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Model identification of a linear system is a matured topic in control engineering. Whereas identifying the model of a nonlinear multi-input-multi-output (MIMO) system is more challenging and complex task. In chemical industries, coupled-tank system is a very well known process entity which is also MIMO in nature with nonlinear behavior. Here, in our reported work, we have identified the model of a...
In this paper, a new constrained multivariable predictive control strategy based on T-S modeling approach and Particle Swarm Optimization is considered. The proposed method determines the control actions by minimizing a nonlinear criterion based on Particle Swarm Optimization algorithm. At optimization stage, the proposed algorithm integrates the Alienor method in order to find a set of solutions...
This paper describes a new multi-objective predictive controller approach for nonlinear Multi Inputs Multi Outputs process. The proposed approach is formulated by using a New constrained Multi-objective PSO algorithm and T-S Fuzzy System. T-S Fuzzy modeling approach is used to forecast the behaviors of nonlinear Multi Inputs Multi Outputs process. The proposed PSO algorithm is developed to determine...
Although conventional controllers based on PID, Fuzzy logic and Neural networks are able to meet the working requirements, the ever increasing thirst to achieve perfection by reducing overshoots, undershoots, transient and steady state errors have led to Model Predictive Controllers (MPC). These controllers depend on the error between the actual output from the plant and the computed output generated...
In this paper a two degree of freedom Twin Rotor MIMO System (TRMS), which is employed to model the pitch and yaw directions of a helicopter, is considered. Using the model of TRMS, MATLAB simulaitons are performed. The open loop model of the system is unstable. Since the system is both controllable and observable, a robust and optimal Model Predictive Controller (MPC) is designed to control the system...
Steady-state control error occurs in case of unmeasured disturbances or disagreement between the model and the actual behaviour of the system because, state-space controllers do not have integral character. This problem (offset-free reference tracking) of state-space controllers has been observed in both theoretical and application areas (industrial MPC Implementation IDCOM, DMC, and QDMC IDCOM-M)...
This paper investigates the use of Dynamic Model Control (DMC) on an Activated Sludge Reactor. The reduced bio-reactor activated sludge ASM1 model, which describes the biological degradation of an activate sludge reactor, is designed based on several simplifications, as a Takagi Sugeno fuzzy model (TS). The TS model structure is based on a set of linear sub models, covering the process input-output...
The purpose of this paper is to present a MIMO Smith Predictor (SP) based on gain-scheduling controller for multiple pool canal systems. The MIMO Smith Predictor structure takes into account the variations of the parameters and delays with the operating point by using a linear parameter varying (LPV) model of the multiple pool canal system. The error in the estimation of the delays is bounded by unstructured...
A new approach to identification of multi-input multi-output (MIMO) Hammerstein-Wiener system is presented. The output nonlinear block consists of several single-input single-output (SISO) blocks, one of which is dead zone and saturation nonlinearity. The hinging hyperplane (HH) model expresses the character. The MIMO input nonlinear block is described by multi layer feed forward neural networks....
We consider a multi-user multiple-input multiple-output (MIMO) system using block diagonalization and eigenbeam-space division multiplexing to suppress inter-user interference and inter-stream interference. In time-varying environments, the performance seriously degrades due to mistracking of beamforming. A channel prediction scheme can eliminate the effect of the time-varying environments. In this...
In this paper, the Hammerstein fault prediction modeling based on least squares support vector machines (LS-SVM) is presented for the prediction the key parameters of the imperial smelting furnace (ISF). ISF is a nonlinear, multi-input and multi-output (MIMO) system that is difficult to model by the classical methods. Due to the particularly simple structure of the Hammerstein model and the generalization...
This paper investigates the performance of two Multivariable Model Predictive Control (MPC) strategies: selfish and solidary. These strategies are based on the main ideas developed in the EPSAC (Extended Prediction Self-Adaptive Control) approach to MPC. A two degree of freedom (2DOF) helicopter simulation has been chosen to illustrate these concepts, as it represents a complicated and challenging...
Decoupling multi-input multi-output systems is a simplifying technique to design, implement and tune control systems. If there are also multiple delays, the decoupling is even more interesting although there is always a prize to pay and the resulting controllers can become excessively complex. In this paper a new approach to deal with these situations is presented. The procedure involves extracting...
Teaching multivariable control usually involves a certain level of mathematical sophistication and hence requires some labaratorial exemplification of the material given in formal lectures. This paper reports on a hands-on approach to multi-variable control education via the implementation of a model predictive controller on a two-input, two output coupled drive apparatus. This scaled-down system...
This paper proposes a new mathematical method to solve min-max predictive controller for a class of constrained linear Multi Input Multi Output (MIMO) systems. A parametric uncertainty state space model is adopted to describe the dynamic behavior of the real process. Since the resulting optimization problem is non convex, a deterministic global optimization technique is adopted to solve it which is...
During the last years, multiple-input multiple-output (MIMO) and OFDM systems emerged as a solution for the high data link in radio communication. The performances of these systems depend not only on the propagation channel path loss but also on the delay and the angular spread of the channel impulse response [1]. Ray-tracing or ray-launching models have the advantage to predict all channel large-scale...
Predictive control seems to be a promising approach that can help to improve properties of existing ventilation systems applied in road tunnels. Advantages of predictive control result mainly from its ability to solve both SISO and MIMO tasks, to have regard for dynamics of process changes in a broad extent, to compensate effect of measurable and non-measurable failures and to formulate the task as...
This paper presents the empirical modeling of the gaseous pilot plant which is a kind of interacting series process with presence of nonlinearities. In this study, the discrete-time identification approach based on subspace method with N4SID algorithm is applied to construct the state space model around a given operating point, by probing the system in open-loop with variation of input signals. Three...
Using Model-Based Control the complex control problem presented by the Remote Minehunting and Disposal System is reduced to a series of decoupled single-input single-output independent control loops that can be controlled by traditional off-the-shelf PID controllers.
Unit load system of power plant is a double-input and double-output multivariable system with big inertia. The conventional control method cannot get satisfactory result. In this paper, the system is firstly decomposed into two double-input and single-output subsystems and two MAC controllers are designed accordingly. By solving the two controllerspsila equations, predictive control sequence is obtained...
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