Nonlinear predictive controller based on S-PARAFAC Volterra models applied to a communicating two-tank system

This paper proposes a new predictive controller approach for nonlinear process based on a reduced complexity homogeneous, quadratic discrete-time Volterra model called quadratic S-PARAFAC Volterra model. The proposed model is yielded by using the symmetry property of the Volterra kernels and their tensor decomposition using the PARAFAC technique that provides a parametric reduction compared to the conventional Volterra model. This property allows synthesising a new nonlinear-model-based predictive control (NMBPC). We develop the general form of a new predictor, and therefore, we propose an optimisation algorithm formulated as a quadratic programming under linear and nonlinear constraints. The performances of the proposed quadratic S-PARAFAC Volterra model and the developed NMBPC algorithm are illustrated on a numerical simulation and validated on a benchmark as a continuous stirred-tank reactor system. Moreover, the efficiency of the proposed quadratic S-PARAFAC Volterra model and the NMBPC approach are validated on an experimental communicating two-tank system.     

基于神经网络和多模型的非线性离散自适应控制

针对一类非线性离散时间单变量系统,提出了基于多模型切换策略的非线性自适应控制方法.首先将被控系统划分为多个工作区间,然后在每个工作区间内建立1个线性自适应控制器和1个非线性神经网络自适应控制器.线性控制器可以保证系统的稳定性,神经网络非线性控制器可以有效的改善系统的暂态性能,采用有效的切换策略可以在保证系统稳定的情况下很好的改善系统的性能.仿真结果验证了所提出方法的有效性.     

基于性能诊断的预测控制器参数整定策略

针对多变量模型预测控制系统在长期运行中出现设定值变化、模型失配、扰动特性变化后控制器参数不再匹配的问题,提出一种在得到性能诊断结果后基于改进粒子群算法的控制器参数整定方法.通过分析最优控制律与三项系统性能的关系,构造出对应的目标优化函数,对粒子群算法迭代过程中粒子的位置和惯性因子做出改进,弥补该算法易于陷入局部最优以及...     

基于最大-最小蚂蚁系统的PID参数整定

PID参数是影响PID控制器控制效果的重要参数。本文提出一种基于最大-最小蚂蚁系统(MMAS)进行PID参数整定的新型算法MPID,并给出了MPID算法的具体实现步骤。实验仿真表明,MPID算法与基于遗传算法、基本蚁群算法的PID整定方法相比,优化效果有明显改善,说明了该算法的可行性和优越性。     

PID-type fuzzy logic controller tuning based on particle swarm optimization

In this paper, a new PID-type fuzzy logic controller (FLC) tuning strategy is proposed using a particle swarm optimization (PSO) approach. In order to improve further the performance and robustness properties of the proposed PID-fuzzy approach, two self-tuning mechanisms are introduced. The scaling factors tuning problem of these PID-type FLC structures is formulated and systematically resolved, using a proposed constrained PSO algorithm. The case of an electrical DC drive benchmark is investigated, within a developed real-time framework, to illustrate the efficiency and superiority of the proposed PSO-based fuzzy control approaches. Simulation and experimental results show the advantages of the designed PSO-tuned PID-type FLC structures in terms of efficiency and robustness.     

Robust PID controller tuning based on the heuristic Kalman algorithm

This paper presents a simple but effective tuning strategy for robust PID controllers satisfying multiple performance criteria. Finding such a controller is known to be computationally intractable via the conventional techniques. This is mainly due to the non-convexity of the resulting control problem which is of the fixed order/structure type. To solve this kind of control problem easily and directly, without using any complicated mathematical manipulations and without using too many “user defined” parameters, we utilize the heuristic Kalman algorithm (HKA) for the resolution of the underlying constrained non-convex optimization problem. The resulting tuning strategy is applicable both to stable and unstable systems, without any limitation concerning the order of the process to be controlled. Various numerical studies are conducted to demonstrate the validity of the proposed tuning procedure. Comparisons with previously published works are also given.     

Robust PID controller tuning based on the constrained particle swarm optimization

This paper proposes a novel tuning strategy for robust proportional-integral-derivative (PID) controllers based on the augmented Lagrangian particle swarm optimization (ALPSO). First, the problem of PID controller tuning satisfying multiple H_∞ performance criteria is considered, which is known to suffer from computational intractability and conservatism when any existing method is adopted. In order to give some remedy to such a design problem without using any complicated manipulations, the ALPSO based robust gain tuning scheme for PID controllers is introduced. It does not need any conservative assumption unlike the conventional methods, and often enables us to find the desired PID gains just by solving the constrained optimization problem in a straightforward way. However, it is difficult to guarantee its effectiveness in a theoretical way, because PSO is essentially a stochastic approach. Therefore, it is evaluated by several simulation examples, which demonstrate that the proposed approach works well to obtain PID controller parameters satisfying the multiple H_∞ performance criteria.     

Adaptive stabilization using a nonlinear time-varying controller

Byrnes et al. (1986) showed that there is no smooth, finite-dimensional, nonlinear time-invariant (NLTI) controller which asymptotically stabilizes every finite-dimensional, stabilizable and detectable, linear time-invariant (LTI) plant (with a fixed number of inputs and outputs). Here we construct a finite-dimensional nonlinear time-varying (NLTV) controller which does exactly that; we treat both the discrete-time and continuous-time cases. With p equal to one in the discrete-time case and the number of plant outputs in the continuous-time case, we first show that for every stabilizable and detectable plant, there exists a p-dimensional linear time-varying (LTV) compensator which provides exponential stabilization; we then construct a (p+1)-dimensional NLTV controller which asymptotically stabilizes every admissible plant by switching between a countable number of such LTV compensators     

Comparative study on linearized robot models

For representing the input—output behaviour of a robot manipulator by a linear time-invariant model, four direct linearization schemes are: (i) state linearization, (ii) linearization based on an identification method, (iii) linearization based on neglecting velocity-dependent and gravity terms and (iv) linearization based on neglecting the velocity-dependent term only (rate linearization). In order to make an appropriate choice of linear model for the development of real-time control, these schemes are extensively studied in this paper. It is shown that the rate linearization method leads to a satisfactory tradeoff between computation, accuracy, and stability. In the case of high velocity motions, a combination of state linearization and rate linearization is proposed.     

基于模糊性能指标的广义预测控制器参数调整

针对广义预测控制算法在控制时域中求得的M个控制量,利用模糊模拟技术对系统的约束进行检验,不断修正目标函数中控制量的加权系数,充分利用系统预测控制量的信息,增强系统的鲁棒性,并满足系统的约束。     

基于NEI调节机制的非线性智能优化控制器

基于神经内分泌系统的整体调节机制,提出一种非线性优化智能控制器.该控制器由提呈单元、抗体控制单元、主控单元、优化单元和辨识单元组成.提呈单元根据免疫提呈机制对控制偏差进行动态处理,抗体控制单元通过调整控制实体的数量来消除控制偏差,主控单元调整提呈单元和抗体控制单元的控制作用,优化单元和辨识单元优化提呈单元和抗体控制单元的参数.仿真结果表明,相对于传统的PID控制,该智能控制器具有较好的控制性能.     

Nonlinear controller construction based on a model with state-dependent representation for a nonlinear system

This paper addresses a method of nonlinear controller construction based on a model with a state-dependent representation for a nonlinear system. In this method, the controller construction and generation of manipulated values are separated. A nonlinear system and its controller are firstly expressed by the coefficients of the state-dependent representation without any approximation. At the stage of controller implementation for the nonlinear system, the manipulated values are calculated by means of an algorithm of the numerical analysis. The properties of the proposed method are analysed. With the analytical considerations and simulation studies, the proposed method is compared with several nonlinear control methods, such as exact linearization method and the linear approximation method, and its merits are verified.     

基于神经网络模型的非线性多步预测学习控制器

构造出一种建模网络,通过对它的学习来辨识过程动态,通过对广义预测控制目标函数的在线优化求得控制律.仿真结果验证了该算法的有效性.     

A self-learning and tuning fuzzy logic controller based on genetic algorithms and reinforcements

This article presents a new method for learning and tuning a fuzzy logic controller automatically. A reinforcement learning and a genetic algorithm are used in conjunction with a multilayer neural network model of a fuzzy logic controller, which can automatically generate the fuzzy control rules and refine the membership functions at the same time to optimize the final system's performance. In particular, the self-learning and tuning fuzzy logic controller based on genetic algorithms and reinforcement learning architecture, which is called a Stretched Genetic Reinforcement Fuzzy Logic Controller (SGRFLC), proposed here, can also learn fuzzy logic control rules even when only weak information, such as a binary target of “success” or “failure” signal, is available. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. It is shown that the system can solve a fairly difficult control learning problem more concretely, the task is a cart–pole balancing system, in which a pole is hinged to a movable cart to which a continuously variable control force is applied. © 1997 John Wiley & Sons, Inc.     

Study of predictive controller tuning methods

Several tuning guidelines for model predictive control proposed in literature have been converted into suitable tuning rules and investigated with the help of simulations of two typical transfer functions and a nonlinear unstable chemical reactor as well as a real-time laboratory turbogenerator control application. A modified version of a supervisory performance tuning procedure has also been applied to explore the possibility of application of auto-tuning in model predictive control. A new tuning procedure is finally presented on the basis of the results obtained using several previously existing tuning guidelines.     

Stochastic tuning of a spacecraft controller using neural networks

There have been many demonstrations of the advantages of using neural networks in control systems. Networks, such as the MLP, offer a level of adaptability and non-linearity, both of which are required in some control systems. However, for spacecraft attitude control, high levels of dependability are also required. This poses serious questions for the acceptability of neural networks. This paper describes a suggested control system which uses two MLP networks for the control of thrusters on the SOHO spacecraft. However, rather than applying the networks directly, they form part of a stochastic parameter-selection system which is used to adapt a conventional (PD) control system. It is suggested that using neural networks indirectly in this way better guarantees the dependability/reliability of the control system.     

On a linearized model and adaptive controller implementation for manipulator motion

The detailed model for the motion of a manipulator system is linearized along a specific path. An on-line parameter estimation algorithm based on the least-squares criterion is used to determine the parameter values along the path at the operating instances. Based on the estimated parameter values, a self-tuning adaptive controller is designed by minimizing a chosen performance criterion. Simulation results as well as experimental results are presented to demonstrate the approach. Certain aspects of the implementation are discussed.     

Nonlinear discrete-time controller based on fuzzy-rule emulated network and shuttering condition

This article introduces the adaptive controller for a class of nonlinear discrete-time systems based on the sliding shuttering condition and the self adjustable network called Multi-Input Fuzzy Rules Emulated Network (MIFREN). By using only the online learning phase, MIFREN’s functional is the nonlinear discrete-tine function approximation and the disturbance estimation together. The proposed theorem is introduced for the designing procedure of all controller’s parameters and MIFREN’s adaptation gain. Simulation results demonstrate the justification of the theorem for the tracking performance and the unknown disturbance rejection.     

Robustness of a discrete-time predictor-based controller for time-varying measurement delay

A predictor-based controller for time-varying delay systems is presented in this paper and its robustness properties for different uncertainties are analyzed. First, a time-varying delay dependent stability condition is expressed in terms of LMIs. Then, uncertainties in the knowledge of all plant-model parameters are considered and the resulting closed-loop system is shown to be robust with respect to these uncertainties. A significant improvement with respect to the same control strategy without predictor is achieved. The scheme is applicable to open-loop unstable plants and it has been tested in a real-time application to control the roll angle of a quad-rotor helicopter prototype. The experimental results show good performance and robustness of the proposed scheme even in the presence of long delay uncertainties.     

Adaptive sliding mode controller design based on T-S fuzzy system models

An adaptive sliding mode control (ASMC) technique based on T-S fuzzy system models is proposed in this paper for a class of perturbed nonlinear MIMO dynamic systems in order to solve tracking problems. A T-S fuzzy model is firstly formed by utilizing fuzzy theorem to amalgamate a set of linearized dynamic equations. The adaptive sliding mode controller is then designed based on this fuzzy model with perturbations. The proposed control scheme can drive the dynamics of controlled system into a designated sliding surface in finite time, and guarantee the property of asymptotical stability. It is also shown that the information of upper bound of modeling errors as well as perturbations, except the information of upper bound of input uncertainty, is not required when using the proposed controller.