This paper improves some results in [Antonio Sala, Carlos Ari?o, Asymptotically necessary and sufficient conditions for stabilityand performance in fuzzy control: applications of Polya’s theorem, Fuzzy Sets and Systems 158 (2007) 2671-2686]. In the relaxationof the stability results, only a smaller number of artificial decision variables are required. Thus, we obtain the equivalent stabilityresults, with lower requirement on the computation.

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.

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 further studies stabilization of nonlinear systems represented by a Takagi–Sugeno (T-S) discrete fuzzy model. By extending a nonquadratic Lyapunov function and applying a nonparallel distributed compensation (non-PDC) law, a new stabilization conclusion is presented that is a generalization of some previous results in the literature. The new conclusion under a special situation is also suitable for a PDC law, which presents to be more relaxed than some existing results. An example is provided to demonstrate the effectiveness of the new conclusions.

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