Nonlinear output regulation for invertible nonlinear MIMO systems

In this paper, we address the problem of output regulation for a broad class of multi‐input multi‐output (MIMO) nonlinear systems. Specifically, we consider input–affine systems, which are invertible and input–output linearizable. This class includes, as a trivial special case, the class of MIMO systems which possess a well‐defined vector relative degree. It is shown that if a system in this class is strongly minimum phase, in a sense specified in the paper, the problem of output regulation can be solved via partial‐state feedback or via (dynamic) output feedback. The result substantially broadens the class of nonlinear MIMO systems for which the problem in question is known to be possible. Copyright © 2015 John Wiley & Sons, Ltd.     

A novel algorithm for selective current limit used in AMOLED panel

A selective current limit algorithm based on color space conversion is proposed in this paper. There are mainly two advantages for this algorithm. Firstly, only the luminance component Y is decreased, and the color component Cb or Cr is not changed; thus, the color distortion is avoided in the process of current limit. Secondly, the current of pixels with high luminance is limited while that of those pixels with low luminance is not, which means current limit is executed selectively. For the same value of mean square error, the simulation results show that the ratio of pixels with high luminance of the image processed by the proposed algorithm is zero, and that processed by the net power control and the algorithm based on color space conversion is 0.081 and 0.0854, respectively. This algorithm is implemented on a field‐programmable gate array to drive the image display on the AMOLED panel.     

Solving linear generalized Nash equilibrium problems numerically

This paper considers the numerical solution of linear generalized Nash equilibrium problems (LGNEPs). Since many methods for nonlinear problems require the nonsingularity of some second-order derivative, standard convergence conditions are not satisfied in our linear case. We provide new convergence criteria for a potential reduction algorithm (PRA) that allow its application to LGNEPs. Furthermore, we discuss a projected subgradient method (PSM) and a penalty method that exploit some known Nikaido–Isoda function-based constrained and unconstrained optimization reformulations of the LGNEP. Moreover, it is shown that normalized Nash equilibria of an LGNEP can be obtained by solving a single linear program. All proposed algorithms are tested on randomly generated instances of economic market models that are introduced and analysed in this paper and that lead to LGNEPs with shared and with non-shared constraints. It is shown that these problems have some favourable properties that can be exploited to obtain their solutions. With the PRA and in particular with the PSM we are able to compute solutions with satisfying precision even for problems with up 10,000 variables.     

Incremental subgradient method for nonsmooth convex optimization with fixed point constraints

This paper proposes an incremental subgradient method for solving the problem of minimizing the sum of nondifferentiable, convex objective functions over the intersection of fixed point sets of nonexpansive mappings in a real Hilbert space. The proposed algorithm can work in nonsmooth optimization over constraint sets onto which projections cannot be always implemented, whereas the conventional incremental subgradient method can be applied only when a constraint set is simple in the sense that the projection onto it can be easily implemented. We first study its convergence for a constant step size. The analysis indicates that there is a possibility that the algorithm with a small constant step size approximates a solution to the problem. Next, we study its convergence for a diminishing step size and show that there exists a subsequence of the sequence generated by the algorithm which weakly converges to a solution to the problem. Moreover, we show the whole sequence generated by the algorithm with a diminishing step size strongly converges to the solution to the problem under certain assumptions. We also give examples of real applied problems which satisfy the assumptions in the convergence theorems and numerical examples to support the convergence analyses.     

Distributed average computation for multiple time‐varying signals with output measurements

This paper studies the distributed average computation problem for multiple time‐varying signals with bounded inputs. Based only on relative output measurements, a pair of continuous algorithms with, respectively, static and adaptive coupling strengths are designed and utilized. From the concept of boundary layer approach, the proposed continuous algorithm with static coupling strengths can asymptotically obtain the average value of the multiple reference signals without chattering phenomenon. Furthermore, for the case of algorithms with adaptive coupling strengths, the calculation errors are uniformly ultimately bounded and exponentially converge to a small adjustable bounded set. Finally, a simulation example is presented to show the validity of the theoretical results. Copyright © 2015 John Wiley & Sons, Ltd.     

Adaptive Fuzzy PD+ Control for Attitude Maneuver of Rigid Spacecraft

In this paper, an adaptive fuzzy PD+ controller is proposed for the attitude maneuver of rigid spacecraft. The novel controller adjusts the gains of the PD+ attitude controller online according to attitude errors and angular velocity errors during the maneuver procedure. Therefore, quick response and avoidance of actuator saturation can be achieved simultaneously. Furthermore, the adaptation mechanism is designed, based on Lyapunov theory, to guarantee the stability of the closed‐loop system. To achieve good performance of the closed‐loop system under the constraint of actuator saturation, controller parameter optimization is developed on the basis of a genetic algorithm. Simulation results show that the transient performance and robustness against parametric uncertainty and environmental disturbance of the adaptive fuzzy PD+ controller are better than those of a constant PD+ controller.     

Time Averaging Algorithms with Stopping Rules for Multi‐Agent Consensus with Noisy Measurements

A time averaging technique is introduced to consensus algorithms in networked multi‐agent systems under a noisy environment. Each agent communicates with its neighboring agents via a constant gain, while the time averaging states of the agents are considered as the values for agreement. The variation of the time averaging states then is evaluated at a specific number of iterations, where the number is given explicitly in terms of parameters related to the consensus accuracy and its probabilistic guarantee. This result establishes a rigorous stopping rule for the multi‐agent consensus with noisy measurements. Several results on this type of stopping rules are provided for undirected, directed, and time‐varying communication graphs. These theoretical results are illustrated through numerical examples.     

Application of improved firefly algorithm for programmed PWM in multilevel inverter with adjustable DC sources

Programmed pulse width modulation is an optimized pulse width modulation which is particularly applicable for high-power applications where the power losses must be kept below firm limits. Based on the offline estimation, it is capable of pre programming the harmonic profile of the output waveform over a range of modulation indices by eliminating some lower order harmonics. In this paper, improved firefly algorithm (FA) is applied to determine the optimum switching angles for the 11- level cascaded H bridge multilevel inverter (MLI) with adjustable DC sources in order to eliminate pre specified lower order harmonics and to achieve the desired fundamental voltage. Though number of optimization algorithms is available for the estimation of switching angles, Firefly algorithm takes least computation time and surpasses all other 11 metaheuristic Algorithms. The algorithm and the model are developed using MATLAB and the validity of the simulation is confirmed by an experimental setup using FPGA Spartan 6A DSP. Results are compared with the results obtained using particle swarm optimization (PSO) and artificial bee colony algorithm (ABCA) and it is proved that the proposed method offers reduced total harmonic distortion (THD) with less computation period.     

Optimal design of adaptive type-2 neuro-fuzzy systems: A review

Type-2 fuzzy logic systems have extensively been applied to various engineering problems, e.g. identification, prediction, control, pattern recognition, etc. in the past two decades, and the results were promising especially in the presence of significant uncertainties in the system. In the design of type-2 fuzzy logic systems, the early applications were realized in a way that both the antecedent and consequent parameters were chosen by the designer with perhaps some inputs from some experts. Since 2000s, a huge number of papers have been published which are based on the adaptation of the parameters of type-2 fuzzy logic systems using the training data either online or offline. Consequently, the major challenge was to design these systems in an optimal way in terms of their optimal structure and their corresponding optimal parameter update rules. In this review, the state of the art of the three major classes of optimization methods are investigated: derivative-based (computational approaches), derivative-free (heuristic methods) and hybrid methods which are the fusion of both the derivative-free and derivative-based methods.     

一种高效强K—栅栏覆盖构建算法

K－栅栏覆盖是无线传感器网络覆盖控制的研究热点之一。本文构建了强栅栏覆盖模型,提出了分区强K－栅栏覆盖构建算法PMNSB,用最少的节点形成强栅栏。首先把监控区域分成多个子区域,通过匈牙利算法选用移动距离之和最少的网格集合为基准1－栅栏覆盖,缺少移动节点的子区域,选择附近区域的剩余移动节点修补形成1－栅栏覆盖。水平相邻的两个子区域之间构建竖直栅栏,这些1－栅栏合起来构成强K－栅栏覆盖。仿真结果证明了该方法的有效性,本文的研究对提升无线传感器网络的性能具有重要的理论与实际意义。