Degradation Of Closed-loop Performance Research Articles

Active Flutter Suppression: Quantification of Performance Loss Due to Actuator Saturation

Active flutter suppression technologies have the potential to lead to major weight savings while keeping the envisaged flight envelope for the future generation of commercial aircraft. Since the stabilization of aeroelastic systems undergoing flutter instabilities is strongly affected by actuators’ characteristics and delays in the closed loop, the degradation of closed-loop performance due to actuators’ nonlinearities must be thoroughly understood. Comparisons between the region of attraction and the controllable region can support the quantification of performance loss due to common actuators’ nonlinearities, such as amplitude and rate limits. At first, this paper illustrates a numerically robust algorithm for the full characterization of the controllable region of a generic unstable system under input amplitude and rate constraints. Secondly, the concept of region of attraction is expanded to assess the boundedness of the closed-loop response in the presence of input saturation and atmospheric disturbances. These concepts are here employed to define two nonlinear performance measures that quantify the performance loss due to actuators’ saturation in an unstable aeroelastic system stabilized by a flutter suppression controller. The approach is demonstrated on a realistic aeroelastic system for which different H2 and H∞ output feedback controllers are synthesized to augment the damping of the critical flutter mode.

Remaining useful life prediction for motor systems by iteratively updated wiener process based on closed-loop performance degradation monitoring

Driven by the need for remaining useful life prediction of degraded motor systems with feedback controllers, a real-time updated Wiener stochastic process is adopted to model the performance degradation of motor systems. First, a closed-loop performance index of the motor system is derived incorporating the multiple slow time-varying characteristic of motor parameters. On this basis, the drift coefficient and diffusion coefficient of the Wiener degradation model are updated to obtain the prior maximum likelihood function with available historical data. It is followed by the iterative optimization of nonlinear feature parameter in the Wiener degradation model with taking the prior maximum likelihood function as the cost equation. The effectiveness of proposed remaining useful life prediction architecture for closed-loop motor systems is demonstrated by motor systems.

A Robust Stackelberg Game for Cyber-Security Investment in Networked Control Systems

We present a resource-planning game for cyber-security of networked control systems (NCSs). The NCS is assumed to be operating in closed loop using a linear state feedback <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H}_{2}$ </tex-math></inline-formula> -controller. A zero-sum, two-player Stackelberg game (SG) is developed between an attacker and a defender for this NCS. The attacker aims to disable communication of selected nodes and thereby render the feedback gain matrix to be sparse, leading to degradation of closed-loop performance, while the defender aims to prevent this loss by investing in the protection of targeted nodes. Both the players trade their <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H}_{2}$ </tex-math></inline-formula> -performance objectives for the costs of their actions. The standard backward induction (BI) method is modified to determine a cost-based Stackelberg equilibrium (CBSE) that saves the players’ costs without degrading the control performance. We analyze the dependence of a CBSE on the relative budgets of the players and on the node “importance” order. Moreover, a robust defense (RD) method is developed for the realistic case when the defender is not informed about the attacker’s resources. The proposed algorithms are validated using examples from wide-area control of electric power systems. It is demonstrated that reliable and RD is feasible unless the defender’s resources are severely limited relative to the attacker’s resources. We also show that the proposed methods are robust to time-varying model uncertainties and thus are suitable for long-term security investment in realistic NCSs. Finally, we use computationally efficient genetic algorithms (GAs) to compute the optimal strategies of the attacker and the defender in realistic large power systems.

Saturated Backstepping-Based Tracking Control of a Quadrotor With Uncertain Vehicle Parameters and External Disturbances

Knowing the vehicle’s parameters (e.g., mass, moment of inertia) in advance is not always possible in quadrotor control applications, especially if we use the quadrotor for cargo transportation. Uncertain cargo weight and size together with external disturbances may result in closed-loop performance degradation or even instability. In light of this problem, this letter proposes a saturated robust adaptive tracking controller for a quadrotor based on nonlinear Lyapunov-theory, where a set of estimation laws are designed to compensate for uncertain parameters and disturbances, achieving global uniformly ultimately boundedness (GUUB). Additionally, through the use of saturation functions, the designed thrust force is ensured to be bounded by a predefined value. Numerical simulation examples are presented and discussed to validate the effectiveness of the proposed solution. To further highlight the enhancements of the presented method, comparison results with an existing control strategy are provided and analyzed.

Inertia-free computation efficient immersion and invariance adaptive tracking control for Euler-Lagrange mechanical systems with parametric uncertainties

This paper presents a computation efficient Immersion and Invariance (I&I) adaptive control method for the tracking control problem of Euler–Lagrange mechanical systems with parametric uncertainties. By employing a generalized velocity observer, the proposed controller retains the basic advantages of the I&I design and requires much less extension on extra closed-loop system states when compared with the frequently used filter-based I&I adaptive control method, and the initial value constraint in the filer-based I&I to achieve “parameter lock” is also removed. Compared with the existing dynamic-scaling-based I&I adaptive control methods, the requirement of the priori knowledge on the exact bounds of unknown parameters is avoided. In addition, the dynamic gains in the proposed control method have linear relationship with the scaling factor instead of quadric, which could reduce the growth speed of the dynamic gains and avoid possible closed-loop performance degradation.

Model-based strategies for sensor fault accommodation in uncertain dynamic processes with multi-rate sampled measurements

This work presents model-based strategies for the accommodation of sensor faults in dynamic processes with multi-rate sampled state measurements. The developed strategies account explicitly for both closed-loop stability and performance considerations. Initially, a model-based feedback control system, in which a model predictor compensates for the unavailability of state measurements between sampling times, is designed. The stability and performance characteristics of the multi-rate sampled-data closed-loop system are analyzed and explicitly characterized in terms of the state sampling rates, the fault parameters, and the various process, model and controller design parameters. The resulting characterizations provide insight into the robustness and margins of tolerable faults that can be accommodated, and are used to develop both stability-based and performance-based fault accommodation schemes that aim to maintain closed-loop stability and minimize the degradation of closed-loop performance in the presence of sensor faults. Two types of sensor faults are considered. These include faults that manifest themselves as improper sensor readings, as well as faults that cause drift in the sensor sampling rate. The first type of fault introduces errors in the model state updates at the sampling times, while the second type of fault alters the rate at which the sensors sample the process states. The developed methods are illustrated through a case study involving a cascade of two non-isothermal continuous-stirred tank reactors with plant-model mismatch and access to full state measurements that are sampled at different rates. The case study gives some insight into the effects of sensor faults on the stability and performance of the sampled-data state feedback control system, and how these effects can be mitigated through use of fault-tolerant control.

H2 and H∞ State-Feedback Control Through Multi-Hop Networks: Trade-Off Analysis Between the Network Load and Performance Degradation

This paper is concerned with the H2 and H∞ control problems in networked control framework, dealing specifically with cases where the communication between the components is made via a multi-hop network susceptible to packet loss, which is modeled by a Markov chain with Bernoulli distribution. The aim is to investigate the trade-off between the network resource saving and the closed-loop performance degradation in the context of state-feedback control. The performance of the control design is measured by means of the H2 and H∞ norms. Two main topics are evaluated: i) The influence of the initial probability distribution on the design of the controller; and ii) The relationship between the expected value of the output signal energy and the probability of successful transmissions (associated with the maximum allowable transmission per data packet) for a given transport scheme and a given performance criterion. The results are applied in a case study based on a linearized numerical model of the Furuta’s inverted pendulum.

Preserving Privacy of Finite Impulse Response Systems

Adding input and output noises for increasing model identification error of finite impulse response (FIR) systems is considered. This is motivated by the desire to protect the model of the system as a trade secret by rendering model identification techniques ineffective. Optimal filters for constructing additive noises that maximizes the identification error subject to maintaining the closed-loop performance degradation below a limit are constructed. Furthermore, differential privacy is used for designing output noises that preserve the privacy of the model.

Gain-Scheduling Control of Flexible Aircraft with Actuator Saturation and Stuck Faults

In this paper, a fault-tolerant control design that can gain schedule with the airspeed is developed for a flexible aircraft with actuator saturation and stuck control surface faults. First, a flexible aircraft model that captures the coupling between rigid-body motions and flexible modes is presented. Then, a linear parameter-varying fault-tolerant controller featuring a proportional and integral structure is designed for a stuck control surface scenario, with control gains solved from a set of set-invariant conditions represented by linear matrix inequalities. The controller, which gain schedules with the airspeed, is able to eliminate the effects of stuck faults on the system output while minimizing the effects on other system states and guaranteeing no closed-loop performance degradation caused by actuator saturation. Finally, the effectiveness of the proposed design is demonstrated on an aircraft model with high-aspect-ratio wings in numerical simulations.

Fault Detection in Process Systems using Hidden Markov Disturbance Models

Fault detection and diagnosis is critical for maintaining the health of process systems. Common fault signals include process and disturbance parameter changes, as well as sensor and actuator malfunctions (such as persistent drifts and biases). These may be characterized by the existence of latent ‘fault’ states. This work examines the effectiveness of a Hidden Markov Model framework for modeling such fault regimes. The proposed methodology may be interpreted as a generalization of a commonly-employed Mixture-of-Gaussians (Kesavan and Lee (1997)) approach and is demonstrated through a shell-and-tube heat exchanger problem. Furthermore, the exibility of the method is shown in the context of detecting valve stiction. This is a significant problem in process industries where a valve's output suffers from excessive friction and is unable to track its input leading to degradation in closed-loop performance.

High-Performance Spacecraft Adaptive Attitude-Tracking Control Through Attracting-Manifold Design

Virtually every existing adaptive attitude control solution is based on the certainty-equivalence principle, which permits the adaptive controller structure to be based upon the deterministic feedback control algorithm (controller design based on nominal system information without any inertia-parameter uncertainty) and to be used in conjunction with a suitable adaptive parameter-estimation algorithm. However, one of the main drawbacks of the certainty-equivalence-based adaptive control methodology is the arbitrary degradation in closed-loop performance due to the adaptation (parameter-estimation) process, which acts like a forcing disturbance (uncertain parameter effect) imposed onto the deterministic closed-loop control dynamics. In this paper, we significantly deviate from the classical certainty-equivalence-based adaptive control framework and develop, for the first time (to our best knowledge), a noncertainty-equivalent adaptive attitude control algorithm. This novel control design process eliminates the deleterious performance-degradation effects of the certainty-equivalence controller through the introduction of a stable attracting manifold into the adaptation process, such that the resulting closed-loop adaptive attitude control dynamics recover the deterministic (ideal) case of closed-loop attitude controller performance (i.e., no uncertain parameter effects). In addition to detailed derivations of the new controller design and a rigorous sketch of all the associated stability and attitude error convergence proofs, we present numerical simulation results that not only illustrate the various features of the new noncertainty-equivalent controller design methodology but also highlight the ensuing closed-loop-performance benefits when compared with the conventional certainty-equivalence-based adaptive control schemes.

Controller Reduction by the Block Balanced Realization Using the General Representation of H.INF. Controllers

In this paper, we propose a new controller reduction method using the closed-loop block balanced realization technique. To decrease the closed-loop performance degradation, it is need to take account the closed-loop configuration in controller reduction process. The block balanced realization technique was developed from the open-loop balanced realization method to consider the closed-loop configuaration. In our method, we employ the block balanced realization method to reduce the order of H∞ controllers, and to consider robustness for controller variation, we use the general representation of H∞ controllers. A numerical example is presented to illustrate the effectiveness of our method.

Control System Selection: A Measure of Control Quality Loss in Analytical Control

A question that often process control engineers face is for what class of processes one should use model predictive control that requires solving numerically a constrained optimization problem repeatedly on-line. The alternative is to use analytical control, such as P, PI, PID and differential geometric control, which do not require the on-line optimization. In other words, for what class of processes can analytical control provide control quality close to the optimal control quality that model predictive control (MPC) can provide? Here an analytical controller is defined as the one whose implementation does not require solving a constrained optimization problem numerically. This work presents a measure that allows one to quantify the degradation in closed-loop performance when one implements analytical control instead of MPC. A special case of the measure is used to derive a simple test that can be used to check if a given process with active input constraints can be controlled satisfactorily by analytical control. It is shown that processes with active input constraints and directionality benefit greatly from MPC. In other words, for input-constrained processes whose nonsingular characteristic (decoupling) matrix is independent of manipulated inputs and can be made diagonal by row or column re arrangements, control quality provided by analytical control can be adequate. The measure is used to see if four input-constrained process examples can be controlled satisfactorily by analytical control. Closed-loop responses are shown to confirm the usefulness of the measure.

A methodology for control-relevant nonlinear system identification using restricted complexity models

Abstract A broadly-applicable, control-relevant system identification methodology for nonlinear restricted complexity models (RCMs) is presented. Control design based on RCMs often leads to controllers which are easy to interpret and implement in real-time. A control-relevant identification method is developed to minimize the degradation in closed-loop performance as a result of RCM approximation error. A two-stage identification procedure is presented. First, a nonlinear ARX model is estimated from plant data using an orthogonal least squares algorithm; a Volterra series model is then generated from the nonlinear ARX model. In the second stage, a RCM with the desired structure is estimated from the Volterra series model through a model reduction algorithm that takes into account closed-loop performance requirements. The effectiveness of the proposed method is illustrated using two chemical reactor examples.

Optimal anti-windup control of saturating discrete-time MIMO systems

Directional changes in control signals due to actuator saturation may cause severe deterioration in performance, especially when a plant is ill-conditioned. However, this fact is usually ignored by conventional anti-windup schemes. To tackle this problem, this paper presents an anti-windup controller that provides graceful degradation of closed-loop performance for saturating discrete-time MIMO systems. In addition, linear performance is preserved in the absence of actuator saturation. The present anti-windup scheme achieves its goals by minimizing the euclidean norm of the weighted difference between the performance of the saturating system and that of the unsaturated linear system. This optimal performance problem is solved under a renewed generalized conditioning technique. Necessary and sufficient conditions for the existence of a unique optimal solution and the unique optimal anti-windup control law are obtained. Stability of the saturating system is also discussed. Finally, the proposed controller is successfully applied to an uncertain ill-conditioned distillation column in the illustrative example.

Robust control of processes subject to saturation nonlinearities

Motivated by current practice, a two-step design technique for saturating systems is studied. First an “optimal” (for example in the H∞ sense) linear controller is designed neglecting saturation. Then a saturation compensation scheme (anti-windup) is designed which provides graceful degradation of closed-loop performance in the face of saturation. The focus in this paper is on the second step, and obtaining general results and insights applicable to any (linear) system subject to saturation. A design technique is developed which results in effective saturation compensation for a given multivariable plant and linear compensation design. For particular controller choices the resulting saturation compensator is shown to be equivalent to proven techniques includin g anti-rest windup and internal model control (IMC). Tools are developed for robust stability and performance analysis of nonlinear systems. Well-known structured singular value robustness tests for linear systems are extended to a class of nonlinear systems. Sufficient conditions are developed which guarantee closed-loop stability for all plants in a structured uncertainty set and for all nonlinearities of a specified form. These tests result in simple conditions on the initial linear controller design which must be satisfied in order to guarantee robust stability of the saturating plant. In some instances this requires that the original linear design be detuned. A procedure for performing this detuning is outlined. A promising single-step procedure for the synthesis of optimal robust linear controllers for saturating systems is also outlined. While this approach lacks the simplicity of the two-level decomposition, it appears to have promise for situations where the impact of the saturation on the closed loop is severe.