A Wavelet Neural Network Based Non-linear Model Predictive Controller for a Multi-variable Coupled Tank System

In this paper, a novel real time non-linear model predictive controller(NMPC) for a multi-variable coupled tank system(CTS) is designed. CTSs are highly non-linear and can be found in many industrial process applications. The involvement of multi-input multi-output(MIMO) system makes the design of an effective controller a challenging task. MIMO systems have inherent couplings,interactions in-between the process input-output variables and generally have an complex internal structure. The aim of this paper is to design, simulate, and implement a novel real time constrained NMPC for a multi-variable CTS with the aid of intelligent system techniques. There are two major formidable challenges hindering the success of the implementation of a NMPC strategy in the MIMO case. The first is the difficulty of obtaining a good non-linear model by training a non-convex complex network to avoid being trapped in a local minimum solution. The second is the online real time optimisation(RTO) of the manipulated variable at every sampling time.A novel wavelet neural network(WNN) with high predicting precision and time-frequency localisation characteristic was selected for an MIMO model and a fast stochastic wavelet gradient algorithm was used for initial training of the network. Furthermore, a genetic algorithm was used to obtain the optimised parameters of the WNN as well as the RTO during the NMPC strategy. The proposed strategy performed well in both simulation and real time on an MIMO CTS. The results indicated that WNN provided better trajectory regulation with less mean-squared-error and average control energy compared to an artificial neural network. It is also shown that the WNN is more robust during abnormal operating conditions.     

Improved identification and autotuning of PI controllers for MIMO processes by relay techniques

A technique for on-line identification and tuning is proposed to be used in the framework of a MIMO autotuning procedure. The proposed technique does not suffer from the risks of instability and the lack in performance of common tuning techniques in MIMO autotuning. Identification is accomplished through an extension of the well known ATV autotune identification method and requires only few additional tests in order to obtain some more knowledge about the process. The resulting model, which describes with good precision the process in a region of frequencies around the critical point, is then used for tuning: the integral time is found as a function of the model time constants and delay, while the gain is computed in order to give a desired value of the closed-loop resonance peak. Examples of application show that advantages over other proposed techniques can be retained for processes having different dynamic characteristics.     

Hybrid real coded genetic algorithm solution to economic dispatch problem

This paper presents a new, two-phase hybrid real coded genetic algorithm (GA) based technique to solve economic dispatch (ED) problem with multiple fuel options. The proposed hybrid scheme is developed in such a way that a simple real coded GA is acting as a base level search, which makes a quick decision to direct the search towards the optimal region, and local optimization by direct search and systematic reduction in size of the search region method is next employed to do the fine tuning. Constraint satisfaction technique has been employed to improve the solution quality and reduce the computational expenses. In order to validate the effectiveness of the proposed hybrid real coded genetic algorithm, the result of 10-generation unit ED problem with multiple fuel options is considered. The result shows that the proposed hybrid algorithm not only improves the solution accuracy and reliability but also makes the algorithm more efficient in terms of number of function evaluations and computation time. The simulation study clearly demonstrates that the proposed hybrid real coded genetic algorithm is practical and valid for real-time applications.     

多变量系统的PID 控制器设计

针对多输入多输出系统,提出一种基于微粒群算法的灰色系统预测PID 控制算法．该方案将多 输入多输出系统转化成若干个多输入单输出子系统,使用多变量一阶灰色模型GM(1;N) 预测出每个子系统的 输出,实现了多输入多输出系统的对角解耦．然后用微粒群算法优化PID 控制器的参数,用优化后的PID 控 制器对每个子系统进行控制．对两个典型的多变量控制过程进行了仿真,结果表明,这种控制方法超调量较 小,鲁棒性强,响应速度快,抗干扰能力强.     

Thermal MIMO controller for setpoint regulation and load disturbance rejection

The thermal interaction problem of a multi-input multi-output (MIMO) temperature process is caused by the temperature gradient through the plant. Generally, industrial controllers are used for the decentralized control and do not use a process mathematical model. In many cases, trial-and-error tuning is required for solving the problem. The present paper introduces a MIMO thermal model and the decoupling PID control strategy. The decoupling control and its shortcomings are described from the perspective of the user. An improved strategy based on a robust observer is developed. For industrial implementation, this observer is quite simple because only the first-order model given by the decoupling model is implemented. The program code is simple for the microprocessor of a low-cost commercialized controller. In addition, a hunting rejection method based on the observer is presented. The present paper applies the method to the twin screw extruder in order to verify the effectiveness of the proposed strategy. The proposed method has been successfully to solve the interaction problem for MIMO temperature process.     

Tuning an adaptive controller using a robust control approach

This article proposes a new technique for the tuning of a discrete adaptive controller that is designed based on Lyapunov stability concepts. The tuning is based on the minimisation of a performance index that can be calculated from a generalised eigenvalue problem (GEVP) using LMI's (Linear Matrix Inequalities). The proposed technique results in an adaptive controller with time-varying tuning gains. The solution is based on an approximation of the optimal dual adaptive control problem. The tuning technique was used to perform on-line control of a first-order system and an isothermal and a non-isothermal CSTR. The results show that the proposed approach provides better performance than an adaptive algorithm with the same structure, but with constant adaptation gains. Also, the proposed algorithm is shown to be superior to an adaptive controller based on a Recursive Least Squares (RLS) estimator during sudden changes in model parameters.     

New optimal controller tuning method for an AVR system using a simplified Ant Colony Optimization with a new constrained Nelder–Mead algorithm

In this paper, an optimal gain tuning method for PID controllers is proposed using a novel combination of a simplified Ant Colony Optimization algorithm and Nelder–Mead method (ACO-NM) including a new procedure to constrain NM. To address Proportional-Integral-Derivative (PID) controller tuning for the Automatic Voltage Regulator (AVR) system, this paper presents a meta-analysis of the literature on PID parameter sets solving the AVR problem. The investigation confirms that the proposed ACO-NM obtains better or equivalent PID solutions and exhibits higher computational efficiency than previously published methods. The proposed ACO-NM application is extended to realistic conditions by considering robustness to AVR process parameters, control signal saturation and noisy measurements as well as tuning a two-degree-of-freedom PID controller (2DOF-PID). For this type of PID, a new objective function is also proposed to manage control signal constraints. Finally, real time control experiments confirm the performance of the proposed 2DOF-PIDs in quasi-real conditions. Furthermore, the efficiency of the algorithm is confirmed by comparing its results to other optimization algorithms and NM combinations using benchmark functions.     

Realization of simple adaptive control by using parallel feedforward compensator

A problem is dealt with concerning a practical design scheme for a multi-input/multi-output simple adaptive control (SAC) system. The SAC method which was first derived as a sort of the direct model following adaptive control based on the command generator tracker (CGT) technique was fundamentally applicable to plants satisfying the so-called almost strictly positive real (ASPR) condition. However, the method can be applied to non-ASPR plants by using a parallel feedforward compensator (PFC), which makes the augmented plant including PFC be ASPR. Such a technique was proposed by Bar-Kana for plants satisfying the output feedback stabilizable condition. As an extension, the authors have proposed a simple and concrete PFC design procedure for SISO non-ASPR plants. The special feature of this proposal is that the method can be applicable to non-ASPR plants as the minimum phase system. The main aim of the paper is to give a practical design procedure of PFC for MIMO plants as a natural extension of the SISO case, so that an actual realization of MIMO SAC can be attained for a broad class of MIMO plants. The effectiveness of the proposed method is also confirmed through numerical simulations.     

SISO approaches for linear programming based methods for tuning decentralized PID controllers

Two tuning techniques are proposed to design decentralized PID controllers for weakly coupled and general MIMO systems, respectively. Each SISO loop is designed separately, and the controller parameters are obtained as a solution of a linear programming optimization problem with constraints on the process stability margins. Despite the SISO approach, loop interactions are accounted for either by Gershgorin bands (non-iterative method) or an equivalent open-loop process (iterative method). The tuning results and performance from both methods are illustrated in four simulations of linear processes, and a laboratory-scale application in a Peltier process. Four applications contemplate closed-loop performance comparisons between the proposed techniques and techniques from the literature. One application illustrates the feasibility of the proposed iterative method, based on EOPs, in tuning decentralized PIDs for a 5 × 5 system. Moreover, an analysis of the effect of model uncertainty in the phase and gain margins of the closed-loop process is performed.     

ILC strategy for progress improvement of economic performance in industrial model predictive control systems

A novel approach to progress improvement of the economic performance in model predictive control (MPC) systems is developed. The conventional LQG based economic performance design provides an estimation which cannot be done by the controller while the proposed approach can develop the design performance achievable by the controller. Its optimal performance is achieved by solving economic performance design (EPD) problem and optimizing the MPC performance iteratively in contrast to the original EPD which has nonlinear LQG curve relationship. Based on the current operating data from MPC, EPD is transformed into a linear programming problem. With the iterative learning control (ILC) strategy, EPD is solved at each trial to update the tuning parameter and the designed condition; then MPC is conducted in the condition guided by EPD. The ILC strategy is proposed to adjust the tuning parameter based on the sensitivity analysis. The convergence of EPD by the proposed ILC has also been proved. The strategy can be applied to industry processes to keep enhancing the performance and to obtain the achievable optimal EPD. The performance of the proposed method is illustrated via an SISO numerical system as well as an MIMO industry process.     

Robust capacity maximization transceiver design for MIMO OFDM systems多天线正交频分复用系统中鲁棒性容量最大化的收发机设计

In this paper, we investigated capacity maximization problem for Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing systems with imperfect channel state information (CSI). To the best of our knowledge, the considered problem is still an open problem. However, the transceiver designs for MIMO OFDM systems have been extensively studied. It seems nobody gives closed-form solutions for resource allocation for MIMO OFDM systems with statistical channel estimation errors up to date. In our work, based on practical channel estimation algorithm, the channel estimation errors are first derived and then the robust resource allocation problem has been formulated. The structure of the optimal robust precoder is first derived, based on which the optimization problem will be simplified significantly. Furthermore, based on the Lagrangian dual method, a robust power allocation algorithm is proposed. The proposed power allocation can be considered as a variant of water-filling solution named cluster water-filling solution. Finally, simulation results show that our proposed robust design outperforms the non-robust design in terms of channel capacity.     

Neural‐network‐based predictive controller design: An application to temperature control of a plastic injection molding process

This paper presents a neural‐network‐based predictive control (NPC) method for a class of discrete‐time multi‐input multi‐output (MIMO) systems. A discrete‐time mathematical model using a recurrent neural network (RNN) is constructed and a learning algorithm adopting an adaptive learning rate (ALR) approach is employed to identify the unknown parameters in the recurrent neural network model (RNNM). The NPC controller is derived based on a modified predictive performance criterion, and its convergence is guaranteed by adopting an optimal algorithm with an adaptive optimal rate (AOR) approach. The stability analysis of the overall MIMO control system is well proven by the Lyapunov stability theory. A real‐time control algorithm is proposed which has been implemented using a digital signal processor, TMS320C31 from Texas Instruments. Two examples, including the control of a MIMO nonlinear system and the control of a plastic injection molding process, are used to demonstrate the effectiveness of the proposed strategy. Results from both numerical simulations and experiments show that the proposed method is capable of controlling MIMO systems with satisfactory tracking performance under setpoint and load changes. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Reduction of multiple‐input multiple‐output specific absorption rate synthesized using a fast estimation model

A technique that improves multiple‐input multiple‐output (MIMO) specific absorption rate (SAR) is proposed for multiple‐antenna transmitters. When multiple antennas are implemented, the SAR evaluation is more complex than the stand‐alone counterpart. Techniques for the reduction of stand‐alone SAR cannot be applied to the MIMO scenario straightforwardly. To reduce MIMO SAR and preserve radiation characteristics simultaneously, a technique based on solving the fast estimation model of MIMO SAR is proposed. The proposed technique is cast into two phases. In the phase one, the fast estimation model is constructed for MIMO SAR. This model can predict arbitrary combinations of MIMO SAR once the contribution of the electric field of each antenna is obtained. The capability of the prediction is validated through 19 scenarios. In the phase two, the fast estimation model is formulated as a mathematical programming problem, where the current amplitude of each antenna is treated as decision variables. The combination of the decision variables that leads to the lowest SAR is thus the optimum solution. The proposed technique is tested using a quad‐element antenna system that operates in 3.4 to 3.7 GHz. The results indicate that most of the scenarios depict reduced SAR. The averaging MIMO SAR is improved by 13.75%.     

A robust sampled regulator for stable systems with monotone step responses

It is shown that a discrete time integrating regulator can be designed based on a strongly simplified model. The regulator has only two tuning parameters which are easy to choose from the step response of the open loop system. The problem is a prototype for a general robustness problem.     

Compact wideband MIMO mobile‐antenna system design using mode‐based decoupling techniques

This work focuses on designing small mobile antennas and their multiple‐input multiple‐output (MIMO) applications. In this study, it was investigated that small mobile antennas can obtain high radiation performance only when a tight coupling with the ground plane is generated through ground‐mode tuning (GMT) of the ground plane. Furthermore, a novel mode‐based decoupling concept is presented for their MIMO applications, based on a four‐port network and by considering the ground‐mode effect. Consequently, the proposed mode‐based decouplers can effectively sustain high radiation performance and improve the isolation while providing low correlation by generating diagonally directed radiation patterns. In the proposed MIMO system, both GMT structures and mode‐based decouplers are implemented utilizing the metal rims around the ground plane, thereby occupying very compact clearance, and the measured MIMO antennas fully covered 0.69 to 1 GHz band with high isolation up to 20 dB and low envelope correlation coefficient (ECC) value below 0.5, sufficiently applicable in mobile devices.     

一阶线性自抗扰控制的整定

线性自抗扰控制（linear active disturbance rejection control,LADRC）是解决系统外部不可测扰动和内部未知不确定性的一种新型控制方法。其精髓是将系统的不确定性转化为一个可观测的状态,利用扩张状态观测器进行实时估计,并用状态反馈控制率实时进行补偿。在满足鲁棒度策略和时间乘平方误差积分的约束条件下,首先针对一阶惯性加迟延模型提出了一阶LADRC的整定公式,然后通过典型的基准系统和温度控制实验,对整定公式进行测试,最后与常规的SIMC （simplified internal model control）-PI （proportional-integral）整定方法进行性能比较。仿真结果证明了该一阶LADRC整定公式的可行性,拓展了其在工业控制领域的应用。     

非理想信道估计下的全双工MIMO系统的优化研究

在实际通信环境中,精确信道状态信息很难获取.本文考虑非理想信道估计条件下的全双工MIMO蜂窝系统,该系统不仅受自干扰、同信道干扰的影响,还受到信道估计误差带来的影响.因此有必要对该通信系统进行资源优化,来抵抗多种干扰并提高性能.通过建模一个联合用户配对和功率分配的优化问题来最大化系统速率,由于该联合优化问题是非凸优化问题,基于分解和梯度投影的渐进算法用来求解该问题.仿真结果表明,在非理想信道状态信息条件下,使用本文提出的算法优化后,全双工MIMO蜂窝系统性能优于半双工系统.进一步地,实验分析了不同类型干扰对系统性能的影响,表明信道估计误差对性能影响最大.     

Nonfragile containment control of nonlinear multi-agent systems via a disturbance observer-based approach

In this article, we consider the nonfragile containment control problem of nonlinear multi-agent systems (MASs) with exogenous disturbance where the communication links among agents under consideration is directed. Firstly, based on relative output measurements between the agent and its neighbors, a disturbance observer-based control protocol is proposed to solve the containment control problem of MASs with inherent nonlinear dynamics and exogenous disturbances. Secondly, because of the additional tuning of parameters in the real control systems, uncertainties in the designing of observer and controller gains always occur, and as a result, an output feedback controller with disturbance rejection is conceived and the containment control problem of nonlinear MASs with nonfragility is thoroughly investigated. Then, depending on matrix transformation and inequality technique, sufficient conditions of the designed controller gains exist, which is derived from the asymptotic stability analysis problem of some containment error dynamics of MASs. Finally, two simulation examples are exploited to illustrate the effectiveness of the proposed techniques.     

A frequency reconfigurable MIMO antenna with agile feedline for cognitive radio applications

A novel frequency agile multiple‐input‐multiple‐output (MIMO) patch antenna based on a reconfigurable feedline is proposed. The proposed antenna structure has two hexagonal‐shaped patch antenna elements. A defected ground structure having hexagonal shape is included in the ground plane to make the design compact and improve isolation among antenna elements. Further compactness is achieved using reactive loading. Frequency reconfigurability is realized by employing varactor diodes in the microstrip feedline. The proposed antenna achieves a frequency reconfigurable band with wide tuning range from 1.42 to 2.27 GHz with good gain and efficiency. Furthermore, an envelope correlation coefficient value of less than 0.2 and minimum isolation of 12 dB was achieved, displaying good MIMO performance. The presented antenna has a planar, low profile design with compact size of 100 × 50 mm². Thus, frequency agility, wide range tuning, compactness, and planar structure of the proposed antenna design make it suitable for modern wireless handheld devices particularly in cognitive radio applications.