This paper considers stability of discrete-time nonlinear systemsin Takagi–Sugeno (T–S) form. This problem has been studied formore than 20 years with many sufficient conditions, and the asymptoticallynecessary and sufficient (ANS) conditions with respect to the commonquadraticLyapunov, function, having being obtained. This paper considersgeneral forms of homogeneous polynomially nonquadratic Lyapunov(HPNQL) function and homogeneous polynomially parameterized nonparalleldistributed compensation (HPP-non-PDC) law. By generalization oftheprocedurebasedonP?olya’s theoremand techniques used for parameterdependentlinearmatrix inequality (PD-LMI) which have been studied previouslyin different contexts, ANS stability conditions with respect to thegeneral HPNQL function are obtained.

This paper considers output feedback robust model predictive control for the quasi-linear parametervarying (quasi-LPV) system with bounded disturbance. The so-called quasi-LPV means that the varyingparameters of the linear system are known at the current time, but unknown in the future. The controllaw is parameterized as a parameter-dependent dynamic output feedback, and the closed-loop stabilityis specified by the notion of quadratic boundedness. An iterative algorithm is proposed for the on-linesynthesis of the control law via convex optimization. A numerical example is given to illustrate theeffectiveness of the controller.

We address the distributed model predictive control (MPC) for a set of linear local systems with decoupled dynamics and acoupled global cost function. By the decomposition of the global cost function, the distributed control problem is convertedto the MPC for each local system associated with a cost involving neighboring system states and inputs. For each localcontroller, the infinite horizon control moves are parameterized as N free control moves followed by a single state feedbacklaw. An interacting compatibility condition is derived, disassembled and incorporated into the design of each local control soas to achieve the stability of the global closed-loop system. Each local system exchanges with its neighbors the current statesand the previous optimal control strategies. The global closed-loop system is shown to be exponentially stable provided thatall the local optimizers are feasible at the initial time. Copyright

This article addresses robust model predictive control (MPC) for systems with polytopicdescription. An existing off-line robust MPC enables the on-line computation as simple assearching a state feedback gain among the look-up table. We consider enlarging the region ofattraction of the off-line MPC by incorporating free perturbation items. First, a largerellipsoidal region of attraction is off-line constructed, in which a simple optimization problemis solved on-line. Then, outside of this larger ellipsoid, a standard robust MPC is solved onlinesuch that the overall region of attraction can be rendered as a much larger nonellipsoidalone. The proposed approach possesses all the merits of the off-line MPC and greatly enlargesthe region of attraction. A simulation example is given to demonstrate the effectiveness.

This paper gives two alternative off-line synthesis approaches to robust model predictive control (RMPC) for systems with polytopic description. In each approach, a sequence of explicit control laws that correspond to a sequence of nested asymptotically invariant ellipsoids are constructed off-line. In order to accommodate a wider class of systems, nominal performance cost is chosen to substitute the "worst-case" one in an existing technique. In the design of control law for a larger ellipsoid, the second approach further incorporates the knowledge of control laws associated with all smaller ellipsoids, so as to further improve feasibility and optimality. The effectiveness of the alternative approaches is illustrated by a simulation example.