Performance-based control system design automation via evolutionary computing

This paper develops an evolutionary algorithm (EA) based methodology for computer-aided control system design (CACSD) automation in both the time and frequency domains under performance satisfactions. The approach is automated by efficient evolution from plant step response data, bypassing the system identification or linearization stage as required by conventional designs. Intelligently guided by the evolutionary optimization, control engineers are able to obtain a near-optimal ‘‘off-thecomputer’’ controller by feeding the developed CACSD system with plant I/O data and customer specifications without the need of a differentiable performance index. A speedup of near-linear pipelineability is also observed for the EA parallelism implemented on a network of transputers of Parsytec SuperCluster. Validation results against linear and nonlinear physical plants are convincing, with good closed-loop performance and robustness in the presence of practical constraints and perturbations

emgr - The Empirical Gramian Framework

System Gramian matrices are a well-known encoding for properties of input-output systems such as controllability, observability or minimality. These so-called system Gramians were developed in linear system theory for applications such as model order reduction of control systems. Empirical Gramian are an extension to the system Gramians for parametric and nonlinear systems as well as a data-driven method of computation. The empirical Gramian framework - emgr - implements the empirical Gramians in a uniform and configurable manner, with applications such as Gramian-based (nonlinear) model reduction, decentralized control, sensitivity analysis, parameter identification and combined state and parameter reduction

Extracting Boolean rules from CA patterns

A multiobjective genetic algorithm (GA) is introduced to identify both the neighborhood and the rule set in the form of a parsimonious Boolean expression for both one- and two-dimensional cellular automata (CA). Simulation results illustrate that the new algorithm performs well even when the patterns are corrupted by static and dynamic nois

Nonlinear self-tuning control for power oscillation damping

Power systems exhibit nonlinear behavior especially during disturbances, necessitating the application of appropriate nonlinear control techniques. Lack of availability of accurate and updated models for the whole power system adds to the challenge. Conventional damping control design approaches consider a single operating condition of the system, which are obviously simple but tend to lack performance robustness. Objective of this research work is to design a measurement based self-tuning controller, which does not rely on accurate models and deals with nonlinearities in system response. Designed controller is required to ensure settling of inter-area oscillations within 10−12s, following disturbance such as a line outage. The neural network (NN) model is illustrated for the representation of nonlinear power systems. An optimization based algorithm, Levenberg-Marquardt (LM), for online estimation of power system dynamic behavior is proposed in batch mode to improve the model estimation. Careful study shows that the LM algorithm yields better closed loop performance, compared to conventional recursive least square (RLS) approach with the pole-shifting controller (PSC) in linear framework. Exploiting the capability of LM, a special form of neural network compatible with feedback linearization technique, is applied. Validation of the performance of proposed algorithm is done through the modeling and simulating heavy loading of transmission lines, when the nonlinearities are pronounced. Nonlinear NN model in the Feedback Linearization (FLNN) form gives better estimation than the autoregressive with an external input (ARX) form. The proposed identifier (FLNN with LM algorithm) is then tested on a 4−machine, 2−area power system in conjunction with the feedback linearization controller (FBLC) under varying operating conditions. This case study indicates that the developed closed loop strategy performs better than the linear NN with PSC. Extension of FLNN with FBLC structure in a multi-variable setup is also done. LM algorithm is successfully employed with the multi-input multi-output FLNN structure in a sliding window batch mode, and FBLC controller generates multiple control signals for FACTS. Case studies on a large scale 16−machine, 5−area power system are reported for different power flow scenarios, to prove the superiority of proposed schemes: both MIMO and MISO against a conventional model based controller. A coefficient vector for FBLC is derived, and utilized online at each time instant, to enhance the damping performance of controller, transforming into a time varying controller