Computational approach to optimal damping controller design for a GCSC

The gate-controlled series capacitor (GCSC) was proposed initially for series compensation of transmission line to control power flow. Then, it was shown that, in some conditions, the GCSC could damp SSR even if no specific controller is used. In the present work, the GCSC was tested for mitigation of subsynchronous resonance (SSR) including a control loop. A detailed analysis of the use of the GCSC with turn-off angle control for SSR damping is presented. The power system used for the analysis of this phenomenon was the first IEEE benchmark. The actual possibility of eliminating SSR was verified by digital simulation using the ATP/EMTP (alternative transients program /electromagnetic transients program). As a result is it possible to say that GCSC is a powerful device for controlled series compensation both for power flow control and SSR damping

In this paper, we present a global optimal and stable fuzzy controller design method for both continuous- and discrete-time fuzzy systems under both finite and infinite horizons. First, a sufficient condition is proposed which indicates that the global optimal effect can be achieved by the fuzzily combined local optimal controllers. Based on this sufficient condition, we derive a local concept approach to designing the optimal fuzzy controller by applying traditional linear optimal control theory. The stability of the entire closed-loop continuous fuzzy system can be ensured by the designed optimal fuzzy controller. The optimal feedback continuous fuzzy system can not only be guaranteed to be exponentially stable, but also be stabilized to any desired degree. Also, the total energy of system output is absolutely finite. Moreover, the resultant feedback continuous fuzzy system possesses an infinite gain margin; that is, its stability is guaranteed no matter how large the feedback gain becomes. Two examples are given to illustrate the proposed optimal fuzzy controller design approach and to demonstrate the proved stability properties.

Bellman’s discrete dynamic programming is one of the most general approaches to solve optimal control problems. For discrete-time dynamical systems, it is, at least theoretically, capable to determine globally optimal control laws. In most practical cases, both state and control variables are subject to constraints. Due to the necessity for gridding of the range of both state and control variables in numerical implementations of dynamic programming, the computational effort grows exponentially with increasing system dimensions. This fact is well known as the curse of dimensionality. Furthermore, gridding of intervals representing uncertain system parameters is inevitable, if dynamic programming is used for the design of optimal controllers for systems with uncertainties. In this contribution, an interval arithmetic procedure for the design of optimal and robust controllers is presented. This procedure relies on the basic concepts of dynamic programming. Sophisticated techniques for the exclusion of non-optimal control strategies significantly reduce the computational burden. Since interval techniques can be applied to both continuous-time and discrete-time dynamical systems, the interval arithmetic optimization approach presented in this chapter is applicable to both cases. In addition, the inclusion of effects of uncertain parameters in the underlying optimality criteria is demonstrated. For that purpose, interval arithmetic routines for analysis and design of optimal and robust controllers have been developed. Details about computationally efficient implementations of interval arithmetic optimization procedures and numerical results for a mechanical positioning system with statedependent switchings between different dynamical models for viscous and Coulomb friction are summarized.

In this paper, the model of three-axis dual rotor helicopter is built, through a new motor control algorithm of real-time grouping, the decoupling and independent control of pitch, yaw and roll channels is realized; then the “dead zone” is introduced to design the attitude controller on the basis of the optimal control law which minimize the weighted sum of response time and energy consumption, to achieve optimal control of the attitude of the helicopter; finally, the simulation model of the attitude control system is established in the MATLAB/Simulink environment. Simulation results show the feasibility of the optimal attitude controller design.

In the past decade, adaptive dynamic programming (ADP) has been widely used to realize online learning tracking control of dynamical systems, where neural networks with manually designed features are commonly used. In order to improve the generalization capability and learning efficiency of ADP, this paper presents a novel framework of ADP with sparse kernel machines by integrating kernel methods and approximately linear dependence (ALD) analysis into the critic module of ADP for the optimal tracking controller design. An ADP algorithm based on sparse kernel learning and heuristic dynamic programming (HDP) is proposed, that is, kernel HDP (KHDP). Based on KHDP, an experiment is established. By simulation, the effectiveness of proposed algorithm is demonstrated.

SUMMARY The development of control strategies for controllable passive dampers — ‘semiactive’ damping devices — is complicated by the nonlinear and dissipative nature of the devices and the nonlinear nature of the closed-loop system with any form of parametric feedback control. Control design for nonlinear systems is often achieved by designing a control for a linearized model since strategies for linear systems are straightforward. One such approach is clipped-optimal control in which the desired damper forces are determined from an optimal controller (e.g., linear quadratic regulator, linear quadratic Gaussian, H2 and H∞), which is designed assuming that the damping devices are fully linear actuators that can exert any forces (dissipative or nondissipative), and a secondary bang–bang controller commands the controllable damper to exert forces as close as possible to the desired forces. However, designs using any linearized model generally result in suboptimal (and sometimes very poor) performance because the linear actuator assumption differs from the actual implementation with a dissipative damping device. Thus, one must generally resort to a large-scale parameter study in which the nonlinear system is simulated many times to determine control strategies that are actually optimal for the nonlinear controlled closed-loop system. This paper demonstrates how an approach developed by the authors, which can reduce the model of a system with local modifications to a lower order set of Volterra integral equations in the modification forces, can significantly decrease the computational burden of a complex control design study for controllable dampers. The approach is first adapted to use a state feedback control with a bang–bang on–off secondary controller. Then, the structural and damping device models are detailed. The optimal semiactive control design is demonstrated via both a parameter study and an optimization algorithm. Numerical examples demonstrate that the approach is effective and, compared with a conventional simulation approach, can reduce computation time by two orders of magnitude. Copyright © 2014 John Wiley & Sons, Ltd.

Observer-based dynamic optimal recoil controller design for deepwater drilling riser systems

The issue of optimal controller design which is based on exact linearization of state feedback in the uncertain and chaotic systems with external disturbances is researched. First, based on the mature quadratic optimal control theory, the nonlinear chaotic system model with disturbance is converted to an equivalent linear system model without disturbance. Second, an optimal controller design method based on quadratic performance index is given which aims at pseudo-linear-system. The optimal control rate is shown after equation solution, and then the nonlinear chaotic system is changed into an equivalent linear system with Lie derivative transformation which makes the project simple and easily realized. With the simulation for parameter uncertainty in Liu chaotic systems, the result shows that the control quality has been greatly improved.

Design of an intelligent optimal neural network-based tracking controller for nonholonomic mobile robot systems

An optimal control method for distributed parameter systems is developed and applied to active vibration control of a plate. Piezoelectric material is used as an actuator. The structural dynamics are modeled using the space—time assumed mode expansion technique (a Rayleigh—Ritz method applied to both time and space variables). In particular, the generalized coordinates (both mechanical and electrical) and all generalized forces are described in terms of expansion functions. Using Hamilton's law of varying action along with the space—time assumed mode expansion results in algebraic equations of motion. These are then used as the constraint equations in the optimal controller design. Using space—time expansion, the usual variational optimal control problem is transformed into an equivalent algebraic problem. Optimal solutions are then obtained in a closed form and the solution is global within the time period considered. The solution procedure does not lead to a Riccati equation as is the case in the conventional optimal control solutions. The direct optimal control problem of a vibrating plate is illustrated through simulations where a parametric study is undertaken to check the controller performance. This study shows that the developed optimal control method is simple and the structure—control interaction is an inherent property of the method.

The reusable launch vehicle (RLV) has strongly nonlinear, fast time-varying characters and multiple control modes, which challenge attitude control in task-adaptability greatly. A nonlinear RCS/aerosurfaces compound controller control scheme was presented. First a RLV model equipped with reaction control system (RCS) and aerosurfaces is developed. Then fast and slow loops control system is designed based on time-scale separate theory. The optimal control law was implemented to generate optimal control moments. The control allocation maps the control moment commands into thrusters and aerodynamic effectors' commands. The daisy-chain combination method is applied to the control allocation to deal with the harmonization problem of the RCS and aerosufaces. The Results show the compound control scheme meets the attitude tracking performance requirements. Simulation demonstrates that the nonlinear RCS/aerosurfaces compound controller saves RCS fuel effectively and achieves coordinated control between RCS and aerosurfaces. The control scheme can also be used in other reentry vehicles or aerospace planes.

This paper describes the development of a preview controller based on optimal control theory for positioning control of a pneumatic servo system. The preview theory of tracking control using the future information has been studied extensively but did not get enough applications in the field of servo control systems, used in modern industrial processing, that will increase the accuracy and efficiency of the industrial product. In the pneumatic servo system, the position of pneumatic cylinder is the command input and its future information can be known a priori. The proposed control system is composed of feedback from the state of the plant and feed-forward from the reference signal by using future information. The validity of the proposed control system is confirmed by simulation results and comparative analysis is done with standard optimal control. Simulation results imply that good performance and good phase characteristics can be obtained using optimal preview controller.

Optimal control of interconnected systems with time-correlated noises: Application to vehicle platoon

Optimal control over multiple erasure channels using a data dropout compensation scheme

Consideration is given to the selection of velocity feedback gains for individual dampers for the members of a structurally controlled large flexible space structure. The problem is formulated as an optimal output feedback regulator problem, and necessary conditions are derived for minimizing a quadratic performance function. The diagonal nature of the gain matrix is taken into account, along with knowledge of noise covariances. It is pointed out that the method presented offers a systematic approach to the design of a class of controllers for enhancing structural damping, which have significant potential if used in conjunction with a reduced-order optimal controller for rigid-body modes and selected structural modes.