Persistency of excitation, sufficient richness and parameter convergence in discrete time adaptive control

The main result of this paper is to give new techniques for deriving explicit conditions on the exogenously specified reference trajectory to guarantee parameter convergence for a class of either single-input single-output or multi-input multi-output discrete time adaptive control schemes. Other interesting results are relations between persistently exciting signals and sufficiently rich signals.     

Design of a stable adaptive controller for driving aerobic fermentation processes near maximum oxygen transfer capacity

In many industrial fermentation processes oxygen availability is the main limiting factor for product production. Typically the dissolved oxygen (DO) concentration decreases continuously at the beginning of the batch until it reaches a critical level where the oxygen transfer rate is very close to the vessel's maximum transfer capacity. The process may be further driven close to this sensitive operating point with a controller that manipulates the carbon source feed rate. This operating strategy is linked with important productivity issues and is still frequently realised in open-loop at production scale. The main purpose of the present study is to derive an effective closed-loop control solution and to demonstrate its economical advantage in relation to the open-loop form of operation. A stable model reference adaptive controller (MRAC) was designed based on a phenomenological model of the process. The implementation requires two on-line measurements: the DO tension and oxygen transfer rate (OTR) between gas–liquid phases, which are nowadays standard and easily available in production facilities. The controller performance is accessed with a simulation case study. The main results show that the adaptive controller is precise, stable and robust to disturbances and to inaccuracies like variability in raw materials typical in fermentations run in complex media. The controller is simple, easy to implement, and could possibly improve productivity in processes for which oxygen transfer capacity is limiting growth and product production.     

A stability criterion for a class of discrete nonlinear systems with random parameters

In this paper, discrete nonlinear models with random parameters are studied. A new frame to classify and analyze discrete stochastic nonlinear systems has been developed from deterministic nonlinear systems to stochastic nonlinear systems. This frame is broad and includes a large class of stochastic nonlinear systems. A stability criterion developed for this frame is a non-Lyapunov method of stability analysis and is easily applied. In addition, this derived sufficient condition of stability is obtained without the assumption of stationarity for the random noise as frequently assumed in the literature.     

Neural network modeling and control of cement mills using a variable structure systems theory based on-line learning mechanism

It is well known that the major cause of instability in industrial cement ball mills is the so-called plugging phenomenon. A novel neural network adaptive control scheme for cement milling circuits that is able to fully prevent the mill from plugging is presented. Estimates of the one-step-ahead errors in control signals are calculated through a neural predictive model and used for controller tuning. A robust on-line learning algorithm, based on sliding mode control (SMC) theory is applied to both: to the controller and to the model as well. The proposed approach allows handling of mismatches, uncertainties and parameter changes in the model of the mill. The simulation results from indicate that both the neural model and the controller inherit the major advantages of SMC, i.e. robustness. Furthermore, learning is achieved in a rapid manner.     

Feedback linearization of a flexible manipulator near its rigid body manifold

It is shown that a ‘slow’ 2n-dimensional manifold exists in the 4n-dimensional state space of an n-link manipulator with n flexible joints. While the flexible manifold is not linearizable by feedback, its restriction to'the slow manifold is. A method is given for computing a feedback control which achieves an approximate linearization.     

Noninteracting control with stability for a class of nonlinear systems

In this paper we address the problem of noninteracting control with stability for the class of nonlinear square systems for which noninteraction can be achieved (without stability) by means of invertible static state-feedback. The use of both static state-feedback and dynamic state-feedback is investigated. We prove that in both cases the asymptotic stabilizability of certain subsystems is necessary to achieve noninteraction and stability. We use this and some recent results to state a complete set of necessary and sufficient conditions in order to solve the problem.     

Adaptive recurrent neural network control using a structure adaptation algorithm

This paper proposes an adaptive recurrent neural network control (ARNNC) system with structure adaptation algorithm for the uncertain nonlinear systems. The developed ARNNC system is composed of a neural controller and a robust controller. The neural controller which uses a self-structuring recurrent neural network (SRNN) is the principal controller, and the robust controller is designed to achieve L ² tracking performance with desired attenuation level. The SRNN approximator is used to online estimate an ideal tracking controller with the online structuring and parameter learning algorithms. The structure learning possesses the ability of both adding and pruning hidden neurons, and the parameter learning adjusts the interconnection weights of neural network to achieve favorable approximation performance. And, by the L ² control design technique, the worst effect of approximation error on the tracking error can be attenuated to be less or equal to a specified level. Finally, the proposed ARNNC system with structure adaptation algorithm is applied to control two nonlinear dynamic systems. Simulation results prove that the proposed ARNNC system with structure adaptation algorithm can achieve favorable tracking performance even unknown the control system dynamics function.     

Indirect approach to continuous time system identification of food extruder

A three-stage approach to system identification in the continuous time is presented which is appropriate for day-to-day application by plant engineers in the process industry. The three stages are: data acquisition using relay feedback; non-parametric identification of the system step response; and parametric model fitting of the identified step response. The method is evaluated on a pilot-scale food-cooking extruder.     

基于ISS的非线性纯反馈系统的自适应动态面控制

研究一类具有未知死区的非线性纯反馈系统的自适应控制问题．基于输入状态稳定理论和小增益定理,提出一种自适应动态面控制方案．该方案有效地减少了可调参数的数目,避免了传统后推设计中由于需要对虚拟控制反复求导而导致的计算复杂性．理论分析证明了闭环系统是半全局一致终结有界的．