Design of generalized minimum variance controllers for nonlinear multivariable systems

The design and implementation of Generalized Minimum Variance control laws for nonlinear multivariable systems that can include severe nonlinearities is considered. The quadratic cost index minimised involves dynamically weighted error and nonlinear control signal costing terms. The aim here is to show the controller obtained is simple to design and implement. The features of the control law are explored. The controller obtained includes an internal model of the process and in one form is a nonlinear version of the Smith Predictor

Robust predictive feedback control for constrained systems

A new method for the design of predictive controllers for SISO systems is presented. The proposed technique allows uncertainties and constraints to be concluded in the design of the control law. The goal is to design, at each sample instant, a predictive feedback control law that minimizes a performance measure and guarantees of constraints are satisfied for a set of models that describes the system to be controlled. The predictive controller consists of a finite horizon parametric-optimization problem with an additional constraint over the manipulated variable behavior. This is an end-constraint based approach that ensures the exponential stability of the closed-loop system. The inclusion of this additional constraint, in the on-line optimization algorithm, enables robust stability properties to be demonstrated for the closed-loop system. This is the case even though constraints and disturbances are present. Finally, simulation results are presented using a nonlinear continuous stirred tank reactor model

NGMV control of delayed piecewise affine systems

A Nonlinear Generalized Minimum Variance (NGMV) control algorithm is introduced for the control of piecewise affine (PWA) systems. Under some conditions, discrete-time PWA systems can be transferred into an equivalent state-dependent nonlinear system form. The equivalent state-dependent systems maintain the hybrid nature of the original PWA systems and include both the discrete and continuous signals in one general description. In a more general way, the process is assumed to include common delays in input or output channels of magnitude k. Then the NGMV control strategy [1] can be applied. The NGMV controller is related to a well-known and accepted solution for time delay systems (Smith Predictor) but has the advantage that it may stabilize open-loop unstable processes [2]

Nonlinear generalized minimum variance control under actuator saturation

A new Generalized Minimum Variance control law has been derived recently for the controlof nonlinear multivariable systems. In this paper we restrict our interest tosingle-input, single-output plants with input nonlinearities in the form of hard actuatorlimits. Since in real systems saturation always exists in some form, e.g. as a result ofvalve opening limits or finite power supply, this is a natural case to consider. One ofthe well-known problems associated with input saturation is the integral windupphenomenon, which occurs whenever the controller includes integral action. In this paper,we show that the classical form of the 'anti-windup' mechanism can be obtained withinthe Nonlinear GMV controller framework by a suitable selection of the design parameters.The advantage of the approach is that the anti-windup mechanism is obtained naturallyfrom the optimization problem. There is also the possibility that the technique can beextended for other specialized nonlinear compensation problems

State-dependent Kalman filters for robust engine control

Vehicle emissions variations impose significant challenges to the automotive industry. In these simulation studies, nonlinear estimation techniques based on state-dependent and extended Kalman filtering are developed for spark ignition engines to enhance robustness of the feedforward fuel controllers to changes in nominal system parameters and measurement errors. A model-based approach is used to derive the optimal filters. Numerical simulations indicate the superiority of estimation-based approaches to enhance robustness of in-cylinder air estimation which directly contributes to the precision of engine exhaust air-fuel ratio and, consequently the consistency of the tailpipe emissions. The results obtained are for an aggressive driving profile and are presented and discusse

Non-linear predictive generalised minimum variance state-dependent control

A non-linear predictive generalised minimum variance control algorithm is introduced for the control of nonlinear discrete-time state-dependent multivariable systems. The process model includes two different types of subsystems to provide a variety of means of modelling the system and inferential control of certain outputs is available. A state dependent output model is driven from an unstructured non-linear input subsystem which can include explicit transport delays. A multi-step predictive control cost function is to be minimised involving weighted error, and either absolute or incremental control signal costing terms. Different patterns of a reduced number of future controls can be used to limit the computational demands

Non-linear minimum variance estimation for fault detection systems

A novel model-based algorithm for fault detection in stochastic linear and non-linear systems is proposed. The non-linear minimum variance estimation technique is used to generate a residual signal, which is then used to detect actuator and sensor faults in the system. The main advantage of the approach is the simplicity of the non-linear estimator theory and the straightforward structure of the resulting solution. Simulation examples are presented to illustrate the design procedure and the type of results obtained. The results demonstrate that both actuator and sensor faults can be detected successfully

Resilient nonlinear control for attacked cyber-physical systems

In this paper, the problem of resilient nonlinear control for cyber-physical systems (CPSs) over attacked networks is studied. The motivation for this paper comes from growing applications that demand the secure control of CPSs in industry 4.0. The nonlinear physical system considered can be attacked by changing the temporal characteristics of the network, causing fixed time or time-varying delays and changing the orders of received packets. The systems under attack can be destabilized if the controller is not designed to be robust with an adversarial attack. In order to cope with nonlinearity of the physical system, a nonlinear generalized minimum variance controller and a modified Kalman estimator are derived. A worst-case controller is presented for fixed-time delay. In the situations of time-varying delays and out-of-order transmissions, an opportunistic estimator and a resilient controller are designed through an on-line algorithm in the sense that it is calculated by using the information in the received packets immediately. The ability to use the received information immediately leads to the improvement of the controller's performance. Simulation results are provided to show the applicability and performance of control law developed

Nonlinear predictive generalized minimum variance LPV control of wind turbines

More advanced control strategies are needed for use with wind turbines, due to increases in size and performance requirements. This applies to both individual wind turbine controls and for the total coordinated controls for wind farms. The most successful advanced control method used in other industries is predictive control, which has the unique ability to handle hard constraints that limit system performance. However, wind turbine control systems are particularly difficult in being very nonlinear and dependent upon the external parameter variations which determine behaviour. Nonlinear controllers are often complicated to implement. The approach proposed here is to use one of the latest predictive control methods which can be used with linear parameter varying (LPV) models. These can approximate the behaviour of nonlinear wind turbines and provide a simpler control structure to implement. The work has demonstrated the feasibility and benefits that may be obtained

Nonlinear model-based condition monitoring of advanced gas-cooled nuclear reactor cores

The graphite core is one critical component in gascooled nuclear reactors and it ages and degrades over time. As a result, the graphite core can dictate the life-time of a reactor in a nuclear power station. To support the safety cases and ensure the continued safe operation of an advanced gas-cooled reactor (AGR) nuclear plant, it is important to closely monitor the condition of its reactor graphite core to maintain the integrity throughout the life of the reactor. Toward this end, the fuel grab load trace (FGLT) measurements are currently used as main information sources to infer the core condition. Due to the fact that the FGLTs are masked by many effects in the refuelling process, the first principles models for nuclear refuelling process are promising to separate the information of interests to core condition from the masked FGLT measurements. To reliably and accurately obtain the unknown parameters existing in the developed first principles model for model-based condition monitoring of AGR nuclear graphite cores, this paper presents a nonlinear system identification approach. In this approach, a nonlinear first principles model is first developed to describe the refuelling process. A friction model is then investigated to mathematically deal with the frictional effects. The aerodynamic force is also modelled separately. Finally, the Trust- Region Reflective Newton method is used to find the optimal parameters in the nonlinear refuelling model. The real-world data from an AGR nuclear power plant is employed to demonstrate the effectiveness of the proposed nonlinear system identification approach for nonlinear model-based condition monitoring of graphite core