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This paper considers the optimal robust control of chaotic systems subject to parameter perturbations and measurement noise. Three novel dynamic tracking controllers are designed herein to suppress the chaotic behaviour in Lorenz systems under practical constraints. The dynamic controllers allow for extra improvements towards optimal and robust tracking but their design is challenging in the chaotic system. Given a single measured state only, the single-state feedback controllers are optimally tuned using high-performance heuristic Particle Swarm Optimization (PSO) algorithm with a constrained multi-objective function focusing mainly on the Integral Absolute Error (IAE). The controlled system stability is ensured through Routh–Hurwitz stability criteria where control parameters are further tuned using a metaheuristic PSO algorithm to ensure faster transients with minimum errors when tracking desired state trajectories and also to ensure robustness to parameter variations and external disturbances in the chaotic system. Different designs are tested through extensive simulations and comparisons where the proposed PSO-tuned PD-tracking controller is proved superior for not only suppressing the chaotic behaviour and reducing the transients but also for their high robustness to parameter uncertainties and external disturbances. • Novel optimal robust controllers are introduced to suppress the chaotic behaviour in Lorenz systems. • The dynamic controllers ensure system stability and fast convergence under practical conditions. • The single-state feedback controllers are optimally tuned using particle swarm optimization. • High robustness to parameter variations and external disturbances in the chaotic system is attained. • The designs are validated through three scenarios and they have shown superior performance. [ABSTRACT FROM AUTHOR]

In many countries, wind power is being developed as a primary source of renewable energy. However, it is difficult to describe and forecast wind speed features, which are stochastic and intermittently complex. Moreover, it is important both to obtain precise forecasts of wind speed for wind power generation and to determine their contribution to regional economies. In this paper, we propose a novel approach for wind speed forecasting. The proposed model is a hybrid that uses the wavelet analysis technique (WAT) for denoising, a negative correlation learning neural network (NCL-NN) ensemble, and an ensemble structure optimized using particle-swarm optimization (PSO). We name the approach WAT-NCL-PSO. We define a novel fitness function to optimize the performance of the NCL-NN ensemble. Then, we rebuild the new NCL-NN ensemble using the contribution rates (CRs) by applying the PSO algorithm. We compiled wind speed datasets from six wind power generator sites in western China and used them to test the performance of the proposed model. Further, we analyzed the model's performance in terms of its robustness and time complexity. Finally, to illustrate the effectiveness of the proposed approach, we compared its performance with that of the back-propagation neural network (BPNN), support vector machine (SVM), bagging, AdaBoost, random forest (RF), long short-term memory (LSTM), seasonal autoregressive integrated moving average (SARIMA), SVM with ensemble empirical mode decomposition (EEMD-SVM), and NCL-NN with wavelet denoising (WAT-NCL) models. The simulation results demonstrate that the performance of the WAT-NCL-PSO model is superior to other methods in terms of forecasting accuracy for short-term wind speeds. [ABSTRACT FROM AUTHOR]

In this paper, optimization of a linear objective function subject to a generalized fuzzy relational inequalities is investigated in which an arbitrary continuous t-norm is considered as fuzzy composition, and fuzzy inequality replaces ordinary inequality in the constraints. Unlike most optimization algorithms, in fuzzy relational inequalities with fuzzy constraints (FRI-FC) we find a near-feasible solution having a better objective value than those resulting from the resolution of the similar problems with crisp (ordinary) inequality constraints. Such solutions are called super-optima in this paper. Subsequently, an algorithm is proposed to find a super-optimum with pre-specified desirable infeasibility. For this purpose, we firstly study some structural properties of the FRI-FC problem and present a new formulization that is independent of t-norms used in the constraints of the problem. This new formulation converts the primary problem into an equivalent problem with simple constraints without considering any penalty parameters. However, it is proved that the transformed equivalent problem enables our algorithm to distinguish unfavorable points and generate a sequence of solutions converging to a super-optimum under a certain sufficient condition. Finally, a modified PSO is presented in which the ability of PSO to solve unconstrained problems with continuous domain, the structure of the transformed problem and some fuzzy structural modifications are combined and form an efficient algorithm to solve the generalized FRI-FC problems. The modified PSO algorithm has been applied to the generalized FRI-FC problem defined with ten well-known continuous t-norms. Additionally, an idea of the FRI-FC problems as outer approximators for FRI problems with ordinary inequalities is also investigated. [ABSTRACT FROM AUTHOR]

In the wireless sensor networks with multiple mobile sinks, the movement of sinks or failure of sensor nodes may lead to the breakage of the existing routes. In most routing protocols, the query packets are broadcasted to repair a broken path from source node to sink, which cause significant communication overhead in terms of both energy and delay. In order to repair broken path with lower communication overhead, we propose an efficient routing recovery protocol with endocrine cooperative particle swarm optimization algorithm (ECPSOA) to establish and optimize the alternative path. In the ECPSOA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is enriched by the endocrine mechanism, which can enhance the capacity of global search and improve the speed of convergence and accuracy of the algorithm. By using this method, the alternative path from source nodes to the sink with the optimal QoS parameters can be selected. Simulation results show that our routing protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption. [ABSTRACT FROM AUTHOR]