Order Nonlinear Plants Research Articles

Design and performance comparison of different adaptive control schemes for pitch angle control in a twin – rotor – MIMO – system

The Twin Rotor MIMO System is a higher order non-linear plant and is inherently unstable due to cross coupling between tail and main rotor. In this paper only the control of main rotor is considered which is non-linear and stable by using adaptive schemes. The control problem is to achieve perfect tracking for input reference signals while maintaining robustness and stability. Four adaptive schemes were implemented, two using Model Reference Adaptive Control under which MIT rule and Modified MIT rule are used. The other two using Adaptive Interaction, namely, Adaptive PID and Approximate Adaptive PID. It is observed that adaptive schemes fulfill all the three system performance requirements at the same time. Modified MIT rule was found to give superior performance in comparison to other controllers. Also Approximate Adaptive PID was able to stabilize the main rotor and cancel the effect of cross coupling between tail rotor and main rotor when operating simultaneously without the need for designing decouplers for the system. Thus the main rotor can be made independent from the state of the tail rotor by using Approximate Adaptive PID.

Nonsingular terminal sliding-mode control of nonlinear planar systems with global fixed-time stability guarantees

Abstract This paper proposes the use of a novel nonsingular Terminal Sliding surface for the finite-time robust stabilization of second order nonlinear plants with matched uncertainties. Mathematical characteristics of the proposed surface are such that a fixed bound naturally exists for the settling time of the state variable, once the surface has been reached. A simple redesign of the control input able to ensure the feature of fixed-time reaching of the sliding surface will be also presented, and fixed-time stability will be guaranteed by the proposed Terminal Sliding Mode Control design method. A careful simulation study has been performed using a benchmark system taken from the literature.

NEURAL MODELLING AND SLIDING MODE CONTROL OF BIODEGRADATION PROCESS IN A ROTATING BIOREACTROR

A new Recurrent Neural Network Model (RNNM) has been applied for measurement data filtering and parameters plus state estimation of hydrocarbons biodegradation process, contained in polluted slurry, treated in a rotating bioreactor. The pattern used for RNNM back-propagation learning is composed by six input variables and three output variables. The total time of learning is 200 epochs of 76 iterations each and the Mean Squared Error reached is below 1.25%. Then the RNNM is simplified and used to design a sliding mode control the two-input two-output high order nonlinear plant. The MSE% of control reached 1% at the end of the process.

Plant-wide predictive control for a thermal power plant based on a physical plant model

A constrained non-linear, physical model-based, predictive control (NPMPC) strategy is developed for improved plant-wide control of a thermal power plant. The strategy makes use of successive linearisation and recursive state estimation using extended Kalman filtering to obtain a linear state-space model. The linear model and a quadratic programming routine are used to design a constrained long-range predictive controller. One special feature is the careful selection of a specific set of plant model parameters for online estimation, to account for time-varying system characteristics resulting from major system disturbances and ageing. These parameters act as non-stationary stochastic states and help to provide sufficient degrees-of-freedom to obtain unbiased estimates of controlled outputs. A 14th order non-linear plant model, simulating the dominant characteristics of a 200 MW oil-fired power plant has been used to test the NPMPC algorithm. The control strategy gives impressive simulation results, during large system disturbances and extremely high rate of load changes, right across the operating range. These results compare favourably to those obtained with the state-space GPC method designed under similar conditions.

Input/output linearization using time delay control and time delay observer

An input/output linearization (IOL) method using time delay control (TDC) and time delay observer (TDO) is presented. This method enables the IOL method to be applied to plants when all the states of plant are not measurable or the measured plant output is very noisy. The designed control system requires neither an accurate plant model nor the real time computation of plant nonlinearity. Consequently, the proposed control algorithm turned out to be computationally efficient and easy to design for nonlinear plants. In a simulation for a second order nonlinear plant, the output followed desired response well and the control performance appeared to be superior to IOL using TDC and numerical differentiation. Finally in an experiment with a pneumatic servo system, we obtained results consistent with those obtained from the simulation, and it was confirmed that the proposed control algorithm can be effectively used in a real closed-loop system.

Singular perturbation method for near optimum design of high-order non-linear systems

This paper extends the singular perturbation method proposed in an earlier paper by the same authors. For a class of non-linear systems, a sufficient condition is given under which the optimal control is continuous and differentiable with respect to perturbations which change the order of the system, the “singular perturbations”. Then a low-order near optimum design for high-order systems is developed. The dimensionality 2( n + m) of the boundary value problem is reduced to 2 n, while the performance achieved is close to optimum. Example of the near-optimum design for a fifth order non-linear plant is included.