Weighted input predictive controller

A discrete-time algorithm is presented which is based on a predictive control scheme in the form of dynamic matrix control. A set of control inputs are calculated and made available at each time instant, the actual input applied being a weighted summation of the inputs within the set. The algorithm is directly applicable in a self-tuning format and is therefore suitable for slowly time-varying systems in a noisy environment.

Adaptive general predictive controller for nonlinear systems

A nonlinear general predictive controller (NLGPC) is described which is based on the use of a Hammerstein model within a recursive control algorithm. A key contribution of the paper is the use of a novel, one-step simple root solving procedure for the Hammerstein model, this being a fundamental part of the overall tuning algorithm. A comparison is made between NLGPC and nonlinear deadbeat control (NLDBC) using the same one-step nonlinear components, in order to investigate NLGPC advantages and disadvantages.

Output envelope adjustment for an environmental fuzzy logic controller

A two-level fuzzy logic controller for use in air-conditioning systems is outlined in this paper. At the first level a simplified controller is produced from expert knowledge and envelope adjustment is introduced, while the second level provides a means for adapting this controller to different working spaces. The mechanism for adaption is easily implemented and can be used in real time. A series of simulations is presented to illustrate the proposed schema.

Quasi-decoupled fuzzy logic controller

The authors describe the design of a fuzzy logic controller for the control of a planar two-link manipulator. The plant is quasi-decoupled with respect to gravity. Complete decoupling is not achieved due to the nonoptimal nature of the expert rules. The performance of the fuzzy controller is compared to that of the critically damped computed torque controller. Results are presented complete with robustness tests.

Simplified PID self-tuning controller for robotic manipulators

The paper describes a self-tuning adaptive PID controller suitable for use in the control of robotic manipulators. The scheme employs a simple recursive estimator which reduces the computational effort to an acceptable level for many applications in robotics.

A robust controller design method for feedback substitution schemes using genetic algorithms

Controllers for feedback substitution schemes demonstrate a trade-off between noise power gain and normalized response time. Using as an example the design of a controller for a radiometric transduction process subjected to arbitrary noise power gain and robustness constraints, a Pareto-front of optimal controller solutions fulfilling a range of time-domain design objectives can be derived. In this work, we consider designs using a loop shaping design procedure (LSDP). The approach uses linear matrix inequalities to specify a range of objectives and a genetic algorithm (GA) to perform a multi-objective optimization for the controller weights (MOGA). A clonal selection algorithm is used to further provide a directed search of the GA towards the Pareto front. We demonstrate that with the proposed methodology, it is possible to design higher order controllers with superior performance in terms of response time, noise power gain and robustness.

Real-time application of a constrained predictive controller based on dynamic neural networks with feedback linearization

This paper describes an experimental application of constrained predictive control and feedback linearisation based on dynamic neural networks. It also verifies experimentally a method for handling input constraints, which are transformed by the feedback linearisation mappings. A performance comparison with a PID controller is also provided. The experimental system consists of a laboratory based single link manipulator arm, which is controlled in real time using MATLAB/SIMULINK together with data acquisition equipment.

A model-based PID controller for Hammerstein systems using B-spline neural networks

In this paper, a new model-based proportional–integral–derivative (PID) tuning and controller approach is introduced for Hammerstein systems that are identified on the basis of the observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a B-spline neural network. The control signal is composed of a PID controller, together with a correction term. Both the parameters in the PID controller and the correction term are optimized on the basis of minimizing the multistep ahead prediction errors. In order to update the control signal, the multistep ahead predictions of the Hammerstein system based on B-spline neural networks and the associated Jacobian matrix are calculated using the de Boor algorithms, including both the functional and derivative recursions. Numerical examples are utilized to demonstrate the efficacy of the proposed approaches.

B-spline neural networks based PID controller for Hammerstein systems

A new PID tuning and controller approach is introduced for Hammerstein systems based on input/output data. A B-spline neural network is used to model the nonlinear static function in the Hammerstein system. The control signal is composed of a PID controller together with a correction term. In order to update the control signal, the multistep ahead predictions of the Hammerstein system based on the B-spline neural networks and the associated Jacobians matrix are calculated using the De Boor algorithms including both the functional and derivative recursions. A numerical example is utilized to demonstrate the efficacy of the proposed approaches.

Antidepressant medications reduce subcortical-cortical resting-state functional connectivity in healthy volunteers

Studies have revealed abnormalities in resting-state functional connectivity in those with major depressive disorder specifically in areas such as the dorsal anterior cingulate, thalamus, amygdala, the pallidostriatum and subgenual cingulate. However, the effect of antidepressant medications on human brain function is less clear and the effect of these drugs on resting-state functional connectivity is unknown. Forty volunteers matched for age and gender with no previous psychiatric history received either citalopram (SSRI; selective serotonergic reuptake inhibitor), reboxetine (SNRI; selective noradrenergic reuptake inhibitor) or placebo for 7 days in a double-blind design. Using resting-state functional magnetic resonance imaging and seed based connectivity analysis we selected the right nucleus accumbens, the right amygdala, the subgenual cingulate and the dorsal medial prefrontal cortex as seed regions. Mood and subjective experience were also measured before and after drug administration using self-report scales. Despite no differences in mood across the three groups, we found reduced connectivity between the amygdala and the ventral medial prefrontal cortex in the citalopram group and the amygdala and the orbitofrontal cortex for the reboxetine group. We also found reduced striatal-orbitofrontal cortex connectivity in the reboxetine group. These data suggest that antidepressant medications can decrease resting-state functional connectivity independent of mood change and in areas known to mediate reward and emotional processing in the brain. We conclude that hypothesis-driven seed based analysis of resting-state fMRI supports the proposition that antidepressant medications might work by normalising the elevated resting-state functional connectivity seen in depressed patients.

A MATLAB GUI for learning controller design in the frequency domain

A MATLAB GUI is presented which is used to help students learn to design controllers in the frequency domain. It complements the author’s two previous GUIs for plotting and identification of systems in the frequency domain. It also incorporates the concept used in the “electronic calculator that makes students think” to assist learning. Positive student feedback affirms that the GUI has helped their understanding.