饱和离散时间系统非保守输出反馈控制器设计

针对输入饱和离散系统由于采用输出反馈而导致的控制器设计存在很强保守性的问题,将凸多面体分析的方法应用于系统吸引域描述中,给出了基于状态的系统可控域的顶点描述和面描述形式,建立了系统输出反馈与基于状态的系统可控域之间的关系;为解决由于不稳定系统输出反馈第一步控制不施加任何控制作用而造成的系统状态可控域大大减小的保守性问题,提出了基于状态观测器的输出反馈非保守控制器设计方法;针对二阶不稳定系统,根据系统输出矩阵及输出初始值的不同情况,给出了输出反馈控制器第一步控制作用的具体形式,并证明了在该控制器作用下,系统的可控域达到最大,从而最大程度减小了控制器的保守性。最后通过Matlab进行了数值仿真实例研究。研究结果验证了所设计控制器的有效性。     

Sliding mode controllers for a tempered glass furnace

This paper investigates the design of two sliding mode controllers (SMCs) applied to a tempered glass furnace system. The main objective of the proposed controllers is to regulate the glass plate temperature, the upper-wall temperature and the lower-wall temperature in the furnace to a common desired temperature. The first controller is a conventional sliding mode controller. The key step in the design of this controller is the introduction of a nonlinear transformation that maps the dynamic model of the tempered glass furnace into the generalized controller canonical form; this step facilitates the design of the sliding mode controller. The second controller is based on a state-dependent coefficient (SDC) factorization of the tempered glass furnace dynamic model. Using an SDC factorization, a simplified sliding mode controller is designed. The simulation results indicate that the two proposed control schemes work very well. Moreover, the robustness of the control schemes to changes in the system׳s parameters as well as to disturbances is investigated. In addition, a comparison of the proposed control schemes with a fuzzy PID controller is performed; the results show that the proposed SDC-based sliding mode controller gave better results.     

Design of nonlinear PID controller and nonlinear model predictive controller for a continuous stirred tank reactor

In this paper, the authors have represented the nonlinear system as a family of local linear state space models, local PID controllers have been designed on the basis of linear models, and the weighted sum of the output from the local PID controllers (Nonlinear PID controller) has been used to control the nonlinear process. Further, Nonlinear Model Predictive Controller using the family of local linear state space models (F-NMPC) has been developed. The effectiveness of the proposed control schemes has been demonstrated on a CSTR process, which exhibits dynamic nonlinearity.     

A novel interval type-2 fractional order fuzzy PID controller: Design,performance evaluation,and its optimal time domain tuning

In this paper, a novel concept of an interval type-2 fractional order fuzzy PID (IT2FO-FPID) controller, which requires fractional order integrator and fractional order differentiator, is proposed. The incorporation of Takagi-Sugeno-Kang (TSK) type interval type-2 fuzzy logic controller (IT2FLC) with fractional controller of PID-type is investigated for time response measure due to both unit step response and unit load disturbance. The resulting IT2FO-FPID controller is examined on different delayed linear and nonlinear benchmark plants followed by robustness analysis. In order to design this controller, fractional order integrator-differentiator operators are considered as design variables including input-output scaling factors. A new hybridized algorithm named as artificial bee colony-genetic algorithm (ABC-GA) is used to optimize the parameters of the controller while minimizing weighted sum of integral of time absolute error (ITAE) and integral of square of control output (ISCO). To assess the comparative performance of the IT2FO-FPID, authors compared it against existing controllers, i.e., interval type-2 fuzzy PID (IT2-FPID), type-1 fractional order fuzzy PID (T1FO-FPID), type-1 fuzzy PID (T1-FPID), and conventional PID controllers. Furthermore, to show the effectiveness of the proposed controller, the perturbed processes along with the larger dead time are tested. Moreover, the proposed controllers are also implemented on multi input multi output (MIMO), coupled, and highly complex nonlinear two-link robot manipulator system in presence of un-modeled dynamics. Finally, the simulation results explicitly indicate that the performance of the proposed IT2FO-FPID controller is superior to its conventional counterparts in most of the cases.     

Analysis,control and design of a non-inverting buck-boost converter: A bump-less two-level T–S fuzzy PI control

In this paper, a new modified fuzzy Two-Level Control Scheme (TLCS) is proposed to control a non-inverting buck-boost converter. Each level of fuzzy TLCS consists of a tuned fuzzy PI controller. In addition, a Takagi–Sugeno–Kang (TSK) fuzzy switch proposed to transfer the fuzzy PI controllers to each other in the control system. The major difficulty in designing fuzzy TLCS which degrades its performance is emerging unwanted drastic oscillations in the converter output voltage during replacing the controllers. Thereby, the fuzzy PI controllers in each level of TLCS structure are modified to eliminate these oscillations and improve the system performance. Some simulations and digital signal processor based experiments are conducted on a non-inverting buck-boost converter to support the effectiveness of the proposed TLCS in controlling the converter output voltage.     

Neural network controller for robotic motion control

Since a robotic manipulator has a complicated mathematical model, it is difficult to design a control system based on the complicated multi-variable nonlinear coupling dynamic model. Intelligent controllers using fuzzy and neural network approaches do not need a real mathematical model to design the control structure and have attracted the attention of robotic control researchers recently. A traditional fuzzy logic controller does not have learning capability and it needs a lot of effort to search for the optimal control rules and the shapes of membership functions. Owing to the time-varying behaviour of the system, the required fine tracking accuracy is difficult to achieve by adjusting the fuzzy rules only. The implementation problems of neural network control are the initial training and initial transient stability. In order to improve the position control accuracy and system robustness for industrial applications, a neural controller is first trained off-line by using the input and output (I/O) data of a traditional fuzzy controller. Then the neural controller is implemented on a five-degrees-of-freedom robot with a back propagation algorithm for online adjustment. The experimental results show that this neural network controller achieved the required trajectory tracking accuracy after 15 on-line operations.     

A neural network-based adaptive algorithm on the single EWMA controller

The single EWMA controller has been proven to have excellent performance for small disturbances in the run-to-run process. However, incorrect selection of the EWMA parameter can have the opposite effect on the controlled process output. An adaptive system is necessary to automatically adjust the controller parameters on-line in order to have better performance. In this study, a simple and efficient algorithm based on neural networks (NN) is proposed to minimise the inflation of the output variance on line. The authors have shown that the sequence of EWMA gains, generated by a NN-based adaptive approach, converges close to the optimal controller value under IMA (1, 1), step and trend disturbance models. The paper also shows that the NN-based adaptive EWMA controller has a superior performance than its predecessors.     

Adaptive Control of Multivariable Process Using Recurrent Neural Networks

Abstract

Industrial processes are naturally multivariable in nature, which also exhibit non-linear behavior and complex dynamic properties. The multivariable four-tank system has attracted recent attention, as it illustrates many concepts in multivariable control, particularly interaction, transmission zero, and non-minimum phase characteristics that emerge from a simple cascade of tanks. So, the multivariable laboratory process of four interconnected water tanks is considered for modeling and control. For processes which show nonlinear and multivariable characteristics, classical control strategies like PIDs have performance limitations. Hence, intelligent approaches like Neural Networks (NN) is an important term in this juncture. The use of Recurrent Neural Network (RNN) is apt for modeling and control of nonlinear dynamic processes as it contains the past information about the process. The objective of the current study is to design and implement an adaptive control system using RNN for a nonlinear multivariable process.

The proposed adaptive design comprises an estimator based on RNN, which adapts online and predicts one step ahead output. A Recursive Least Square (RLS) based back propagation algorithm is used for training the network. The controller used is also a RNN, which minimizes the difference between the predicted output and reference trajectory. The objective function is minimized using a steepest descent algorithm which gives the optimum control input. Desired performance of the system is ensured by the parallel operation of both. The proposed control strategy is implemented in a laboratory scale four tank system. The trajectory tracking and disturbance rejection response obtained are compared with the response obtained by using a well designed decoupled, decentralized IMC controller.     

Design of a fractional order PID controller using GBMO algorithm for load–frequency control with governor saturation consideration

Load–frequency control is one of the most important issues in power system operation. In this paper, a Fractional Order PID (FOPID) controller based on Gases Brownian Motion Optimization (GBMO) is used in order to mitigate frequency and exchanged power deviation in two-area power system with considering governor saturation limit. In a FOPID controller derivative and integrator parts have non-integer orders which should be determined by designer. FOPID controller has more flexibility than PID controller. The GBMO algorithm is a recently introduced search method that has suitable accuracy and convergence rate. Thus, this paper uses the advantages of FOPID controller as well as GBMO algorithm to solve load–frequency control. However, computational load will higher than conventional controllers due to more complexity of design procedure. Also, a GBMO based fuzzy controller is designed and analyzed in detail. The performance of the proposed controller in time domain and its robustness are verified according to comparison with other controllers like GBMO based fuzzy controller and PI controller that used for load–frequency control system in confronting with model parameters variations.     

Decentralized PID controller for TITO systems using characteristic ratio assignment with an experimental application

This paper presents a decentralized PID controller design method for two input two output (TITO) systems with time delay using characteristic ratio assignment (CRA) method. The ability of CRA method to design controller for desired transient response has been explored for TITO systems. The design methodology uses an ideal decoupler to reduce the interaction. Each decoupled subsystem is reduced to first order plus dead time (FOPDT) model to design independent diagonal controllers. Based on specified overshoot and settling time, the controller parameters are computed using CRA method. To verify performance of the proposed controller, two benchmark simulation examples are presented. To demonstrate applicability of the proposed controller, experimentation is performed on real life interacting coupled tank level system.     

Predictive position and force control for shape memory alloy cylinders

In this paper, a linear lightweight electric cylinder constructed using shape memory alloy (SMA) is proposed. Spring SMA is used as the actuator to control the position and force of the cylinder rod. The model predictive control algorithm is investigated to compensate SMA hysteresis phenomenon and control the cylinder. In the predictive algorithm, the future output of the cylinder is computed based on the cylinder model, and the control signal is computed to minimize the error and power criterion. The cylinder model parameters are estimated by an online identification algorithm. Experimental results show that the SMA cylinder is able to precisely control position and force by using the predictive control strategy though the hysteresis effect existing in the actuator. The performance of the proposed controller is compared with that of a conventional PID controller.     

快速刀具伺服分数阶PID控制仿真的研究

利用分数阶PID控制,提出了一种新的快速刀具伺服(FTS)跟踪控制方法,以改善FTS的控制性能。根据差分进化算法,讨论了分数阶PID控制器的参数整定;通过数值仿真,考察了该方法的可行性和有效性。针对FTS的轨迹跟踪,根据响应时间、跟踪精度等指标,详细比较了分数阶PID控制与传统PID控制的性能。仿真结果表明,分数阶PID控制器的阶跃响应时间约为5×10-7s,是PID控制响应时间的42%,对频率为1 kHz,幅值为1μm的正弦信号的跟踪误差约为6 nm,是PID跟踪误差的50%,验证了基于分数阶PID控制器实现FTS轨迹跟踪控制的可行性和优越性。     

FOA-PID控制器在悬臂式掘进机截割头升降控制系统中的应用

为提高悬臂式掘进机截割头升降控制精度,在建立系统数学模型基础上,分别采用FA算法和FOA算法对PID参数进行优化,对FA-PID控制器和FOA-PID控制器控制性能进行仿真分析,并为了验证两种控制器实际控制性能进行了实验研究。得出结论:对于幅值为1 mm 的阶跃信号以及不同频率的正弦信号,FOA-PID控制器响应性能均优于FA-PID控制器;FOA-PID控制器更能满足悬臂式掘进机对于截割头升降控制精度的要求。     

陀螺稳定平台扰动的自抗扰及其滤波控制

分析了影响陀螺稳定平台隔离控制精度的主要因素,包括被控系统模型中的未建模部分、状态的随机扰动以及输出信号的测量噪声等。研究了综合解决各方面影响因素的控制方案以进一步提高陀螺稳定平台隔离精度。针对上述影响因素,设计一个两步控制策略。第一步,利用自抗扰对系统中未建模部分进行观测及其前向补偿,将自抗扰控制中的反馈控制设计为PID控制,以实现抗平台扰动的调节控制;第二步,利用Kalman滤波器对系统中的状态扰动及测量噪声进行滤波消除。详细描述了提出的控制策略并对其性能进行了系统仿真实验及参数优化。结果表明,该方案在幅值为3°、频率为1/6Hz的载体扰动下能达到4.61%的隔离度,与非线性摩擦力建模辨识及其前向补偿策略控制实际陀螺稳定平台达到的隔离度的最好值9.39%相比,文中提出的控制隔离性能提高了50.9%,具有更高的实用价值。     

基于FPGA的模糊自整定PID控制器的研究

提出了一种基于VHDL描述、FPGA实现的模糊自整定PID控制器设计方法。首先，借助Matlab系统仿真工具，优化得出模糊自整定PID参数的模糊推理规则和控制器算法结构。然后，进行控制器的VHDL分层设汁，作为单一控制器芯片，重点编程实现和时序仿真：模糊逻辑推理、模糊自整定PID算法、数据缓存和I／O接口控制。最后，在一个具体的FPGA芯片上实现该控制器，并在此基础上进行系统实验。实验结果表明：FPGA作为单一控制器实现模糊自整定PID控制编程规范、时序验证方便、系统修改灵活，且基本无须改动硬件，是实现单片或小系统智能控制策略的一种新的有效途径。     

Nonlinear model predictive control of managed pressure drilling

A new design of nonlinear model predictive controller (NMPC) is proposed for managed pressure drilling (MPD) system. The NMPC is based on output feedback control architecture and employs offset-free formulation proposed in [1]. NMPC uses active set method for computing control inputs. The controller implements an automatic switching from constant bottom hole pressure (CBHP) regulation to flow control mode in the event of a reservoir kick. In the flow control mode the controller automatically raises the bottom hole pressure setpoint, and thereby keeps the reservoir fluid flow to the surface within a tunable threshold. This is achieved by exploiting constraint handling capability of NMPC. In addition to kick mitigation the controller demonstrated good performance in containing the bottom hole pressure (BHP) during the pipe connection sequence. The controller also delivered satisfactory performance in the presence of measurement noise and uncertainty in the system.     

Development of a GPC-based PID controller for unstable systems with deadtime

Till now, traditional low-order control schemes have never been applicable to unstable systems with deadtime. In this paper, we present the first application of a PID controller with time-scheduled gains to unstable systems with deadtime, consisting of a single unstable pole. The control gains are designed based on a generalised predictive control (GPC) approach. The only user specifications required are simple and classical desired properties as in the natural frequency and the damping ratio of the closed-loop system. An approach is further developed to subsequent on-line self-tuning of control weights so that the overall control system remains applicable and effective in the face of disturbances and slowly varying dynamics. A detailed analysis of the closed-loop stability of the thus designed control system is further provided in the paper. Based on stability conditions developed, the prediction horizon for the GPC-based controller may be effectively computed. Finally, simulation examples illustrate the performance of the control system.     

A new approach to design of a dynamic output feedback stabilizing control law for LTI systems

We present a new state-space approach to construct a dynamic output feedback controller which stabilizes a class of linear time invariant systems All the states of the given system are not measurable and only the output is used to design the stabilizing control law In the design scheme, however, we first assume that the given system can be stabilized by a feedback law composed of the output and its derivatives of a certain order Beginning with this assumption, we systematically construct a dynamic system which removes the need of the derivatives The mam advantage of the proposed controller is regarding the controller order, which may be smaller than that of conventional output feedback controller Using a simple numerical example, it is shown that the order of the proposed controller is indeed smaller than that of reduced-order observer based output feedback controller     

Robust MPC-based current controller against grid impedance variations for single-phase grid-connected inverters

Recently, LCL filters have been widely used in the output of single phase inverters. Since, the grid side inductor in these filters is in series with the grid impedance at the Point of Common Coupling (PCC), it may create new resonances. This phenomena may take the control loop toward instability. In this case, in order to have a reliable operation, the current controller should be insensitive to the grid impedance variation. In order to damp these resonances, researchers have presented some methods using active or passive damping. These methods added an extra loop to the control loop, an extra passive component in the filter or extra sensor in the control process. But in most of them, the complexity and the cost of controller have been increased. Therefore, presenting a simple control method without extra sensor, passive component or extra arrangement can be a promising approach. This paper presents an MPC-based current controller, which is simple and robust against the grid impedance variation and even the variation of the LCL filter parameters. In contrast to classical multi-loop controller like Proportional-Resonant (PR) controllers, the proposed control method does not need any parameter tuning. In the proposed controller, the switching plan and duty cycles are determined by a cost function and a switching table. Therefore, at the same time with any variation in grid impedance, the proposed controller changes the next switching state and duty cycle. Operating performance like look-up table, searching in all possible switching states to find the best state for the next switching period, makes the controller adaptive and robust against the variation of LCL filter parameters. In order to confirm the effectiveness of the proposed controller, simulations and experimental results of the proposed controller are compared with a classical PR controller.