Reverse engineered MPC for tracking with systems that become uncertain

A constrained model predictive control technique for tracking is proposed for systems whose models become uncertain (for example after a sensor failure). A linear time invariant robust controller with integral action is used as a baseline and ``reverse engineered'' into the form of a reduced order observer, steady state target calculator and control gain, based on a nominal model, augmented with integrating disturbance states. Constraints are enforced by optimising over perturbations to the nominal control action. Clean transition between a nominal, high performance mode of operation when parameters are known, to a safe and recursively feasible robust mode when parameters are unknown can be facilitated by using the same steady state target in both cases.The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement number 314 544, project ``RECONFIGURE''.European Control Conference 2014, Strasbour

Vibration suppression in multi-body systems by means of disturbance filter design methods

This paper addresses the problem of interaction in mechanical multi-body systems and shows that subsystem interaction can be considerably minimized while increasing performance if an efficient disturbance model is used. In order to illustrate the advantage of the proposed intelligent disturbance filter, two linear model based techniques are considered: IMC and the model based predictive (MPC) approach. As an illustrative example, multivariable mass-spring-damper and quarter car systems are presented. An adaptation mechanism is introduced to account for linear parameter varying LPV conditions. In this paper we show that, even if the IMC control strategy was not designed for MIMO systems, if a proper filter is used, IMC can successfully deal with disturbance rejection in a multivariable system, and the results obtained are comparable with those obtained by a MIMO predictive control approach. The results suggest that both methods perform equally well, with similar numerical complexity and implementation effort

Estimation of pH and MLSS using Neural Network

The main challenges to achieving a reliable model which can predict well the process are the nonlinearities associated with many biological and biochemical processes in the system. Artificial intelligent approaches revolved as better alternative in predicting the system. Typical measured variables for effluent quality of wastewater treatment plant are pH, and mixed liquor suspended solids (MLSS). This paper presents an adaptive neuro-fuzzy inference system (ANFIS) and feed-forward neural network (FFNN) modeling applied to the domestic plant of the Bunus regional sewage treatment plant. ANFIS and feed- forward neural network techniques as nonlinear function approximators have demonstrated the capability of predicting nonlinear behaviour of the system. The data for the period of two years and nine months sampled weekly (140 week samples) were collected and used for this study. Simulation studies showed that the prediction capability of the ANFIS model is somehow better than that of the FFNN model. The ANFIS model may serves as a valuable prediction tool for the plant

Adaptive control of a boost-buck converter for thermoelectric generators

Thermoelectric generators (TEGs) are used to recover waste heat of the exhaust gas and convert it into electric energy in automotive applications. The temperature of the waste heat influences the voltage and internal resistor of a TEG. For the electric linking of TEGs to the on-board power supply, a DC-DC converter may be used. The control of the DC-DC converter must be robust against dynamic changes and additionally has to track the maximum power point (MPP) of the TEG. This paper presents a digital cascade controller for a boost-buck converter to charge a vehicle battery and to supply the load. To track the MPP, a hill climbing (HC) algorithm is implemented, which is also used for photovoltaics. The conversion time of the HC is minimized with an adaptive step size. Width variations of electric parameters of TEG influence the dynamic and stability of the controllers. With a closed loop identification, the parameter variation is estimated, and the control parameters can be redesigned. An experimental result show the efficiency of the adaptive control.BMBF, 03X3553E, Thermoelektrische Generatoren 202

Feasibility of Using Nonlinear Time-Frequency Control for Magnetorheological Dampers in Vehicle Suspension

Semi-active vehicle suspensions that use magnetorheological (MR) dampers are able to better dissipate vibrations compared to conventional dampers because of their controllable damping characteristics. The performance of current MR damper control methods is often hindered by incorrect assumptions and linearized models. Therefore, a need exists to design an adaptive controller with improved accuracy and reliability. The objective of this research is to design an improved controller for MR dampers in vehicle suspension using the nonlinear time-frequency control approach and evaluate its feasibility by numerically employing MATLAB Simulink. Simulations in this research are performed using a simplified quarter car suspension model and modified Bouc-Wen damper model. The proposed control method is evaluated based on its ability to reduce the amplitude of vibrations and minimize acceleration of the car body for various test cases. Simulations are also performed using the skyhook controller and passive suspension to assess the performance of the proposed controller. The results of the simulations show that the proposed nonlinear time-frequency controller can successfully be applied to an MR damper suspensions system for vibration control. The proposed controller outperforms the skyhook controller in terms of reducing acceleration of the car body in each of the tested scenarios. The proposed controller also shows the ability to more efficiently manage the current input to the system. In general, the skyhook controller gives more improved vibration amplitude responses but is prone to generate large spikes in car body acceleration at higher frequency road profile inputs. Simulations performed with the passive system show large displacement amplitudes and inability to prevent oscillation. The feed-forward aspect and adaptive nature of the proposed controller gives it the ability to better compensate for the time-delay in the operation of the MR damper. The proposed controller shows sensitivity to controller parameters when pursuing the best response for different road profile input cases

Estimation of pH and MLSS using neural network

The main challenges to achieving a reliable model which can predict well the process are the nonlinearities associated with many biological and biochemical processes in the system. Artificial intelligent approaches revolved as better alternative in predicting the system. Typical measured variables for effluent quality of wastewater treatment plant are pH, and mixed liquor suspended solids (MLSS). This paper presents an adaptive neuro-fuzzy inference system (ANFIS) and feed-forward neural network (FFNN) modeling applied to the domestic plant of the Bunus regional sewage treatment plant. ANFIS and feed- forward neural network techniques as nonlinear function approximators have demonstrated the capability of predicting nonlinear behaviour of the system. The data for the period of two years and nine months sampled weekly (140 week samples) were collected and used for this study. Simulation studies showed that the prediction capability of the ANFIS model is somehow better than that of the FFNN model. The ANFIS model may serves as a valuable prediction tool for the plant

Power System Nonlinear Modal Analysis Using Computationally Reduced Normal Form Method

Increasing nonlinearity in today’s grid challenges the conventional small-signal (modal) analysis (SSA) tools. For instance, the interactions among modes, which are not captured by SSA, may play significant roles in a stressed power system. Consequently, alternative nonlinear modal analysis tools, notably Normal Form (NF) and Modal Series (MS) methods are being explored. However, they are computation-intensive due to numerous polynomial coefficients required. This paper proposes a fast NF technique for power system modal interaction investigation, which uses characteristics of system modes to carefully select relevant terms to be considered in the analysis. The Coefficients related to these terms are selectively computed and the resulting approximate model is computationally reduced compared to the one in which all the coefficients are computed. This leads to a very rapid nonlinear modal analysis of the power systems. The reduced model is used to study interactions of modes in a two-area power system where the tested scenarios give same results as the full model, with about 70% reduction in computation time