Flood forecasting and flood flow modeling in a river system using ANN

In terms of predicting the flow parameters of a river system, such as discharge and flow depth, the continuity equation plays a vital role. In this research, static- and routing-type dynamic artificial neural networks (ANNs) were incorporated in the multiple sections of a river flow on the basis of a storage parameter. Storage characteristics were presented implicitly and explicitly for various sections in a river system satisfying the continuity norm and mass balance flow. Furthermore, the multiple-input multiple-output (MIMO) model form having two base architectures, namely, MIMO-1 and MIMO-2, was accounted for learning fractional storage and actual storage variations and characteristics in a given model form. The model architecture was also obtained by using a trial-and-error approach, while the network architecture was acquired by employing gamma memory along with use of the multi-layer perceptron model form. Moreover, this paper discusses the comparisons and differences between both models. The model performances were validated using various statistical criteria, such as the root-mean-square error (whose value is less than 10% from the observed mean), the coefficient of efficiency (whose value is more than 0.90), and various other statistical parameters. This paper suggests applicability of these models in real-time scenarios while following, continuity norm.

Optimization and Estimation of Transfer Function Model of Milk Evaporation Process

Development of Mathematical model has significant role in many applications such as estimation of relations between input and output variables. The relations can be deterministic or behavioral. From the point of view of control actions to be estimated for a particular application, behavioral model is considered to be of utmost importance. This also amounts the prediction of process behavior in time, frequency and complex domains. Here in this paper, attempts have been made to develop behavioral model of milk evaporation used in dairy industry which is of prominent importance in India because of its agriculture based economy. Besides India, there are also many milk producing countries in the world and their economy is dependent on milk producing animals. It is also imperative that excess milk production to be converted in its preservable form and evaporated milk has special significance in this context. The Milk evaporation is a complex process and its output control variables depend on many of its input manipulation variables. Because of its complex nature and many variables involved in it poses a challenge real life MIMO problem. In this paper, MIMO model of Milk evaporation model is estimated and described.

A Model Free Adaptive Scheme for Integrated Control of Civil Aircraft Trajectory and Attitude

The adaptive trajectory and attitude control is essential for the four-dimensional (4D) trajectory operation of civil aircraft in symmetric thrust flight. In this work, an integrated trajectory and attitude control scheme is proposed based on the =multi-input multi-output (MIMO) model free adaptive control (MFAC) method. First, the full-form dynamic linearization technique is adopted to build the equivalent data model of aircraft. Also, the MIMO MFAC scheme with saturation constraint is designed to achieve an accurate tracking control for a given 4D trajectory and attitude. Besides, the performance limitations of aircraft are taken into consideration, and the MIMO MFAC scheme with hard constraints is designed. In addition, to improve the simulation efficiency, a control scheme with mixed constraints, i.e., saturation and hard constraints, is further proposed. It can be seen from the simulation results that the proposed method can perform an integrated control of the aircraft 4D trajectory and attitude without precise modeling, and the control performance is better than that of the model-based control method in terms of flight altitude and yaw angle control. The integrated data-driven control scheme proposed in this paper provides a theoretical solution for the precise operation of aircraft under 4D trajectory.

Managing driving disturbances in lateral vehicle dynamics via adaptive integrated chassis control

This paper presents a vehicle stability control method based on a multi-input multi-output (MIMO) model reference adaptive control (MRAC) strategy as an advanced driver assistance system (ADAS) to enhance the handling and yaw stability of the vehicle lateral dynamics. The corrective yaw moment and additive steering angle are generated using direct yaw moment control (DYC) and active front steering (AFS) at the upper control level in the hierarchical control algorithm. A nonlinear term is added to the conventional adaptive control laws to handle parametric uncertainties and disturbances. The desired yaw moment generated by the upper-level controller is converted to the brake forces and is distributed to the rear wheels by an optimal procedure at the lower-level. The major contribution of this study is the introduction of a nonlinear integrated adaptive control method based on a constraint optimization algorithm. To verify the effectiveness of the proposed control strategy, the nonlinear integrated adaptive controller, and linear time-varying MRAC are designed and used for comparison. Simulation results are performed for the J-turn and double lane change (DLC) manoeuvres at high speeds and low tyre-road friction coefficients. The desired performance of the proposed controller exhibited significant improvement compared to the conventional MRAC in terms of yaw rate tracking and handling of sideslip limitation.

Multiple Input Multiple Output (MIMO) Model of an Industrial Ecosystem: An Agent-Based Modeling Approach

This paper addresses the application of the Agent-Based Model (ABM) to simulate the evolution of Multiple Input Multiple Output (MIMO) eco-industrial parks to gain insight into their behavior. ABM technique has proven to be an effective tool that can be used to express the evolution of eco-industrial parks. The ABM represents autonomous entities, each with dynamic behavior. The agents within the eco-industrial park are factories, market buyers, and market sellers. The results showed that the Réseau agent-based model allowed the investigation of the behaviors exhibited by different agents in exchange for materials in the industrial park.