Using AQM for performance improvement of networked control systems

International Journal of Control, Automation and Systems - This paper presents a new scheme to jointly control the plant and the network to improve the overall performance of the networked control...     

Iterative learning control for constrained linear systems

This article considers iterative learning control (ILC) for linear systems with convex control input constraints. First, the constrained ILC problem is formulated in a novel successive projection framework. Then, based on this projection method, two algorithms are proposed to solve this constrained ILC problem. The results show that, when perfect tracking is possible, both algorithms can achieve perfect tracking. The two algorithms differ, however, in that one algorithm needs much less computation than the other. When perfect tracking is not possible, both algorithms can exhibit a form of practical convergence to a ‘best approximation’. The effect of weighting matrices on the performance of the algorithms is also discussed and finally, numerical simulations are given to demonstrate the effectiveness of the proposed methods.     

Transient stability improvement of multi-machine power systems using on-line fuzzy control of SMES

Fuzzy control of the super-conducting magnetic energy storage (SMES) is proposed here to improve the transient stability and damping requirements of multi-machine power systems. The effects of the SMES control on the line power transfer are considered in the design of the fuzzy controller. A theoretical realisation of the proposed control scheme is presented and a simulation study on the New-England test system is carried out. A practical implementation, based on local measurements, is explained. The improvement of the system’s critical clearing time, and also the system damping requirements, are clearly demonstrated. Finally, a conclusion is provided.     

On improvement of stability conditions for continuous Mamdani-like fuzzy systems

This paper is concerned with continuous fuzzy systems with singleton consequents, called type II fuzzy systems. It first introduces the canonical form of an unforced type II fuzzy system and its stability theorem presented in the previous study. Then, improving the stability theorem, it gives necessary and sufficient conditions such that a type II fuzzy system is stable with respect to a global quadratic Lyapunov function.     

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.     

Hybrid compensation control for affine TSK fuzzy control systems

The paper proposes a way of designing state feedback controllers for affine Takagi-Sugeno-Kang (TSK) fuzzy models. In the approach, by combining two different control design methodologies, the proposed controller is designed to compensate all rules so that the desired control performance can appear in the overall system. Our approach treats all fuzzy rules as variations of a nominal rule and such variations are individually dealt with in a Lyapunov sense. Previous approaches have proposed a similar idea but the variations are dealt with as a whole in a robust control sense. As a consequence, when fuzzy rules are distributed in a wide range, the stability conditions may not be satisfied. In addition, the control performance of the closed-loop system cannot be anticipated in those approaches. Various examples were conducted in our study to demonstrate the effectiveness of the proposed control design approach. All results illustrate good control performances as desired.     

Iterative method for fuzzy equations

In this paper, we propose numerical solution for solving a system of fuzzy nonlinear equations based on Fixed point method. The convergence theorem is proved in detail. In this method the algorithm is illustrated by solving several numerical examples.     

连续互联系统的模糊分散控制及新的稳定性条件

针对一类连续模糊互联系统,提出一种模糊分散控制器的设计方法,并给出了保证控制系统稳定的更为宽松的充分条件.应用Lyapunov函数法和线性矩阵不等式,证明了模糊分散控制系统的稳定性.仿真结果进一步验证了所提出的模糊分散控制方法的有效性.     

Iterative learning control for a class of singular impulsive systems

In this paper, the iterative learning control problem for a class of nonlinear singular impulsive systems is discussed. Then, a D-type (derivative-type) iterative learning control algorithm is presented such that the output tracks the desired output trajectory as accurate as possible. Furthermore, the sufficient condition for the convergence of the proposed algorithm is established in detail. Finally, a numerical example is included to corroborate the theoretical analyses.     

Genetic-based fuzzy control for half-car active suspension systems

In this paper, a fuzzy controller is designed for automotive active suspension systems. A half-car model is used in order to consider the pitch angle of the body and the coupling dynamics of front and rear wheels. It is assumed that the three measurements of body acceleration, front suspension deflection and rear suspension deflection are available. The fuzzy control rules are separately designed for each measurement. After the fuzzy control rules are determined, a genetic algorithm is applied to tune the membership functions of these control rules. To measure the performance of the designed genetic-based fuzzy active suspension system, three road disturbance models are designed to simulate actual road conditions. The performance of the designed system is evaluated with respect to these disturbance models, and it is shown that the designed active suspension system provides good performance in improving ride quality and maintaining vehicle maneuvrability. It is also shown that the designed active suspension system shows robust performance with system model uncertainties.     

Stable adaptive fuzzy control for MIMO nonlinear systems

In this paper, an indirect adaptive fuzzy control scheme is presented for a class of multi-input and multi-output (MIMO) nonlinear systems whose dynamics are poorly understood. Within this scheme, fuzzy systems are employed to approximate the plant’s unknown dynamics. In order to overcome the controller singularity problem, the estimated gain matrix is decomposed into the product of one diagonal matrix and two orthogonal matrices, a robustifying control term is used to compensate for the lumped errors, and all parameter adaptive laws and robustifying control term are derived based on Lyapunov stability analysis. The proposed scheme guarantees that all the signals in the resulting closed-loop system are uniformly ultimately bounded (UUB). Moreover, the tracking errors can be made small enough if the designed parameter is chosen to be sufficiently large. A simulation example is used to demonstrate the effectiveness of the proposed control scheme.     

Stability analysis of fuzzy control systems

A discrete-time fuzzy control system which is composed of a dynamic fuzzy model and a fuzzy state feedback controller is proposed. Stability of the fuzzy control system is discussed and a sufficient condition to guarantee the stability of the system is given in terms of uncertain linear system theory. The results in this paper improve our previous stability results. An example is used to show the proposed method.     

Complex performance control using sliding mode fuzzy approach for discrete-time nonlinear systems via T-S fuzzy model with bilinear consequent part

International Journal of Control, Automation and Systems - In this paper, a stabilization problem for the discrete nonlinear system with external disturbance, multiplicative noises and multiple...     

Robust fuzzy control of mechanical systems

This paper considers the problem of controlling a mechanical system described by Euler-Lagrange equations to follow a desired trajectory in the presence of uncertainties. A fuzzy logic system (FLS) is used to approximate the unknown dynamics of the system. Based on the a priori information, the premise part of the FLS as well as a nominal weight matrix are designed first and are fixed. A compensation signal to the weight matrix error is designed based on Lyapunov analysis. To further reduce the tracking error due to the function reconstruction error, a second compensation signal is also synthesized. By running two estimators online for weight matrix error bound and function reconstruction error bound, the implementation of the proposed controller needs no a priori information on these bounds. Exponential tracking to a desired trajectory up to a uniformly ultimately bounded error is achieved with the proposed control. The effectiveness of this control is demonstrated through simulation and experiment results. These results also show that by incorporating a priori informations about the system, the fuzzy logic control can result good tracking behavior using a few fuzzy IF- THEN rules.     

Hybrid fuzzy control of robotics systems

This paper presents a new approach towards optimal design of a hybrid fuzzy controller for robotics systems. The salient feature of the proposed approach is that it combines the fuzzy gain scheduling method and a fuzzy proportional-integral-derivative (PID) controller to solve the nonlinear control problem. The resultant fuzzy rule base of the proposed controller can be decomposed into two layers. In the upper layer, the gain scheduling method is incorporated with a Takagi-Sugeno (TS) fuzzy logic controller to linearize the robotics system for a given reference trajectory. In the lower layer, a fuzzy PID controller is derived for all the locally linearized systems by replacing the conventional PI controller by a linear fuzzy logic controller, which has different gains for different linearization conditions. Within the guaranteed stability region, the controller gains can be optimally tuned by genetic algorithms. Simulation studies on a pole balancing robot and a multilink robot manipulator demonstrate the effectiveness and robustness of the proposed approach.     

Knowledge based fuzzy control of systems

A method of fuzzy control of systems based on a concept of human thinking is investigated. The fuzzy controller consists of a knowledge base containing a number of time responses of a process. The elements of this base are cells in which an input and a corresponding output of a system are stored. The cells are organized according to different types of relations. The result is a knowledge structure which contains a model of the process to be controlled. The following phases are distinguished: the learning phase, during which the relation between the imput and the output of a system is learned and the knowledge structure is constructed; and a phase in which the knowledge structure is applied to control the process. The method also provides an indication of the reliability of the control action     

Iterative learning control for linear discrete time nonminimum phase systems

This paper investigates iterative learning control for linear discrete time nonminimum phase systems. First, iterative learning control with advanced output data is considered for maximum phase systems. Next, the results are extended to nonminimum phase systems. The stability of the inverse mapping from the desired output to the input is proven based on the results for maximum phase systems. The input should be updated with the output which is more advanced than the input by the sum of the relative degree of the system and the number of nonminimum phase zeros. An example is given to indicate the importance of proper advances of output in the input update law.     

Second-order successive improvement method for discrete control systems

A second-order improvement algorithm for a discrete-time optimal control problem with a free right endpoint is considered. The solution is based on the expansion of the constructs proposed in [1] in a parameter. A new algorithm for such systems is proposed.     

Design of variable structure control for fuzzy nonlinear systems

In this paper, the variable structure control problem is presented for Takagi–Sugeno fuzzy systems with uncertainties and external disturbances. The sliding surfaces for the T–S fuzzy system are proposed by using a Lyapunov function and a fuzzy Lyapunov function, respectively. And we design the variable structure controllers such that the global T–S fuzzy system confined on the sliding surfaces is asymptotically stable. Two examples are given to illustrate the effectiveness of our proposed methods.