Jose Luis Meza

A Model for the Multiperiod Multiobjective Power Generation Expansion Problem

A long-term multiobjective model for the power generation expansion planning of electric systems is described and evaluated in this paper. The model optimizes simultaneously multiple objectives (i.e., minimizes costs, environmental impact, imported fuel and fuel price risks) and decides the location of the planned generation units in a multiperiod planning horizon. Among the attributes considered in the model are the investment and operation cost of the units, the environmental impact, the amount of imported fuel, and the portfolio investment risk. The approach to solve this problem is based on multiobjective linear programming and the analytical hierarchy process. A case study from the Mexican Electric Power System is used to illustrate the proposed framework

A Multiobjective Evolutionary Programming Algorithm and Its Applications to Power Generation Expansion Planning

The generation expansion planning (GEP) problem is defined as the problem of determining WHAT, WHEN, and WHERE new generation units should be installed over a planning horizon to satisfy the expected energy demand. This paper presents a framework to determine the number of new generating units (e.g., conventional steam units, coal units, combined cycle modules, nuclear plants, gas turbines, wind farms, and geothermal and hydro units), power generation capacity for those units, number of new circuits on the network, the voltage phase angle at each node, and the amount of required imported fuel for a single-period generation expansion plan. The resulting mathematical program is a mixed-integer bilinear multiobjective GEP model. The proposed framework includes a multiobjective evolutionary programming algorithm to obtain an approximation of the Pareto front for the multiobjective optimization problem and analytical hierarchy process to select the best alternative. A Mexican power system case study is utilized to illustrate the proposed framework. Results show coherent decisions given the objectives and scenarios considered. Some sensitivity analysis is presented when considering different fuel price scenarios.

Machine number, priority rule, and due date determination in flexible manufacturing systems using artificial neural networks

When there is a production system with excess capacity, i.e. more capacity than the demand for the foreseeable future, upper management might consider utilizing only a portion of the available capacity by decreasing the number of workers or halting production on some of the machines/production lines, etc. while preserving the flexibility of the production system to satisfy demand spikes. To achieve this flexibility, upper management might be willing to attain some pre-determined/desired performance values in a production system having identical parallel machines in each work center. In this study, we propose a framework that utilizes parallel neural networks to make decisions on the availability of resources, due date assignments for incoming orders, and dispatching rules for scheduling. This framework is applied to a flexible manufacturing system with work centers having parallel identical machines. The artificial neural networks were able to satisfactorily capture the underlying relationship between the design and control parameters of a manufacturing system and the resulting performance targets.

Stable fuzzy self-tuning PID control of robot manipulators

In this paper we present a fuzzy self-tuning algorithm to select the Proportional, Integral and Derivative gains of a PID controller according to the actual state of a robotic manipulator. The stability via Lyapunov theory for the closed loop control system is also analyzed and shown that is locally asymptotically stable for a class of gain matrices depending on the manipulator states. This feature increases the potential of the PID control scheme to handle practical constraints in actual robots such as presence of actuators with limited torque capabilities. Experimental results on a two degrees of freedom robot arm shown the usefulness of the proposed approach.

Analysis via Passivity Theory of a Class of Nonlinear PID Global Regulators for Robot Manipulators

We present a simple global asymptotic stability analysis, by using passivity theory for a class of nonlinear PID regulators for robot manipulators. Nonlinear control structures based on the classical PID controller, which assure global asymptotic stability of the closed-loop system, have emerged. Some works that deal with global nonlinear PID regulators based on Lyapunov theory have been reported by (Arimoto, 1995a), (Kelly, 1998) and (Santibanez & Kelly, 1998). Recently, a particular case of the class of nonlinear PID global regulators originally proposed in (Santibanez & Kelly, 1998) was presented by (Sun et al., 2009). Few saturated PID controllers (that is, bounded PID controllers taking into account the actuator torque constraints) have been reported: for the case of semiglobal asymptotic stability, a saturated linear PID controller was presented in (Alvarez et al., 2003) and (Alvarez et al., 2008); for the case of global asymptotic stability, saturated nonlinear PID controllers were introduced in (Gorez, 1999), (Meza et al., 2005), (Santibanez et al., 2008). The work introduced by (Gorez, 1999) was the first bounded PID-like controller in assuring global regulation; the latter works, introduced in (Meza et al., 2005) and (Santibanez et al., 2008), also guarantee global regulation, but with the advantage of a controller structure which is simpler than that presented in (Gorez, 1999). A local adaptive bounded regulator was presented by (Laib, 2000). Recently a new saturated nonlinear PID regulator for robots has been proposed in (Santibanez et al., 2010), the controller structure considers the saturation phenomena of the control computer, the velocity servo-drivers and the torque constraints of the actuators. The work in (Orrante et al., 2010) presents a variant of the work presented by (Santibanez et al., 2010), where now the controller is composed by a saturated velocity proportional (P) inner loop, provided by the servo-driver, and a saturated position proportional-integral (PI) outer loop, supplied by the control computer. In this chapter we use a passivity based approach to explain the results of global regulation of a class of nonlinear PID controllers proposed by (Santibanez & Kelly, 1998), that include the particular cases reported by (Arimoto, 1995a) and (Kelly, 1998). At the end of the 80’s, it was established in (Kelly & Ortega, 1988) and (Landau & Horowitz, 1988) that the nonlinear Analysis via Passivity Theory of a Class of Nonlinear PID Global Regulators for Robot Manipulators

An Optimal Fuzzy Self-Tuning PID Controller for Robot Manipulators via Genetic Algorithm

This paper deals with the problem of optimizing a fuzzy self-tuning PID controller for robot manipulators. Fuzzy PID controllers have been developed and applied in many fields in the last fifteen years. However, there is no systematic method to design Membership Functions (MFs) for these controllers. We propose a simple method based on Genetic Algorithms (GA) to find optimal input and output MFs of a fuzzy selftuning PID controller. The stability via Lyapunov theory for the closed loop control system is also analyzed and shown that is asymptotically stable for a class of gain matrices depending on the manipulator states. To show the usefulness of the proposed approach, simulation results using a two degree of freedom robot arm are presented.

A fuzzy multicriteria generation expansion planning model

In this paper, we present a fuzzy multi-objective model for power generation expansion planning problem with uncertainty in objective functions and demand balance constraints. The purpose of the fuzzy generation expansion model is to determine the optimal generation amount in existing and new generation units and the optimal transmission network, given that decision maker may tolerate some violation in constraints and objective functions. A fuzzy linear programming approach is utilised to transform the multiobjective fuzzy generation expansion model into a linear program. To illustrate the proposed model, a Mexican electric power system case is studied. A comprehensive sensitivity analysis on the impact of different objective functions, environmental impact of different fuel types and changes in demand is done to illustrate the capability of the proposed model.