinearities of the fuzzy systems in the construction of both the nonlinear controller and the nonquadratic Lyapunov function. Within the considered local control context, the new class of nonquadratic Lyapunov functions provides an effective solution to estimate the closed-loop domain of attraction, which can be nonconvex and even disconnected. The convexification procedure is performed using specific congruence transformations in accordance with the special structures of the proposed SOF controllers and nonquadratic Lyapunov functions. Consequently, the fuzzy SOF control design can be reformulated as an optimization problem under strict linear matrix inequality constraints with a linear search parameter. Compared to existing fuzzy SOF control schemes, the new structures of the control law and the Lyapunov function are more general and offer additional degrees of freedom for the control design. Both theoretical arguments and numerical illustrations are provided to demonstrate the effectiveness of the proposed approach in reducing the design conservatism.

This article develops a unified framework to design fuzzy-model-based observers of general nonlinear systems for both discrete-time and continuous-time cases. This observer problem is known as a challenging task due to the mismatch caused by the unmeasurable premise variables. To deal with this major challenge, we propose to rewrite the nonlinear system as a specific fuzzy model with two types of local nonlinearities: measurable and unmeasurable. Then, a differential mean value theorem for vector-valued functions is applied to local unmeasurable nonlinearities. This allows to represent the estimation error dynamics in a special polytopic form involving measurable membership functions and unknown but bounded time-varying parameters. Using Lyapunov-based arguments, design conditions in terms of linear matrix inequalities are derived to guarantee the asymptotic convergence of the estimation error. Three illustrative examples are given to demonstrate the interests of the new fuzzy observer framework in reducing: 1) the design conservatism and 2) the numerical complexity of the fuzzy observer structure for real-world applications.

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 control method for path tracking of autonomous vehicles. Takagi-Sugeno fuzzy control is used to handle the time-varying vehicle speed and the uncertain tire-road forces involved in the nonlinear vehicle dynamics. To avoid using costly vehicle sensors while keeping a simple control structure, a new fuzzy static output feedback (SOF) scheme is proposed. Moreover, robust set-invariance is exploited to take into account the physical limitations on the steering input and the vehicle state in the control design for safety and comfort improvement. Based on Lyapunov stability arguments, a non-parallel distributed compensation SOF controller is designed for autonomous driving with reduced conservatism. The control design is reformulated as an optimization problem under linear matrix inequalities, easily solved with available numerical solvers. The path tracking performance of the proposed fuzzy controller is evaluated via dynamic driving tests conducted with high-fidelity CarSim/Simulink co-simulations.

In this paper, a novel reference-free shared control system is designed for obstacle avoidance for automated vehicles. Rather than using a reference path to guide the driver, the proposed framework constrains the vehicle's status to guarantee the safety without scarifying the driver's freedom. The constrained Delaunay triangle method is introduced to identify the vehicle's position constraints and the constraints of obstacle avoidance, vehicle stability and physical limitations are investigated and unified. A nonlinear predictive control problem, which is constructed accounting nonlinear vehicle dynamics and given driver actions, is designed to optimize the steering and braking actions needed to keep the vehicle safe. The automation is supposed to correct the driver's steering or braking actions to prevent constraint violation and losing the control of vehicle. The simulation results show that the automation can assist the driver to avoid obstacles and guarantee the vehicle's stability with minimal control intervention.

This paper proposes a novel haptic shared control concept between human driver and autonomous controller for lane keeping in semi-autonomous vehicles. Based on the human-machine interaction during lane keeping, the level of cooperativeness for completion of driving task is identified. Using the identified level of cooperativeness along with the driver workload, the level of assistance required is determined based on an inverse U-shaped relationship. Subsequently based on the level of assistance required, a factor is developed to modulate the assistance torque generated by the autonomous controller. For the generation of the assistance torque, a new ℓ∞linear parameter varying (LPV) control technique is proposed to deal with large variations in the vehicle longitudinal speed and those in the modulation factor. The control architecture works on an integrated driver-in-the-loop model developed by considering vehicle yaw-slip dynamics, steering column, and neuromuscular human driver dynamics. Subsequent closed-loop control performance for dynamic road conditions with varying road curvatures is presented through extensive evaluations.

This paper provides a new solution for path following control of autonomous ground vehicles. H2control problem is considered to attenuate the effect of the road curvature disturbance. To this end, we formulate a standard model from the road-vehicle dynamics, the a priori knowledge on the road curvature, and the path following specifications. This standard model is then represented in a Takagi-Sugeno fuzzy form to deal with the time-varying nature of the vehicle speed. Based on a static output feedback scheme, the proposed method allows avoiding expensive vehicle sensors while keeping the simplest control structure for real-time implementation. The concept of V-stability is exploited using Lyapunov stability arguments to improve the transient behaviors of the closed-loop vehicle system. In particular, the physical upper and lower bounds of the vehicle acceleration are explicitly considered in the design procedure via a parameter-dependent Lyapunov function to reduce drastically the design conservatism. The proposed H2design conditions are expressed in terms of linear matrix inequalities (LMIs) with a single line search parameter. The effectiveness of the new path following control method is clearly demonstrated with both theoretical illustrations and hardware experiments under realworld driving situations.

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 presents a new feedback-feedforward tracking control method for serial manipulators in presence of modeling uncertainties, and both known and unknown disturbances. To improve the tracking performance, the proposed scheme is composed of three main control components: feedforward control, disturbance-observer-based control and feedback control. The feedforward action is designed to account for the effects of the reference signal, considered as known disturbance, on the tracking error dynamics. The disturbance-observer-based control law is proposed to deal with the modeling uncertainties as well as unknown disturbances. Using the concepts of V-stability and H∞ control, the feedback component is designed to satisfy some predefined closed-loop specifications, aiming to improve the tracking performance. The effectiveness of the new tracking control method is demonstrated with a multibody model of a 2-DoF SCARA robot manipulator. In particular, a comparison with two classical tracking control methods is performed to emphasize the interest of our method for robotics control.

This paper is concerned with a new control method which can share the control authority between a human driver and a lane keeping assist system. Based on the concept of multiple controller switching, this shared control method is composed of two levels: operational and tactical. At the operational level, two local optimal-based controllers are designed to satisfy their own predefined control goals. At the tactical level, a supervisor is designed to orchestrate a smooth control authority transition between two local controllers. The closed-loop properties of the human-in-the-loop vehicle system are guaranteed via Lyapunov stability arguments. In particular, the design of both local controllers is recast as a convex optimization problem, easily solved with numerical solvers. The effectiveness of the proposed shared control method is experimentally validated with a human driver and a dynamic driving simulator.

This paper provides a computational method to study the asymptotic stability of piecewise multi-affine (PMA) systems. Such systems stem from a class of fuzzy systems with singleton consequents and can be used to approximate any smooth nonlinear system with arbitrary accuracy. Based on the choice of piecewise Lyapunov functions, stability conditions are expressed as a feasibility test of a convex optimization with linear matrix inequality constraints. The basic idea behind these conditions is to exploit the parametric expressions of PMA systems by means of Finsler's lemma. Numerical examples are given to point out the effectiveness of the proposed method.

This paper addresses a new control design method for Takagi-Sugeno fuzzy systems subject to input nonlinearity, state constraints and also persistent disturbances bounded in amplitude. The proposed method aims at designing simultaneously a dynamic output feedback controller together with its associated anti-windup compensator. The design conditions are derived from Lyapunov stability theorem and expressed in terms of linear matrix inequalities (LMIs). In such a way, the design problem can be solved efficiently with available numerical solvers. The validity of the proposed method is illustrated by means of several numerical examples.

This paper is devoted to the development of a new saturated control law for constrained Takagi-Sugeno fuzzy systems. These systems are subject to both control input and state constraints and also persistent disturbances bounded in amplitude. The design procedure is formulated through linear matrix inequalities (LMIs) form which can be solved by means of convex optimization techniques. Based on the concept of robust invariant set in non-quadratic Lyapunov control framework, the proposed method provides a characterization of the closed-loop domain of attraction. Numerical example is given to demonstrate the interests of the proposed methodology.

This paper is devoted to the development of a shared lateral control strategy for a Driver Steering Assist System (DSAS) that can share the authority with the driver. Up to now, this control issue is still an open research subject in automotive industry due to the complex interactions according to different driving situations between the Human (driver) and the Machine (DSAS). In this work, such interactions are handled by introducing into the vehicle system a fictive time-varying term representing the driver activity. In this way, the actions of the DSAS are computed in function of the driver behaviors (actions and intentions). Using Takagi-Sugeno control technique in the framework of Lyapunov stability theorem, the designed controller is able to handle a large range of variation of vehicle longitudinal speed. Moreover, the proposed controller requires only measured output signals for the design procedure and implementation. The effectiveness of the proposed method is demonstrated with different driving scenarios.

This work deals with the modeling and control of the air system of a turbocharged spark ignited (TCSI) engine. The main purpose is to design a set of nonlinear controllers based on the original physical models of the systems. The proposed strategy is based on the multiple model control approach. Linear Matrix Inequality (LMI) conditions are derived from the Lyapunov's direct method in order to compute the controller for the multiple model subject to bounded noises. The disturbances attenuation is addressed via an H∞constraint. In comparison with existing approaches, the proposed strategy handles more easily the nonlinearities and alleviates significantly the calibration and implementation tasks. Moreover, the feedback gains are readily tuned with few parameters. Finally, this method can be generalized to others more complex turbocharging systems with some adaptations.