极点配置自校正预估PID控制器

对于时滞过程,本文提出了一种新的自校正预估 PID 控制器,它实现了极点配置和静态抗干扰性能,克服了文献[2]提出的自校正预估 PI 控制器和文献[5]提出的自校正预估 PID 控制器不能在线校正控制器参数的缺点.仿真例子说明了本文结果的有效性.     

Robust control of robot manipulators with fast load adaptation

Many adaptive robot controllers have been proposed in the literature to solve manipulator trajectory tracking problems for high-speed operations in the presence of parameter uncertainties. However, most of these controllers stem from the applications of the existing adaptive control theory, which is traditionally focused on tracking slowly time-varying parameters. In fact, manipulator dynamics have fast transient processes for high-speed operations and load changes are abrupt. These observations motivate the present research to incorporate change detection techniques into self-tuning schemes for tracking abrupt load variations and achieving fast load adaptation. To this end, a robustly global stabilizing controller for a robot model with parametric and non-parametric uncertainies is developed based on the Lyapunov second method, and it is then made adaptive via the self-tuning regulator concept. The two-model approach to online change detection in load is used and the estimation algorithm is reinitialized once load changes are detected. This allows a much faster adaptive identification of load parameters than the ordinary forgetting factor approach. Simulation results demonstrate that the proposed controller achieves better tracking accuracy than the existing adaptive and non-adaptive controllers.     

A robust self-tuning scheme for PI- and PD-type fuzzy controllers

Proposes a simple but robust model independent self-tuning scheme for fuzzy logic controllers (FLCs). Here, the output scaling factor (SF) is adjusted online by fuzzy rules according to the current trend of the controlled process. The rule-base for tuning the output SF is defined on error (e) and change of error (Δe) of the controlled variable using the most natural and unbiased membership functions (MFs). The proposed self-tuning technique is applied to both PI- and PD-type FLCs to conduct simulation analysis for a wide range of different linear and nonlinear second-order processes including a marginally stable system where even the well known Ziegler-Nichols tuned conventional PI or PID controllers fail to provide an acceptable performance due to excessively large overshoot. Performances of the proposed self-tuning FLCs are compared with those of their corresponding conventional FLCs in terms of several performance measures such as peak overshoot, settling time, rise time, integral absolute error and integral-of-time-multiplied absolute error, in addition to the responses due to step set-point change and load disturbance and, in each case, the proposed scheme shows a remarkably improved performance over its conventional counterpart     

高阶时滞对象的预测PI(D)控制

利用频率域模型降阶理论,提出了高阶时滞对象的预测PI(D)控制器两种设计方法.一种方法是直接将高阶滞后对象在频率域内降阶为低阶滞后对象,针对低阶滞后对象设计预测PI(D)控制器;另一种方法是按照规定的性能指标设计控制器,并将该控制器在频率域内降阶为具有预测PI(D)控制器的结构形式.这两种方法设计的控制器均具有结构简单、可调参数少、参数调节方便的特点.仿真表明:在模型失配的情况下,此两类预测PI(D)控制器仍然具有良好的控制性能和鲁棒稳定性能.     

Design of a self-tuning rule based controller for a gasolinerefinery catalytic reformer

Design concepts for self-tuning knowledge-based controllers are studied. To accomplish this, two interacting rule-based controllers are constructed for supervisory control and system optimization of a gasoline catalytic reformer. The knowledge bases incorporate human operator experience and basic engineering knowledge about the process dynamics. Inference is provided by a fuzzy logic engine. After manual tuning of the controller scaling coefficient is accomplished, a crisp heuristic is developed for self-tuning. The performance of the self-tuning controller is tested against perturbations of a simulation model of the catalytic reformer     

Fuzzy PI control design for an industrial weigh belt feeder

An industrial weigh belt feeder is used to transport solid materials into a manufacturing process at a constant feedrate. It exhibits nonlinear behavior because of motor friction, saturation, and quantization noise in the sensors, which makes standard autotuning methods difficult to implement. The paper proposes and experimentally demonstrates two types of fuzzy logic controllers for an industrial weigh belt feeder. The first type is a PI-like fuzzy logic controller (FLC). A gain scheduled PI-like FLC and a self-tuning PI-like FLC are presented. For the gain scheduled PI-like FLC the output scaling factor of the controller is gain scheduled with the change of setpoint. For the self-tuning PI-like FLC, the output scaling factor of the controller is modified online by an updating factor whose value is determined by a rule base with the error and change of error of the controlled variable as the inputs. A fuzzy PI controller is also presented, where the proportional and integral gains are tuned online based on fuzzy inference rules. Experimental results show the effectiveness of the proposed fuzzy logic controllers. A performance comparison of the three controllers is also given.     

Control of LTI systems subject to unanticipated extreme perturbations using self-tuning 3-term switching controllers

In the design of conventional control systems for a multivariable system, using robust/adaptive control techniques, the motivation is to design a controller which "works satisfactorily" in the presence of plant uncertainty. Unfortunately, however, if large unanticipated structural changes subsequently occur in the system, severe limitations in practical performance may occur, since such conventional control schemes usually do not have the ability to control systems which are subject to unplanned extreme changes. Moreover, for the realistic situation when control input constraints exist, few results for continuous time multivariable systems are available. In this paper, a new class of self-tuning proportional-integral-derivative switching controllers, which is an extension of the self-tuning integral controller of Miller and Davison, is described, and has the property that it is robust to unplanned extreme changes in the plant and satisfies any feasible control signal input constraints. Results of this self-tuning controller when applied to an experimental multivariable system also are described.     

Intelligent control of exit temperature in a gas-fuel can-type combustor

The dynamical characteristics of a gas-fuel can-type combustor are highly nonlinear and are too complicated to be modeled precisely. Consequently, it is very difficult to control the exit temperature in a combustor using a conventional feedback controller. This paper investigates the models, describing the dynamics of exit temperature for a gas-fuel can-type combustor, and designs the intelligent controllers, based on the characteristics of the constructed models, to control the exit temperature in the combustor. An identified neural network (INN) was utilized to construct the dynamical models because of its powerful learning and handling ability for nonlinear systems. According to the open-loop responses of the investigated models, two controllers, a self-tuning fuzzy proportional–integral–derivative controller and a neural network controller, were developed for the exit temperature control. Experiments were conducted to evaluate the constructed models and the designed controllers.     

On low frequency and long-run effects in self-tuning control

Long-term performance of self-tuning controllers is analysed for low frequency disturbance corrupted single input-single output systems described by five variants of the linear difference equation model. Having the drifts classified, the appropriate models to allow for drift effects are specified and standard recursive least squares parameter estimation schemes are applied. Optimal single-stage control strategies are comparatively analysed both from the steady-state accuracy and stability points of view. As a result, an ‘extended self-tuning controller’ is developed and its superiority over classical ones is demonstrated. The related problem of ‘blow-up’ protection for self-tuning controllers is also investigated and some effective measures are proposed.     

Thermal management in laminated die system

The thermal control of a die is crucial for the development of high efficiency injection moulds. For an effective thermal management, this research provides a strategy to identify a thermal dynamic model and to design a controller. The neural network techniques and finite element analysis enable modeling to deal with various cycle-times for moulding process and uncertain dynamics of a die. Based on the system identification which is experimentally validated using a real system, controllers are designed using fuzzy-logic and self-tuning PID methods with backpropagation and radial basis function neural networks to tune control parameters. Through a comparative study, each controller’s performance is verified in terms of response time and tracking accuracy under different moulding processes with multiple cycle-times.     

Neurofuzzy network based self-tuning control with offset eliminating

The design of nonlinear controllers involves first selecting the input and then determining the nonlinear functions for the controllers. Since systems described by smooth nonlinear functions can be approximated by linear models in the neighbourhood of the selected operating points, the input of the nonlinear controller at these operating points can be chosen to be identical to those of the local linear controllers. Following this approach, it is proposed that the input of the nonlinear controller are similarly chosen, and that the local linear controllers are designed based on the integrating and k-incremental suboptimal control laws for their ability to remove offsets. Neurofuzzy networks are used to implement the nonlinear controllers for their ability to approximate nonlinear functions with arbitrary accuracy, and to be trained from experimental data. These nonlinear controllers are referred to as neurofuzzy controllers for convenience. As the integrating and k-incremental control laws have also been applied to implement self-tuning controllers, the proposed neurofuzzy controllers can also be interpreted as self-tuning nonlinear controllers. The training target for the neurofuzzy controllers is derived, and online training of the neurofuzzy controllers using a simplified recursive least squares (SRLS) method is presented. It is shown that using the SRLS method, computing time to train the neurofuzzy controllers can be drastically reduced and the ability to track varying dynamics improved. The performance of the neurofuzzy controllers and their ability to remove offsets are demonstrated by two simulation examples involving a linear and a nonlinear system, and a case study involving the control of the drum water level in the boiler of a power generation system.     

Robust stabilisation of rotary inverted pendulum using intelligently optimised nonlinear self-adaptive dual fractional-order PD controllers

This article presents an intelligently optimised self-tuning fractional-order control scheme to improve the attitude-stabilisation of an inverted pendulum. Primarily, the scheme employs two Fractional-order Proportional-Derivative (FPD) controllers acting concurrently on the system to minimise the deviations in its state-trajectories. Wherein, one FPD controller compensates the variations in pendulum-angle and its fractional-order derivative to vertically balance the pendulum, where as the other FPD controller acts as a position controller and regulates the variations in arm-angle and its fractional-order derivative. The integration of fractional calculus with conventional PD controllers optimises the reference-tracking performance of the control scheme by increasing its degrees-of-freedom and design flexibility. In order to further improve the system’s immunity against exogenous disturbances, the PD gains of each controller are dynamically adjusted after each sampling interval using piecewise nonlinear functions of their respective state-variations. The hyper-parameters of the nonlinear gain-adjustment functions as well as the fractional-number power of the derivative-operator of each controller are selected via Particle-Swarm-optimisation (PSO) algorithm. The proposed adaptive control scheme is tested on the QNET Rotary Inverted Pendulum setup via ‘hardware-in-the-loop’ experiments. The optimality and robustness of the proposed control scheme are validated by comparing its performance with PSO-based fixed-gain dual-PD and dual-FPD control schemes.     

A novel approach to monitoring and maintenance of industrial PID controllers

Due to process nonlinearities and operating condition changes, industrial processes frequently encounter significant dynamics variations, which would compromise the long-term effectiveness of controller monitoring schemes and leads to superfluous alarms. To address these issues, a novel performance benchmark based on the min-max principle is developed for industrial PID controllers, which has satisfactory applicability for nonlinear processes with operating condition switches. Furthermore, a holistic workflow of monitoring and maintenance of industrial PID controllers is proposed, including online identification of process models, poor control detection based on the accessible benchmark, and maintenance guides for poorly behaved controllers. Both simulation and industrial cases studies are presented to demonstrate the effectiveness of the proposed method.     

On-line PI control of stable processes

A relay based on-line automatic tuning method for PI controllers for stable processes is presented. In the proposed method, prior to controller re-tuning a relay in tandem with the controller and plant induces limit cycle oscillations. Based on the limit cycle measurements, a first order plus dead time (FOPDT) model of the process dynamics is obtained. Simple tuning rules based on ISTE performance criterion and the first order model are developed. The controller settings may be re-tuned non-iteratively to achieve enhanced performance without disrupting closed loop control. A number of simulation examples are given to illustrate the potential advantages of the proposed on-line tuning method.     

Precomputation of generalized predictive self-tuning controllers

A method for implementing generalized predictive self-tuning controllers is presented which avoids heavy computational requirements. The method makes use of the fact that a general predictive controller using a quadratic function results in a linear control law that can be described by a few parameters. These parameters are computed over the range of interest of the process parameters and a function is used to obtain an approximation to the real controller parameters. The controllers' parameters are given for processes which can be modeled by a static gain, a time constant, and an effective dead time, that is, for the majority of processes in industry     

Wavelet neural adaptive proportional plus conventional integral-derivative controller design of SSSC for transient stability improvement

Although the PI or PID (PI/PID) controllers have many advantages, their control performance may be degraded when the controlled object is highly nonlinear and uncertain; the main problem is related to static nature of fixed-gain PI/PID controllers. This work aims to propose a wavelet neural adaptive proportional plus conventional integral-derivative (WNAP+ID) controller to solve the PI/PID controller problems. To create an adaptive nature for PI/PID controller and for online processing of the error signal, this work subtly employs a one to one offline trained self-recurrent wavelet neural network as a processing unit (SRWNN-PU) in series connection with the fixed-proportional gain of conventional PI/PID controller. Offline training of the SRWNN-PU can be performed with any virtual training samples, independent of plant data, and it is thus possible to use a generalized SRWNN-PU for any systems. Employing a SRWNN-identifier (SRWNNI), the SRWNN-PU parameters are then updated online to process the error signal and minimize a control cost function in real-time operation. Although the proposed WNAP+ID is not limited to power system applications, it is used as supplementary damping controller of static synchronous series compensator (SSSC) of two SSSC-aided power systems to enhance the transient stability. The nonlinear time-domain simulation and system performance characteristics in terms of ITAE revealed that the WNAP+ID has more control proficiency in comparison to PID controller. As additional simulations, the features of the proposed controller are compared to those of the literature while some of its promising features like its fast noise-rejection ability and its high online adapting ability are also highlighted.     

Auto-tune system using single-run relay feedback test and model-based controller design

In this paper, a systematic approach for auto-tune of PI/PID controller is proposed. A single run of the relay feedback experiment is carried out to characterize the dynamics including the type of damping behavior, the ultimate gain, and ultimate frequency. Then, according to the estimated damping behavior, the process is classified into two groups. For each group of processes, model-based rules for controller tuning are derived in terms of ultimate gains and ultimate frequencies. To classify the processes, the estimation of an apparent deadtime is required. Two artificial neural networks (ANNs) that characterize this apparent deadtime using the ATV data are thus included to facilitate this estimation of this apparent deadtime. The model-based design for this auto-tuning makes uses of parametric models of FOPDT (i.e. first-order-plus-dead-time) and of SOPDT (i.e. second-order-plus-dead-time) dynamics. The results from simulations show that the controllers thus tuned have satisfactory results compared with those from other methods.     

Practical issues in the implementation of self-tuning control

Implementation aspects of self-tuning regulators are discussed in the paper. There is a large discrepancy between simulation or academic algorithms and practical algorithms. In the idealized environment of simulations it is easy to get different types of adaptive algorithms to perform well. In practice the situation is quite opposite. The adaptive or self-tuning controller must be able to handle nonlinearities, unmodelled dynamics and unmodelled disturbances over a wide range of operating conditions. Some aspects of how to implement self-tuning controllers are discussed in the paper. This includes robustness, signal conditioning, parameter tracking, estimator wind-up, reset action and start-up. Different ways to use the prior knowledge about the process are also discussed.     

Implicit and explicit LQG self-tuning controllers

The design of optimal controllers for use in self-tuning systems is considered. Linear Quadratic Gaussian (LQG) controllers are widely used elsewhere but have not until recently been applied in self-tuning systems, except in minimum output variance forms. The LQG controllers offer a guarantee of stability (when the plant parameters are known) which is particularly useful for nonminimum phase systems. The explicit LQG self-tuning controllers introduced in the following are relatively simple to implement. The first version of an implicit LQG self-tuning controller is also introduced. The magnitude of the transport delay terms need not be known a priori and integral action may be introduced easily. If required the desired closed-loop poles of the system can be prespecified.