Dissipative-based adaptive neural control for nonlinear systems

A dissipative-based adaptive neural control scheme was developed for a class of nonlinear uncertain systems with unknown nonlinearities that might not be linearly parameterized. The major advantage of the present work was to relax the requirement of matching condition, i.e., the unknown nonlinearities appear on the same equation as the control input in a state-space representation, which was required in most of the available neural network controllers. By synthesizing a state-feedback neural controller to make the closed-loop system dissipative with respect to a quadratic supply rate, the developed control scheme guarantees that the L2-gain of controlled system was less than or equal to a prescribed level. And then, it is shown that the output tracking error is uniformly ultimate bounded. The design scheme is illustrated using a numerical simulation.     

Adaptive practical output tracking of a class of nonlinear systems

Focus is hid on the adaptive practical output-tracking problem of a chss of nonlinear systems with high-order lower-triangular structure and uncontrollable unstable linearization. Using the modified adaptive addition of a power integrator technique as a basic tool, a new smooth adaptive state feedback controller is designed. This controller can ensure all signals of the closed-loop systems are globally bounded and output tracking error is arbitrary small.     

Robust H ∞ fuzzy control of nonlinear systems with multiple time delays

A robustness design of fuzzy control via model-based approach is proposed in this article to overcome the effect of approximation error between multiple time-delay nonlinear systems and Takagi--Sugeno (T-S) fuzzy models. A stability criterion is derived based on Lyapunov's direct method to ensure the stability of nonlinear multiple time-delay systems especially for the resonant and chaotic systems. Positive definite matrices P and R_k of the criterion are obtained by using linear matrix inequality (LMI) optimization algorithms to solve the robust fuzzy control problem. In terms of the control scheme and this criterion, a fuzzy controller is then designed via the technique of parallel distributed compensation (PDC) to stabilize the nonlinear multiple time-delay system and the H ^∞ control performance is achieved at the same time. Finally, two numerical examples of the chaotic and resonant systems are demonstrated to show the concepts of the proposed approach.     

Robust H ∞ control for a class of uncertain mechanical systems

In this article, the problem of H _∞ control is investigated for a class of mechanical systems with input delay and parameter uncertainties which appear in all the mass, damping and stiffness matrices. Two approaches, norm-bounded and linear fractional transformation (LFT) uncertainty formulations, are considered. By using a new Lyapunov–Krasovskii functional approach, combined with the advanced techniques for achieving delay dependence, improved robust H _∞ state-feedback controller design methods are developed. The existence condition for admissible controllers is formulated in the form of linear matrix inequalities (LMIs), and the controller design is cast into a convex optimisation problem subject to LMI constraints. If the optimisation problem is solvable, a desired controller can be readily constructed. The result for the norm-bounded uncertainty case improves the existing ones in terms of design conservatism, and that for the LFT uncertainty case represents the first attempt in this direction. An illustrative example is provided to show the effectiveness and advantage of the proposed controller design methodologies.     

Decentralised indirect adaptive output feedback fuzzy H ∞ tracking design for a class of large-scale nonlinear systems

A novel decentralised indirect adaptive output feedback fuzzy controller with a compensation controller and an H _∞ tracking controller is presented for a class of uncertain large-scale nonlinear systems in this article. The compensator adaptively compensates for interconnections between subsystems as well as mismatched errors, while the H _∞ controller suppresses the effect of external disturbances. Based upon the combination of fuzzy inference systems, a state observer, H _∞ tracking technique and the strictly positive real condition, the proposed overall observer-based decentralised algorithm guarantees not only asymptotical tracking of reference trajectories but also an arbitrary small attenuation level of the unmodelled error dynamics including the disturbances on the tracking control. Simulation results substantiate the effectiveness of the proposed scheme.     

Robust adaptive tracking control of MIMO nonlinear systems in the presence of actuator hysteresis

Adaptive tracking control of a class of MIMO nonlinear system preceded by unknown hysteresis is investigated. Based on dynamic surface control, an adaptive robust control law is developed and compensators are designed to mitigate the influences of both the unknown bounded external uncertainties and the unknown Prandtl–Islinskii hysteresis. By adopting the low-pass filters, the explosion of complexity caused by tedious computation of the time derivatives of the virtual control laws is overcome. With the proposed control scheme, the closed-loop system is proved to be semi-globally ultimately bounded by the Lyapunov stability theory, and the output of the controlled system can track the desired trajectories with an arbitrarily small error. Finally, numerical simulations are given to verify the effectiveness of the proposed approach.     

Robust sampled-data H ∞ output tracking control for a class of nonlinear networked systems with stochastic sampling

In this article, the problem of robust sampled-data H _∞ output tracking control is investigated for a class of nonlinear networked systems with stochastic sampling and time-varying norm-bounded uncertainties. For the sake of technical simplicity, only two different sampling periods are considered, their occurrence probabilities are given constants and satisfy Bernoulli distribution, and can be extended to the case with multiple stochastic sampling periods. By the way of an input delay, the probabilistic system is transformed into a stochastic continuous time-delay system. A new linear matrix inequality-based procedure is proposed for designing state-feedback controllers, which would guarantee that the closed-loop networked system with stochastic sampling tracks the output of a given reference model well in the sense of H _∞. Conservatism is reduced by taking the probability into account. Both network-induced delays and packet dropouts have been considered. Finally, an illustrative example is given to show the usefulness and effectiveness of the proposed H _∞ output tracking design.     

Suboptimal adaptive control of a class of non-linear systems†

The problem of controlling a class of nonlinear systems with uncertain parameters and imprecisely defined system and measurement noise terms is considered. A sub-optimal adaptive control algorithm is developed as a solution to coupled linear modelling and control optimization problems. Features of the algorithm are the forward-recursive generation of the modelling and control processes and necessity for past and present values of states and parameters alone in order to implement the algorithm (i.e. no precomputed nominal trajectory is required). These features make the algorithm ideally suited to real-time control applications

Real-time control of a non-trivial fifth-order system, (a model of a plasma torch furnace) is considered and the results of an experimental study are presented.     

On nonlinear H ∞ sliding mode control for a class of nonlinear cascade systems

In this work two main robust control strategies, the sliding mode control (SMC) and nonlinear H ^∞ control, are integrated to function in a complementary manner for tracking control tasks. The SMC handles matched L ^∞[0,∞) type system uncertainties with known bounding functions. H ^∞ control deals with unmatched disturbances of L ₂[0,∞) type where the upper-bound knowledge is not available. The new control method is designed for a class of nonlinear uncertain systems with two cascade subsystems. Nonlinear H ^∞ control is applied to the first subsystem in the presence of unmatched disturbances. Through solving a Hamilton-Jacoby inequality, the nonlinear H ^∞ control law for the first subsystem well defines a nonlinear switching surface. By virtue of nonlinear H ^∞ control, the resulting sliding manifold in the sliding phase possesses the desired L ₂ gain property and to a certain extend the optimality. Associated with the new switching surface, the SMC is applied to the second subsystem to accomplish the tracking task, and ensure the L ₂ gain robustness in the reaching phase. Two illustrative examples are given to show the effectiveness of the proposed robust control scheme.     

模糊免疫PID算法在温度控制系统中的应用研究

该文针对温度控制系统非线性、大滞后、参数时变的特征,设计了模糊免疫PID控制器。该控制器结合了模糊逻辑、免疫机理以及PID调节的各种优点,既具有模糊控制的非线性作用,又具有免疫控制的自适应能力,同时还具有PID控制的广泛适用性。文章介绍了模糊免疫PID控制器的控制原理和设计方法,在Matlab中编写函数仿真,结果表明该控制器能够实现持续干扰情况下的闭环鲁棒稳定,并使系统呈现良好的动态和静态性能。     

模糊PID控制器在聚丙烯生产中的应用

以聚丙烯生产为工业背景,针对聚丙烯的生产过程是一个大滞后、时变、非线性的复杂系统,普通PID控制器有参数整定不良,性能欠佳,对运行工矿的适应性很差等方面的问题,本文提出了模糊加PID的控制方案,并结合聚合反应控制的特点,提出了聚合反应温度的模糊加PID控制的方案,并取得了较好的仿真结果。     

一种模糊PID控制在热工对象中的应用

本文研究一种新型模糊PD控制器在一般热工对象中的应用问题。由于热工对象存在参数时变的问题,因此本文采用仿真方法针对对象参数时变的情况进行了控制器的鲁棒性分析。针对多变量输入,模糊控制规则多而控制器不易实现这一问题,本文提供了一种结构简单、性能优良的一维模糊控制器与PD控制相结合的控制策略。这种控制结构很好的解决了模糊控制不跟踪输入的问题。     

Robust Fuzzy Output Sliding Control without the Requirement of State Measurement

A robust fuzzy output sliding control for nonlinear robotic arms is proposed in this paper. The proposed method not only retains the advantages of the conventional sliding mode control such as robustness against parameter variations and external disturbances, but also uses measurable output signals to define the sliding surface function. A fuzzy controller is developed to modify the control law to avoid state measurement. Control system stability is proved by using the Lyapunov stability theorem. The system robustness is guaranteed. Simulations results demonstrate the validity and effectiveness of the proposed method for uncertain nonlinear robotic arms.     

一类大系统的间接自适应分散模糊控制

针对一类未知非线性大系统,将模糊控制、模糊逻辑系统及滑模控制相结合,提出了一种间接自适应模糊控制策略,仿真结果证明了所提出的算法是有效的.     

Adaptive fuzzy control for pure-feedback stochastic nonlinear systems with unknown dead zone outputs

This paper focuses on an adaptive fuzzy tracking control problem for a class of pure-feedback stochastic nonlinear systems with unknown dead zone outputs. To overcome the design difficulty arising from the nonlinearity in the output mechanism, the new properties of Nussbaum function are employed and an auxiliary virtual controller is constructed. The proposed adaptive fuzzy control method guarantees that all the signals in the closed-loop system are bounded in probability and the tracking error converges to a small neighbourhood of the origin in the sense of mean quartic value. Simulation results further demonstrate the effectiveness of the presented control algorithm.     

Sugeno模糊模型的辨识与控制

提出了一种新的Sugeno模糊模型辨识算法和对非线性系统进行并行化设计的方法.在Sugeno模糊模型辨识中,应用模糊聚类方法可将其前提结构和结论参数的辨识分开进行,减少了计算量;对于非线性系统的控制,Sugeno模糊模型实际上是动态系统的局部线性化,可采用并行设计的方法设计控制器,然后通过模糊推理得到全局控制量.最后通过倒立摆系统的控制说明了本文算法的有效性.     

双层模糊系统融合中心约束型最小包含球

与传统的TSK模糊系统相比,改进的双层TSK模糊系统CTSK(Central TSK Fuzzy System)有如下优点:良好的可解释性、更好的鲁棒性、较强的逼近能力。但对于大样本或超大样本数据集,其时间复杂度和空间复杂度的开销都极大地限制了它的实用性。针对此不足,通过模糊系统融合中心约束型最小包含球(CCMEB)理论提出了CCMEB-CTSK(CCMEB-based CTSK)算法。该算法在继承CTSK优点的同时,又较好地实现了处理大样本和超大样本数据集的有效性和快速性。仿真实验研究分析了采用不同模糊规则数的CCMEB-CTSK的性能指标和运行时间的比较,以及训练样本不加噪声和加入噪声情况下CCMEB-CTSK泛化能力和鲁棒性能的测试。