Stanislaw H. Zak

Bio: Stanislaw H. Zak is an academic researcher. The author has contributed to research in topics: Linear dynamical system & Evolutionary algorithm. The author has an hindex of 1, co-authored 1 publications receiving 362 citations.

Systems and control

Abstract: 1. Dynamical Systems and Modeling 2. Analysis of Modeling Equations 3. Linear Systems 4. Stability 5. Optimal Control 6. Sliding Modes 7. Vector Field Methods 8. Fuzzy Systems 9. Neural Networks 10. Genetic and Evolutionary Algorithms 11. Chaotic Systems and Fractals Index

Adaptive Neural Network Control of a Self-balancing Two-wheeled Scooter

Abstract: This paper presents an adaptive control using radial-basis-function neural networks (RBFNNs) for a two-wheeled self-balancing scooter. A mechatronic system structure of the scooter driven by two dc motors is briefly described, and its mathematical modeling incorporating two frictions between the wheels and the motion surface is derived. By decomposing the overall system into two subsystems (yaw motion and mobile inverted pendulum), one proposes two adaptive controllers using RBFNN to achieve self-balancing and yaw control. The performance and merit of the proposed adaptive controllers are exemplified by conducting several simulations and experiments on a two-wheeled self-balancing scooter.

Observer design for systems with unknown inputs

Abstract: Design procedures are proposed for two different classes of observers for systems withunknown inputs In thefirst approach, the state of the observed system is decomposed into known and unknown components The unknown component is a projection, not necessarily orthogonal, of the whole state along the subspace in which the available state component resides Then, a dynamical system to estimate the unknown component is constructed Combining the output of the dynamical system, which estimates the unknown state component, with the available state information results in an observer that estimates the whole state It is shown that some previously proposed observer architectures can be obtained using the projection operator approach presented in this paper The second approach combines sliding modes and the second method of Lyapunov resulting in a nonlinear observer The nonlinear component of the sliding mode observer forces the observation error into the sliding mode along a manifold in the observation error space Design algorithms are given for both types of observers

Adaptive Control of a Variable-Speed Variable-Pitch Wind Turbine Using Radial-Basis Function Neural Network

Abstract: In order to be economically competitive, various control systems are used in large scale wind turbines. These systems enable the wind turbine to work efficiently and produce the maximum power output at varying wind speed. In this paper, an adaptive control based on radial-basis-function neural network (NN) is proposed for different operation modes of variable-speed variable-pitch wind turbines including torque control at speeds lower than rated wind speeds, pitch control at higher wind speeds and smooth transition between these two modes The adaptive NN control approximates the nonlinear dynamics of the wind turbine based on input/output measurements and ensures smooth tracking of the optimal tip-speed-ratio at different wind speeds. The robust NN weight updating rules are obtained using Lyapunov stability analysis. The proposed control algorithm is first tested with a simplified mathematical model of a wind turbine, and then the validity of results is verified by simulation studies on a 5 MW wind turbine simulator.

Control of a personal transporter based on user position

Abstract: An apparatus controller for prompting a rider to be positioned on a vehicle in such a manner as to reduce lateral instability due to lateral acceleration of the vehicle. The apparatus has an input for receiving specification from the rider of a desired direction of travel, and indicating means for reflecting to the rider a propitious instantaneous body orientation to enhance stability in the face of lateral acceleration. The indicating may include a handlebar that is pivotable with respect to the vehicle and that is driven in response to vehicle turning.

Optimization model of an irrigation reservoir for water allocation and crop planning under various weather conditions

Abstract: This paper develops a non-linear programming optimization model with an integrated soil water balance, to determine the optimal reservoir release policies, the irrigation allocation to multiple crops and the optimal cropping pattern in irrigated agriculture. Decision variables are the cultivated area and the water allocated to each crop. The objective function of the model maximizes the total farm income, which is based on crop–water production functions, production cost and crop prices. The proposed model is solved using the simulated annealing (SA) global optimization stochastic search algorithm in combination with the stochastic gradient descent algorithm. The rainfall, evapotranspiration and inflow are considered to be stochastic and the model is run for expected values of the above parameters corresponding to different probability of exceedence. By combining various probability levels of rainfall, evapotranspiration and inflow, four weather conditions are distinguished. The model takes into account an irrigation time interval in each growth stage and gives the optimal distribution of area, the water to each crop and the total farm income. The outputs of this model were compared with the results obtained from the model in which the only decision variables are cultivated areas. The model was applied on data from a planned reservoir on the Havrias River in Northern Greece, is sufficiently general and has great potential to be applicable as a decision support tool for cropping patterns of an irrigated area and irrigation scheduling.