Integral minimum variance control and benchmarking

A minimum-variance control problem is defined that involves the minimisation of the integral of estimation error. This introduces integral action into the controller. It is believed that the resulting solution is more valuable for closed-loop performance assessment and benchmarking, than the usual minimum-variance results. This is because most industrial controllers need to have integral action and hence the proposed benchmark, which is based on a controller that includes an integrator, will often be more appropriate. The advantage over many other benchmarking methods lies in the simplicity of the results. The situation where the controller structure has a limited, or restricted structure, is also considered. That is, a method is presented where the controller structure may be prespecified, and the coefficients obtained by direct parameter optimisation. This provides a more direct basis for comparison with controllers implemented in existing plants, that may only include, say PID, restricted structures.     

A new method for synthesizing multiple-period adaptive-repetitive controllers and its application to the control of hard disk drives

This paper introduces a new method for synthesizing multiple-period repetitive controllers. The main innovations in the synthesis procedure presented in this article are two. The first one is that this technique yields a solution compatible with the integration of the computed multiple-period repetitive controller into a minimum-variance adaptive control scheme. The second innovation is that the solution is period-recursive, reducing the complexity of controller synthesis considerably when compared with other methods available in the literature. To exemplify the synthesis procedure, a multiple-period repetitive controller is designed and integrated into an adaptive-repetitive control scheme used in the track-following control of a commercial hard disk drive. Experimental results show the effectiveness of the presented approach.     

Multisensor fusion fault tolerant control

In this paper, a multisensor fusion fault tolerant control system with fault detection and identification via set separation is presented. The fault detection and identification unit verifies that for each sensor–estimator combination, the estimation tracking errors lie inside pre-computed sets and discards faulty sensors when their associated estimation tracking errors leave the sets. An active fault tolerant controller is obtained, where the remaining healthy estimates are combined using a technique based on the optimal fusion criterion in the linear minimum-variance sense. The fused estimates are then used to implement a state feedback tracking controller. We ensure closed-loop stability and performance under the occurrence of abrupt sensor faults. Experimental validation, illustrating the multisensor fusion fault tolerant control strategy is included.     

A new output feedback stabilization controller for the disturbance attenuation of MIMO non-linear systems

In this paper, an output feedback discontinuous controller is proposed for a class of non-linear systems to attenuate the disturbance influence on the system performance. Our main goal is to globally stabilize the non-linear system in the presence of unknown structural system uncertainties and external disturbances. Our approach consists of constructing a high gain non-linear observer to reconstruct the unavailable states, and then design discontinuous controller using a backstepping like design procedure to ensure stability of the non-linear system. The design parameters of the observer and the controller are determined in a complementary manner to ensure stability. As a result the whole system can be stabilized while internal stability of the system states is also ensured. Finally, an example is presented to show the effectiveness of the proposed scheme.     

Adaptive minimum-variance and model-reference control for non-minimum phase systems

The well known minimum-variance adaptive controller and model-reference controller have been extended to include non-minimum phase systems. The simplicity of the original controllers is maintained. By allowing the intersample control signal to have two values, rather than one, the system zeros can be allocated to some prespecified locations. The stability issue related to both controllers for non-minimum phase systems is thus resolved.     

Time-domain performance based non-linear state feedback control of constrained linear systems

This article describes a method to design a non-linear state feedback controller that meets a set of time-domain specifications not attainable by linear state feedback. Using a constrained polynomial interpolation technique, an input signal is computed that satisfies the desired time-domain constraints on the input and state-trajectories. The computed input is constructed by non-linear combinations of the states, such that a non-linear state feedback law is obtained. Stability of the resulting closed-loop polynomial system is analysed using sum-of-squares techniques. An illustrative example is presented, showing that the proposed non-linear controller outperforms the best linear static state feedback. To validate the proposed method, experiments on a fourth-order motion system have been carried out.     

Adaptive control for a class of large scale non-linear systems

The design of stabilizing controllers for multi-input multi-output (MIMO) non-linear plants with unknown non-linearities is a challenging problem. The inability to identify the non-linearities on-line or off-line accurately motivates the design of stabilizing controllers based on approximations or on approximate estimates of the plant non-linearities. However, the design of a centralized controller for large scale non-linear systems is often complex due to the high dimensionality and difficulty to implement in real time. In this paper a decentralized-like non-linear adaptive control algorithm is designed and analysed for a class of large scale non-linear systems with unknown non-linearities. The controller guarantees closed loop semi-global stability and convergence of the tracking error to a small residual set whose size can be specified a priori provided the neglected in the control design non-linear interconnections are small relative to the modelled non-linear parts. A procedure for choosing the various design parameters to guarantee that the tracking error bound will converge to within the specified a priori bound is presented. Even though the proposed controller is not purely decentralized it does reduce computations and makes the control design easier than a corresponding centralized approach.     

On the control of non-linear processes: An IDA–PBC approach

A high performance non-linear controller that exploits the properties of port-Hamiltonian systems to precisely define the interconnections and energy dissipations of non-linear processes is presented for a large class of chemical processes. The controller is derived from classical interconnection and damping assignment–passivity based control theory but an additional degree of freedom is introduced in the controller design by the use of “non-exact matching” closed-loop storage functions. By a proper closed-loop interconnection assignment the proposed controller achieves total decoupling between outputs and since no inversion of the process dynamics is made in the design it is equally applicable to minimum phase and nonminimum phase system. The controller design methodology is presented and stability conditions are stated. As illustrative example the proposed method is used to design controllers for a multiple-input/multiple-output non-isothermal continued stirred tank reactor that exhibits nonminimum phase behavior.     

Fuzzy Logic Controller for a Mini Coaxial Indoor?Helicopter

In this paper the design process of a fuzzy logic based controller for a coaxial micro helicopter is presented. The developed controller for altitude, attitude, and position control is tested through simulations on an identified non-linear model of the helicopter. The robustness properties of the controller to parameter variations of the model is assessed, as well as, its ability to accommodate or absorb external disturbances.     

Adaptive control with optimal robust tracking

The problem of optimal robust tracking in two-parameter adaptive control systems under non-linear time-varying unmodelled dynamics is examined. A new robust stability criterion is derived for analysing the robustness of adaptive control systems with non-linear time-varying model errors. Based on the concept of excess robustness and the theory of the minimum H^∞norm, a simple and feasible design algorithm is presented to synthesize a two-parameter adaptive controller which ensures that adaptive control systems can achieve the object of optimal robust tracking in the presence of non-linear time-varying unmodelled dynamics. Simulation results that demonstrate features of the two-parameter adaptive controller with optimal robust tracking in the light of the design algorithm are included.     

基于内模控制的PID控制器在大时滞过程中的应用研究

基于内模控制的PID控制器,只有一个可调参数,且该参数直接与系统的闭环响应速度和回路的鲁棒性在前,克服了常规PID控制参数整定复杂的缺点,易于为工程技术人员所掌握。城市供水出水浊度过程是一个大惯性、大时滞、非线性、时变以及随机干扰多的难控对象。而把IMC—PID控制器应用在该对象上,仿真结果表明可以取得较理想的控制效果。     

Distributed adaptive output consensus control of second-order systems containing unknown non-linear control gains

In this paper, we address the output consensus problem of tracking a desired trajectory for a group of second-order agents on a directed graph with a fixed topology. Each agent is modelled by a second-order non-linear system with unknown non-linear dynamics and unknown non-linear control gains. Only a subset of the agents is given access to the desired trajectory information directly. A distributed adaptive consensus protocol driving all agents to track the desired trajectory is presented using the backstepping technique and approximation technique of Fourier series (FSs). The FS structure is taken not only for tracking the non-linear dynamics but also the unknown portion in the controller design procedure, which can avoid virtual controllers containing the uncertain terms. Stability analysis and parameter convergence of the proposed algorithm are conducted based on the Lyapunov theory and the algebraic graph theory. It is also demonstrated that arbitrary small tracking errors can be achieved by appropriately choosing design parameters. Though the proposed work is applicable for second-order non-linear systems containing unknown non-linear control gains, the proposed controller design can be easily extended to higher-order non-linear systems containing unknown non-linear control gains. Simulation results show the effectiveness of the proposed schemes.     

Self-tuning of 2 DOF control based on evolving fuzzy model

In this paper we present a self-tuning of two degrees-of-freedom control algorithm that is designed for use on a non-linear single-input single-output system. The control algorithm is developed based on the Takagi-Sugeno fuzzy model, and it consists of two loops: a feedforward loop and feedback loop. The feedforward part of the controller should drive the system output to the vicinity of the reference signal. It is developed from the inversion of the T-S fuzzy model. To achieve accurate error-free reference tracking a feedback part of the controller is added. A time-varying error-model predictive controller is used in the feedback loop. The error-model is obtained from the T-S fuzzy model. The T-S fuzzy model of the system, required in the controller, is obtained with evolving fuzzy modelling, which is based on recursive Gustafson-Kessel clustering algorithm and recursive fuzzy least squares. It employs evolving mechanisms for adding, removing, merging and splitting the clusters.The presented control approach was experimentally validated on a non-linear second-order SISO system helio-crane in simulation and real environment. Several criteria functions were defined to evaluate the reference-tracking and disturbance rejection performance of the control algorithm. The presented control approach was compared to another fuzzy control algorithm. The experimental results confirm the applicability of the approach.     

Robust decentralized stabilization of a class of linear discrete-time systems with non-linear interactions

This paper deals with robust stabilization of a class of linear discrete-time systems under non-linear perturbations via output feedback. A bound on the non-linear perturbations is maximized in the design. It is shown that degree of freedoms by the introduction of instrumental variables employed in this paper lead to much flexibility in obtaining both a robust output feedback controller and a maximal allowable bound of the non-linear perturbations. An improved method involving linear matrix inequalities are suggested to solve the matrix inequalities characterizing a solution of the robust stabilization problem. Consequently, the proposed method can yield a much less conservative result than that of earlier methods. Of major interest is an extension to a class of interconnected systems composed of linear subsystems with non-linear interactions. A robust decentralized controller is presented such that the closed-loop systems are maximally tolerant to interconnected non-linear couplings. Numerical examples illustrate the validity of the proposed approach.     

Design of a controller for a multivariable system with varying parameters using the extended direct Nyquist array

The extended direct Nyquist-array design method is used to design a controller for a gas-turbine-powered marine propulsion plant, the parameters of which are dependent upon load conditions. Non-linear simulation results for the compensated system are presented and compared with the non-linear responses obtained using a conventional controller and also a multivariable gain-scheduled controller developed in previous work.     

Robot learning control based on recurrent neural network inverse model

A non-linear model-based feedforward, feedback, and learning controller is presented. This controller can control a non-linear plant such as a robot whose dynamics are initially unknown. In the feedforward part, a recurrent neural network (RNN) is used to model the inverse dynamics of the plant. In the feedback part, a PD controller is added to handle unmodeled dynamics and disturbances. Furthermore, an add-on learning controller is established to reduce tracking errors for repetitive tasks. The controller is validated with the control of a simulated two-joint manipulator. Simulation results show that the controller can successfully learn the inverse dynamics of a robot, perform accurate tracking for a general trajectory, and improve its own performance over the repetitions of a trajectory, with and without a payload change. © 1997 John Wiley & Sons, Inc.     

On the estimation of the mean of a stochastic process

In this paper the optimal minimum-variance unbiased linear discrete estimate of the mean of a stationary, random process is derived. The behaviour of the estimate in case of increasing the number of samples is discussed. An attempt is presented to apply the technique developed to the recurrential algorithm for an on-line estimation. However, because of the difficulties of obtaining a globally optimal algorithm a suboptimal, one-step, minimum-variance algorithm is derived.     

一种改进的Fuzzy-PID温度控制器设计及其在供热网控制系统中的应用

本文针对供热网控制系统的大时滞和非线性等特点,在以往Fuzzy-PID温度控制器的基础上,介绍了一种改进的设计方法。通过对现有模糊控制器的输入量的改进,提高了控制器的动态响应性能;同时利用组合遗传算法来优化PID的三个参数Kp,Ki,Kd,提高了控制器的控制精度;最后,通过一个供热网的温度控制系统的仿真实验验证了这种改进的Fuzzy-PID温度控制器有着较强的动态响应性能和良好的控制精度。     

A microcomputer based adaptive regulator for power system applications

The paper presents the design of an adaptive regulator for excitation control of a synchronous generator connected to an infinite bus through a double-circuit transmission line. The regulator, which is derived by simplification of the well known minimum-variance self-tuning controller, uses a variable forgetting factor stochastic approximation variant of the recursive least-square parameter estimator. This low order self-tuning algorithm is based on 3-parameter identification and is suitable for implementation with a single-chip microcomputer having restricted processing power, limited on-chip program storage and limited on-chip data storage capabilities. Simulation results are presented for a sample power system which is subjected to disturbances such as 3-phase short circuits, changes in terminal voltage and stochastic load variations. Tests using an LSI-11/23 microcomputer show acceptable performance in eliminating steady state errors and adapting rapidly to any changes in system operating conditions.     

Model reference control of non-linear systems with relative order two: application to a semibatch reactor

The design of a robust non-linear feedback controller incorporating integral and derivative actions is presented for non-linear systems with relative order two. The design method is applied to product concentration tracking in the case of parallel reactions occurring in a semibatch isothermal reactor. The simulation results show the effectiveness of the control scheme and its robustness to modelling errors.