This letter presents robustness bounds (RBs) for receding horizon controls (RHCs) of uncertain systems. The proposed RBs are obtained easily by solving convex problems represented by linear matrix inequalities (LMIs). We show, by numerical examples, that the RHCs can guarantee robust stabilization for a larger class of uncertain systems than conventional linear quadratic regulators (LQRs).

This paper proposes a robust fuzzy integral controller for output regulating a class of affine nonlinear systems subject to a bias reference to the origin. First, a common biased fuzzy model is introduced for a class of continuous/discrete-time affine nonlinear systems, such as dc-dc converters, robotic systems. Then, combining an integrator and parallel distributed compensators, the fuzzy integral regulator achieves an asymptotic regulation. Moreover, when considering disturbances or unstructured certainties, a virtual reference model is presented and provides a robust gain design via LMI techniques. In this case, H∞ performances is guaranteed. Note that the information regarding the operational point and bias terms are not required during the controller implementation. Thus, the controller can be applied to a multi-task regulation. Finally, three numerical simulations show the expected results.

This paper proposes an analysis and design methodology for the robust control of affine-in-control nonlinear systems subject to actuator saturation in discrete-time formulation. The robust stability condition is derived for the closed-loop system by the introduction of the fuzzy Kronecker delta. Based on the newly acquired stability condition, a design method is proposed to guarantee the robust H∞ performance. In the design, LMI-based pole placement is employed to use the freedom allowed in the selection of the controller. The validity of the proposed method is asserted by the computer simulation.

The application of neural networks to the state-feedback guaranteed cost control problem of discrete-time system that has uncertainty in both state and input matrices is investigated. Based on the Linear Matrix Inequality (LMI) design, a class of a state feedback controller is newly established, and sufficient conditions for the existence of guaranteed cost controller are derived. The novel contribution is that the neurocontroller is substituted for the additive gain perturbations. It is newly shown that although the neurocontroller is included in the discrete-time uncertain system, the robust stability for the closed-loop system and the reduction of the cost are attained.

In this letter, we consider a problem of global exponential stabilization of a class of approximately feedback linearized systems. With a newly proposed LMI-condition, we propose a controller design method which is shown to be improved over the existing methods in several aspects.

This study proposes a novel adaptive fuzzy control methodology to remove disadvantages of traditional fuzzy approximation based control. Meanwhile, the highly uncertain robot manipulator is taken as an application with either guaranteed robust tracking performances or asymptotic stability in a global sense. First, the design concept, namely, feedforward fuzzy approximation based control, is introduced for a simple uncertain system. Here the desired commands are utilized as the inputs of the Takagi-Sugeno (T-S) fuzzy system to closely compensate the unknown feedforward term required during steady state. Different to traditional works, the assumption on bounded fuzzy approximation error is not needed, while this scheme allows easier implementation architecture. Next, the concept is extended to controlling manipulators and achieves global robust tracking performances. Note that a linear matrix inequality (LMI) technique is applied and provides an easier gain design. Finally, numerical simulations are carried out on a two-link robot to illustrate the expected performances.

This paper provides a computational method to construct a Lyapunov function to prove a stability of hybrid automata that can have nonlinear vector fields. Algebraic inequalities and equations are formulated, which are solved via LMI optimization. Numerical examples are presented to illustrate the proposed method.

In this paper, we propose a delayed feedback guaranteed cost controller design method for uncertain linear systems with delays in states. Based on the Lyapunov method, an LMI optimization problem is formulated to design a delayed feedback controller which minimizes the upper bound of a given quadratic cost function. Numerical examples show the effectiveness of the proposed method.

In this paper, we present an output feedback controller using a fuzzy controller and observer for nonlinear systems with unknown time-delay. Recently, Cao et al. proposed a stabilization method for the nonlinear time-delay systems using a fuzzy controller when the time-delay is known. In general, however, it is impossible to know or measure this time-varying delay. The proposed method requires only the upper bound of the derivative of the time-delay. We represent the nonlinear system with the unknown time-delay by Takagi-Sugeno (T-S) fuzzy model and design the fuzzy controller and observer for the systems using the parallel distributed compensation (PDC) scheme. In addition, we derive the sufficient condition for the asymptotic stability of the equilibrium point by applying Lyapunov-Krasovskii theorem to the closed-loop system and solve the condition in the formulation of LMI. Finally, computer simulations are included to demonstrate the effectiveness of the suggested method.

This paper provides a new robust guaranteed cost controller design method for discrete parameter uncertain time delay systems. The result shows much tighter bound of guaranteed cost than that of existing paper. In order to get the optimal (minimum) value of guaranteed cost, an optimization problem is given by linear matrix inequality (LMI) technique. Also, the parameter uncertain systems with time delays in both state and control input are considered.

In this paper, we present a dynamic output feedback controller design technique for robust decentralized stabilization of uncertain large-scale systems with time-delay in the subsystem interconnections. Based on Lyapunov second method, a sufficient condition for the stability, is derived in terms of three linear matrix inequalities (LMI). The solutions of the LMIs can be easily obtained using efficient convex optimization techniques. A numerical example is given to illustrate the proposed method.

This paper addresses the L-gain filtering problem for continuous-time linear systems with time-varying structured uncertainties and non-zero initial conditions. We propose a full order linear filter that renders the L-gain from disturbance to filtering error within a prescribed level by solving a linear matrix inequality (LMI) feasibility problem. The filter gain is specified by the solution to a set of LMI's. A numerical example is given to illustrate the proposed method.

The robust induced l-norm control problem is considered for uncertain discrete-time systems. We propose a state feedback and an output feedback controller that quadratically stabilize the systems and satisfy a given constraint on the induced l-norm. Both controllers are constructed by solving a set of scalar-dependent linear matrix inequalities (LMI's), and the gain matrices are characterized by the solution to the LMI's.