Robust control design for uninterruptible power systems

International audience

Robust SVC controller design and analysis for uncertain power systems

Dynamic integral sliding mode control for interconnected delayed power systems

In this article, the problem of robust exponential stability and reliable stabilisation for a class of continuous-time networked control systems (NCSs) with a sample-data controller and unknown time-varying sampling rate is considered. The analysis is based on average dwell-time, Lyapunov–Krasovskii functional and linear matrix inequality (LMI) technique. The delay-dependent criteria are developed for ensuring the robust exponential stability of the considered NCSs. The obtained conditions are formulated in terms of LMIs that can easily be solved by using standard software packages. Furthermore, the result is extended to study the robust stabilisation for NCS with parameter uncertainties. A state feedback controller is constructed in terms of the solution to a set of LMIs, which guarantee the robust exponential stabilisation of NCS and the controller. Finally, numerical examples are presented to illustrate the effectiveness of the obtained results.

ROBUST DECENTRALIZED CONTROLLER DESIGN FOR POWER SYSTEMS USING CONVEX OPTIMIZATION INVOLVING LMIS

Results on the design of robust memoryless state feedback controllers for uncertain time-delay systems with norm bounded uncertainty are presented. It is proved that the feasibility of a linear matrix inequality (LMI) problem is necessary and sufficient for the quadratic stabilisation of an uncertain time-delay system. A robust state feedback controller can be constructed using the corresponding feasible solution of the LMI problem. A procedure is given to select a suitable state feedback controller that is also suboptimal in the sense of minimising a bound on a quadratic performance index.

This paper presents a new design algorithm for the decentralized output feedback control problem of large-scale interconnected systems. Each subsystem is composed of a linear (possibly unstable) time-invariant part and an uncertain additive nonlinearity which is a discontinuous function of time and state of the overall system. The nonlinear function is assumed to be bounded by a quadratic inequality, and a decentralized estimated state feedback controller and a decentralized observer are designed for each subsystem, based on linear matrix inequalities. Sufficient conditions for the synthesis of feedback action are provided, under which the proposed controllers and observers can achieve robust stabilization of the overall large-scale system. An attractive feature of the proposed scheme is that it guarantees connective stability of the overall system and requires no intersubsystem communication. The controller design is evaluated on a natural circulation drum boiler, where the nonlinear model describes the key dynamical properties of the drum, the risers, the downcomers, and the turbine-generator unit. The linearized system has two poles at origin, one associated with water dynamics and the other with generator dynamics. Simulation results are presented that show the effectiveness of the proposed control against instabilities following sudden load variations. The control is also effective for steady-state operation.

This paper studies the problem of reliable Hinfin state feedback controller design for linear continuous-time systems. Actuator partial failures and actuator lock-in-place failures are simultaneously considered, and the signals in the case of certain actuators being locked in place are treated as zero-frequency disturbances, which gives an exact description for the lock-in- place failures. Based on the generalized KYP lemma in finite frequency domain, the controller design is developed in the framework of linear matrix inequality (LMI) approach, which can guarantee the asymptotic stability and Hinfin performances of the resulting closed-loop systems when all control components are operational as well as when some actuators fail. Finally, the design procedure and the effectiveness of the proposed method in comparison with the entire frequency approach by using bounded real lemma are illustrated via a numerical example of flight tracking control of an F-16 aircraft.

A nervous system consists of a large number of highly interconnected nerve cells. Nerve cells communicate by generation and transmission of short electrical pulses (action potential). In addition, membrane voltage is the only measurable state in nervous systems. A robust observer-based model reference tracking control is proposed for Hodgkin-Huxley (HH) neuron systems to generate a desired reference response in spite of environmental noises, uncertain initial values, and diffusion currents from other interconnected nerve cells. In order to simplify the robust tracking control design of nonlinear stochastic HH neuron systems, a fuzzy interpolation method is employed to interpolate several linear stochastic systems to approximate a nonlinear stochastic HH neuron system so that the nonlinear robust tracking control problem can be solved by the linear matrix inequality (LMI) technique with the help of Robust Control Toolbox in Matlab. The proposed robust observer-based tracking control scheme can provide new methods for desired action potential generation, suppression of oscillations, and blockage of action potential transmission under environmental noise and diffusion currents. These new methods are useful for patients with different neuron system dysfunctions. Finally, three simulation examples of tracking control of nervous systems are given to illustrate the design procedure and confirm the tracking performance of the proposed method.

Robust velocity and height control problem is dealt with for hypersonic vehicles subject to highly nonlinearity and uncertainties. The linear robust controller design for nonlinear systems suffers from the conservation problem, because the nonlinear dynamics are considered as uncertainties. In this paper, a feedback linearization controller is firstly applied to achieve the dynamic decoupling of the velocity and height channels. Then, robust controllers are designed for each channel to restrain the effects of the parametric uncertainties, external disturbances, and nonlinear dynamics, which cannot be counteracted accurately by the feedback linearization technique. This method can guarantee the robust stability and robust tracking properties of the closed-loop control system, while the conservation of the robust controller design can be reduced because the nonlinear information of dynamic system is used. Simulation comparisons are given to show the advantages of the proposed robust nonlinear control method.

The problem of robust velocity and height control is dealt with for hypersonic vehicles subject to high nonlinearity and uncertainties. Robust linear controller design for nonlinear systems suffers from the problem of conservation because the nonlinear dynamics are considered as uncertainties. In this paper, a feedback linearization controller is firstly applied to achieve dynamic decoupling of the velocity and height channels. Robust controllers are then designed for each channel to achieve the desired tracking and restrain the effects of parametric uncertainties, external disturbances, and nonlinear dynamics, which cannot be accurately counteracted by the feedback linearization technique. This method can guarantee the robust stability and robust tracking properties of the closed-loop control system. Moreover, the conservation of the robust controller design can be reduced because the nonlinear information of the dynamic system is used. Simulation results are given to show the advantages of the proposed robust nonlinear control method compared with its linear counterpart.

To increase the performance of closed-loop controlled systems in off-nominal conditions and in the presence of inevitable faults and uncertainties, a systematic approach based on robust convex optimization for adaptive augmenting control design is discussed in this paper. More specifically, this paper addresses the problem of adaptive augmenting controller (AAC) design for systems with time-varying polytopic uncertainty. First, a robust state-feedback controller is designed via robust convex optimization as a baseline controller. The closed-loop polytopic system with the baseline controller is considered as the desired time-varying reference model for the design of a direct state-feedback adaptive controller. Next using Lyapunov arguments, global stability of combined robust baseline and adaptive augmenting controllers is established. Furthermore, it is proved that tracking error converges to zero asymptotically. A case study for a generic nonminimum phase nonlinear pitch-axis missile autopilot is conducted. Simulation tests are performed to evaluate stability and performance of nonlinear time-varying closed-loop system in the presence of uncertainties in pitching moment and normal force coefficients, and unmodeled time delays. In addition, results of the simulations indicate satisfactory robustness in case of severe loss of control effectiveness event.

This paper proposes a new state-space controller for the robust trajectory tracking control problem of multi-mass resonant systems. It is designed in state-space by combining Differential Flatness (DF) and a higher order Disturbance Observer (DOb). The former systematically generates the control input and state references of a state feed-back controller so that not only regulation but also trajectory tracking control can be performed in state-space. They are obtained in terms of differentially flat output variable in DF. The latter estimates disturbances and their successive time derivatives. Its order depends on the resonant modes of the system. The proposed controller has a two-degrees-of-freedom motion control structure. Its performance and robustness can be independently adjusted by tuning the state feed-back controller and DOb, respectively. It is designed in two steps. In the first one, the state feed-back controller is tuned by only considering the nominal plant model of the system. The nominal performance is adjusted by using pole placement; e.g., all poles of the nominal system are placed on the real axis so as to suppress the vibration at tip. The state and control input references of the state feed-back controller are obtained by using DF. In the second step, the robustness of the system is achieved by reconstructing the state vector via the estimations of disturbances and their successive time derivatives. Thanks to the proposed new state variables, mismatched disturbances are automatically cancelled by the state feed-back controller. The matched disturbances are cancelled by feeding-back their estimations through control input. Hence, the robustness of the multi-mass resonant systems is achieved. Without losing generality, the proposal is verified by considering three-mass resonant systems. Its simulation results are given to validate the proposal.

In this paper, the authors propose an optimal IMC-PID controller design for the Load Frequency Control (LFC) of large-scale power system via model approximation method. The model approximation method uses the Enhanced Differential Evolution (EDE) algorithm to determine an optimal Reduced Order Model (ROM) for the considered large-scale power system by minimizing the performance measure called Integral Square Error (ISE) between their step responses. Later, the LFC design is carried out using an optimal ROM instead of processing with the large-scale power system model. Thus, this simplifies the design, reduces the computational efforts and also helps in determining the lower order controller. An optimal IMC design methodology is proposed by minimizing ISE between the actual output and the reference input responses of the large-scale power system using EDE algorithm. Further, PID controller gains are acquired by least square model matching with the optimal IMC transfer function. The proposed IMC-PID controller design allows a satisfied reference input tracking performance, robustness in disturbance rejection and improves the dynamic stability of the power system. The proposed method is validated by applying it to a single area power system of third-order SISO model and also extended to a centralized two-area thermal–thermal non-reheated power system of a seventh-order MIMO model. The simulation results and the comparison of error performance indices show the efficacy of the proposed method over the significant methods available in the literature.

In this article, robust control schemes are presented for time-varying trajectory tracking control of Wheeled Mobile Robots (WMRs) in the presence of disturbance. First, the robust model predictive control (RMPC) is implemented for the kinematic subsystem by solving the modified optimization problem with the fixed starting point. Second, the robust control is extended for the dynamic subsystem by the Backstepping technique and additional observer. This paper shows how to ensure the stability of the closed-loop tracking error system. The performance of the proposed robust control is verified through simulation studies using Casadi tool.KeywordsModel predictive control (MPC)Wheeled mobile robotic systems (WMRs)Output feedback controlTerminal controllerFeasibility