Alain Segundo Potts

Enhancement in performance and stability of MRI methods

Abstract Two representative approaches for MRI (MPC Relevant Identification) methods are reported in the literature. The first one is based on the solution of an optimal problem, while the second is based on the prefiltering of the system input and output signals. Each method has advantages and disadvantages in accordance with the process to identify, the length of the prediction horizon or its mathematical implementation. A new MRI method is proposed herein, based on the advantages of both algorithms. A comparison is performed among some MRI methods and the new proposed one. The results indicate that in the studied case, the performance of the new method is better.

Optimal power loss motion planning in legged robots

An iterative algorithm to minimize energy loss in kinematic chains is proposed. This algorithm is designed to low level of control where variables such as terminal states, runtime, and physical and electrical parameters of the movement are given by higher levels of control. An original complex problem of optimization is transformed into a simple quadratic programming problem subject to linear constraints by discretizing all dynamic system variables. The whole system is then converted into a recursive matrix equation that is solved iteratively. A proof of convergence is suggested. The performance of the algorithm is illustrated by using it in the motion planning of a quadruped robot.

Detection of decoupled input/output pairs in multivariable systems.

A method is proposed to detect if there is no coupling between an input and an output in systems operating in open-loop, that is, without a supervisory controller. The proposed technique is applicable to multiple input multiple output (MIMO) systems, whose intent is to detect no-model input/output (IO) combinations in a transfer matrix. Traditional approaches for selecting IO pairs are usually performed after the plant model is identified. The presented approach is applied during the pre-identification stage and is based on IO cross-correlation, signal filtering and fuzzy logic analysis. A case study involving the identification of a 7×6 simulated Fluid Catalytic Cracking (FCC) is discussed, as well as an influence analysis of detecting no-model IO pairs in the identification process and in the performance index of a Model Predictive Controller (MPC) applied to a 2×2 simulated distillation column. Finally, the method is tested with a real dataset obtained from an FCC unit of a petrol refinery.

Kinematics Analysis of a Quadruped Robot

Abstract This paper presents the kinematics model of a complex quadruped robot. Initially, the so-called position analysis problem is approached for both the inverse and the direct kinematics problems. All equations and geometric constraints of the system are presented. Finally a singularity analysis of closed-chain kinematics is done, pointing out which configurations should be used or avoided for a desired behavior of the robot. The singularity analysis was done by considering three different kinds of singularities: direct, inverse and combined.

Algorithms and Methods for Identification of Multivariable Plants

Abstract In this work, techniques of multivariable identification that can be applied to the plant operating in open or closed loop are used. The idea is to improve the quality of the obtained models and to reduce the cost and time spent on identifying them. These models are essential for the deployment of advanced controllers like MPC (Modelbased Predictive Controllers). It is known that multivariable MPC controllers reduce the variability of the process, allowing to operate closer to the constraints of quality of the various products, thereby maximizing the profit of the plants. The validation of the models obtained with the proposed tool reveals that they present a satisfactory behavior.

Fuzzy auto-tuning for a PID controller

This paper presents a fuzzy tuning system for realtime industrial PID controllers. The system is tested in an austempering process but can be applied in any industrial process. Besides, an analysis between the response of the process with a PID controller and the system of fuzzy auto-tuning for PID proposed was made.

Detection of no-model input/output combinations in MIMO systems subject to noise and unmeasured disturbances

A method is proposed to detect if there is no coupling between an input and an output in open-loop systems. The proposed technique is applicable to MIMO systems, i.e., the intent is to detect models in a transfer matrix. Traditional approaches to input/output (IO) selection are usually performed after the plant model is identified. The proposed approach is applied during the pre-identification stage and it is based on cross correlation and fuzzy logic analysis. A study case involving identification of a 7×6 simulated system is discussed, as well as an analysis of the influence of detecting no-model IO pairs in the identification process.

Detection of No-Model Input/Output Combinations in a Fluid Catalytic Cracking Unit

Abstract A method is proposed to detect if there is no coupling between an input and an output in systems operating in open-loop. The proposed technique is applicable to multiple input multiple output (MIMO) systems, i.e., the intent is to detect zeros in a transfer matrix. Traditional approaches to input/output (IO) selection are usually performed after the plant model is identified. The proposed approach is applied during the pre-identification stage and it is based on cross-correlation and fuzzy logic analysis. A study case involving identification of a 20 × 10 real system is discussed, as well as the advantages of detecting no-model IO pairs in the identification process.

Performance analysis of MPC based on structures subject to no-model input/output combinations

An analysis of the benefits over MPC performance obtained through the use of a method to detect no-model IO (input/output) combinations for open and closed-loop in multiple input multiple output (MIMO) systems is performed. Traditional approaches to IO selection are usually performed after the plant model is already characterized, which can lead to model-plant mismatch. The approach herein presented is applied during the pre-identification stage, in order to provide previous information to the following stages. A study case involving identification of a 2 × 2 MIMO system is discussed.

Model Predictive Control Relevant Identification

Model predictive control (MPC) is a multivariable feedback control technique used in a wide range of practical settings, such as industrial process control, stochastic control in economics, automotive and aerospace applications. As they are able to handle hard input and output constraints, a system can be controlled near its physical limits, which frequently results in performance superior to linear controllers (Maciejowski, 2002), specially for multivariable systems. At each sampling instant, predictive controllers solve an optimization problem to compute the control action over a finite time horizon. Then, the first of the control actions from that horizon is applied to the system. In the next sample time, this policy is repeated, with the time horizon shifted one sample forward. The optimization problem takes into account estimates of the system output, which are computed with the input-output data up to that instant, through a mathematical model. Hence, in MPC applications, a suitable model to generate accurate output predictions in a specific horizon is crucial, so that high performance closed-loop control is achieved. Actually, model development is considered to be, by far, the most expensive and time-consuming task in implementing a model predictive controller (Zhu & Butoyi, 2002).