A QFT-type frequency-domain controller design technique is presented for multiple input-multiple ouput (MIMO) nonlinear systems with significant STRUCTURED (parametric) UNCERTAINTY. Hard time-domain constraints are imposed on the outputs and control inputs in response to step disturbances. The control design method is based on replacing the nonlinear MIMO system with an equivalent diagonal MIMO linear uncertain plant using compact and convex sets of output functions. Then, a diagonal controller is designed for the equivalent linear system by transferring the time-domain constraints into the frequency-domain to obtain a series of allowable regions for the frequency responses of the nominal loop transfer functions. Next, a loop-shaping procedure is used to shape the nominal loop transfer function and hence the controller for each loop. Finally, Schauder"s fixed point theorem is utilized to show that the same controller will also work for the nonlinear system. The proposed method is illustrated by an example.

This paper concerns a study on the optimal control for nonlinear systems. An appropriate alternative in order to alleviate the nonlinearity of a system is the exact linearization approach. In this fashion, the nonlinear system has been linearized using input-output feedback linearization (IOFL). Then, by utilizing the well developed optimal control theory of linear systems, the compensated nonlinear system has been controlled. Thus, the structure of the objective function will be converted into a quadratic form which is qualitativly comparable with usual cost functions, and from operating viewpoint is more favorable. To qualify such a procedure, it has been applied to two minimum and nonminimum-phase chemical processes, and its performance is verified through computer simulations.

Due to uncertainties in system modeling as well as system parameters, current excitation systems are unable to perform quite satisfactorily over a wide range of operating conditions. In this paper a QFT-based excitation ROBUST control is proposed which the above mentioned uncertainties are, somehow, considered. The Horowitz second method is employed in the design of the nonlinear QFT controller.

In this paper, a ROBUST control law is proposed, based on Lyapunov’s theory and sliding mode control theory, in order to track the angle of attack in nonlinear longitudinal dynamics of a missile. It is assumed that there are unmatched uncertainties in the nonlinear systems. In the proposed algorithm, the controller gains are optimized by Particle Swarm Optimization (PSO) algorithm. For this purpose, a cost function is extracted from the output tracking error. Simulation results show that the proposed algorithm has better performance than conventional Proportional-Integral-Derivative (PID) controller in the presence of unmatched uncertainties.

The water level control in boiler drums is a crucial process in many process industries. In industries, water spillage or overheated tubes of the water boiler are the serious consequences of extremely high or low-level drum water level maintenance. The boiler drum is a MIMO system, consists of dead time nonlinearity and thereby there exists a transportation lag between the input and the system. Also, they possess high dynamic variations. Hence, the control of the boiler drum level is of great importance. Though, conventional PID controllers are employed in industries, due to the presence of nonlinearity, boiler drum performance can be affected when it is controlled by a PID controller. Moreover, the PID controller produces a larger settling time. Here, a ROBUST controller for the boiler drum level control based on the H-infinity technique is designed. The first-principle mathematical model of the boiler drum is formulated. The uncertainties namely: STRUCTURED UNCERTAINTY, unSTRUCTURED UNCERTAINTY, and nonlinear UNCERTAINTY are modeled by incorporating the boiler drum dynamics including its inherent nonlinearity. The boiler drum level control is carried out using the H-infinity controller scheme with the uncertainties accounted for and the performance is compared with that of a conventional PID controller. The qualitative and quantitative comparison of performances of the above control schemes reveals that the H-infinity controller has a quick rise time and faster settling time.

Interactions between input and output variables are a prevalent challenge in the design of multi-loop controllers for multivariable processes, and they can be a major stumbling block to obtaining good overall performance of a multi loop control system. The deconstructed dynamic interaction analysis is proposed to solve this limitation by decomposing the multi loop control system into a series of n independent SISO systems, each with its own PID controller. The multivariable decoupler and multi loop PID controller is applied to Two Tank Conical Interacting System (TTCIS). This TTCIS is chosen as benchmark problem used by many researchers. Firstly, the Mathematical modelling of TTCIS is derived using First principal model. The non-linear system is linearized using Jacobian matrix and decomposed into multiple SISO systems. The controller design for the process is then obtained, and an RGA matrix is constructed to minimise the interaction effects. To demonstrate the efficiency of the suggested strategy, simulation results using TTCIS are provided.

One of the issues of reliable performance in the power grid is the existence of electromechanical oscillations between interconnected generators. The number of generators participating in each electromechanical oscillation mode and the frequency oscillation depends on the structure and function of the power grid. In this paper, to improve the transient nature of the network and damping electromechanical fluctuations, a decentralized ROBUST adaptive control method based on dynamic programming has been used to design a stabilizing power system and a complementary static var compensator (SVC) controller. By applying a single line to ground fault in the network, the ROBUSTness of the designed control systems is demonstrated. Also, the simulation results of the method used in this paper are compared with controllers whose parameters are adjusted using the PSO algorithm. The simulation results show the superiority of the decentralized ROBUST adaptive control method based on dynamic programming for the stabilizing design of the power system and the complementary SVC controller. The performance of the control method is tested using the IEEE 16-machine, 68-bus, 5-area is verified with time domain simulation.

Non-cooperative intelligent control agents (ICAs) with dedicated cost functions, can lead the system to poor performance and in some cases, closed-loop instability. A ROBUST solution to this challenge is to place the ICAs at the feedback Nash equilibrium point (FNEP) of the differential game between them. This paper introduces the designation of a ROBUST decentralized infinite horizon LQR control system based on the FNEP for a linear time-invariant system. For this purpose, two control strategies are defined. The first one is a centralized infinite horizon LQR (CIHLQR) problem (i.e. a supervisory problem), and the second one is a decentralized control problem (i.e. an infinite horizon linear-quadratic differential game). Then, while examining the optimal solution of each of the above strategies on the performance of the other, the necessary and sufficient conditions for the equivalence of the two problems are presented. In the absence of the conditions, by using the least-squares error criterion, an approximated CIHLQR controller is presented. It is shown that the theorems could be extended from a two-agent control system to a multi-agent system. Finally, the results are evaluated using the simulation results of a Two-Area non-reheat power system.

This paper presents a new algorithmic method to design PI controller for a class of nonlinear systems whose state space description is in the form of polynomial functions. Design procedure is taken place based on certain or uncertain nonlinear model of system and sum of squares optimization. A so called density function is employed to formulate the design problem into a convex optimization program of sum of squares optimization form. ROBUSTness of the design is guaranteed by taking parametric UNCERTAINTY into account with an approach similar to that of generalized S-Procedure. Validity and applicability of the proposed method is certified with numerical simulation. This paper, besides presenting an innovated PI control design which is not based on local linearization and works globally, announces a new approach in formulating parametric UNCERTAINTY in nonlinear systems. Derived stability conditions do not suffer from any drawbacks seen in previous results, such as depending on a linearized model or a stable model and it can overcome most control difficulties. Furthermore, employing sum of squares techniques makes it possible to drive stability conditions with least conservatism and directly derive stability of nonlinear system.

A well known method in nonlinear QFT is to substitution of nonlinear plant by an equivalent linear family of plants. This method is based on the selection of a family of desired functions of the time response of the system. In this paper, a previously developed approach is applied to control an inverted pendulum. This approach is based on the arbitrary selection of output functions. The results show that the implementation of the method is more straightforward and lead to satisfactory results.