Controller Design

... Swarm intelligence is a member of artificial intelligence that study intelligence behaviour of groups rather than of individuals, such as social behaviour of insect like ants, bees, wasp, termites and fish. ... Figure 8: Pole-zero plot. REFERENCES Andries P. Engelbrecht. ...

The increased use of changeable characteristics in modern manufacturing and robotic systems and applications call for improved system control design that offers some degree of reconfigurability. The need for control reconfiguration of robotic systems arises due to ...

Electronic throttle is a DC motor driven valve that regulates air inflow into the engine's combustion system. The goal of the throttle control system is twofold: to achieve fast and accurate valve plate angle reference tracking, and to preserve outwearing of the throttle by constraining physical variables to their normal-operation domains. These high quality control demands are hard to accomplish since the plant is burdened with a strong nonlinear effects of friction and limp-home nonlinearity. For the purpose of controller synthesis first a discrete-time piecewise affine model of the throttle was developed and then a time-optimal model predictive control problem was solved. We propose a procedure to model friction in a discrete-time piecewise affine form that is suitable for simulation and control. The optimal controller action computation can be restated as a mixed-integer program. However, due to the small sampling time, solving such a program on-line (in a receding horizon fashion) is very prohibitive. In this paper we use recent theoretical results that enable off-line pre-computation of the optimal control law in the form of a look-up table. The technique employs invariant set computation and reachability analysis using multi-parametric programming. Finally, we compare experimental results of our approach with the performance of a tuned PID controller coupled with a feedforward compensation of the nonlinearities. We achieve more than two times faster transients while preserving the quality regarding the absence of an overshoot and static accuracy within the measurement resolution.

In this paper a new intelligent identification method of uncertainty bound utilizes an adaptive neurofuzzy inference system (ANFIS) in a feedback scheme is proposed. The proposed ANFIS feedback structure performs better in determining the uncertainty bounds with minimum number of iterations and error. In our proposed technique, the intelligent identified uncertainty weighting function is validated utilizing v-gap to ensure the stability of the designed H controlled system. Our proposed intelligent identification of uncertainty bound is demonstrated on a servo motion system. Simulation and experimental results show that the new ANFIS identifier is more reliable and highly efficient in estimating the best uncertainty weighting function for robust controller design.

Recently, a framework for controller design of sampled-data nonlinear systems via their approximate discrete-time models has been proposed in the literature. In this paper, we develop novel tools that can be used within this framework and that are useful for tracking problems. In particular, results for stability analysis of parameterized time-varying discrete-time cascaded systems are given. This class of models arises naturally when one uses an approximate discrete-time model to design a stabilizing or tracking controller for a sampled-data plant. While some of our results parallel their continuous-time counterparts, the stability properties that are considered, the conditions that are imposed, and the the proof techniques that are used, are tailored for approximate discrete-time systems and are technically different from those in the continuous-time context. A result on constructing strict Lyapunov functions from nonstrict ones that is of independent interest, is also presented. We illustrate the utility of our results in the case study of the tracking control of a mobile robot. This application is fairly illustrative of the technical differences and obstacles encountered in the analysis of discrete-time parameterized systems.

Underwater glider is an autonomous underwater vehicle that glides by controlling their buoyancy and attitude using internal actuators. By changing the vehicle's buoyancy intermittently, vertical motion can be achieved. Characteristics of glider motion include upward and downward movement in a saw tooth pattern, turning and gliding in a vertical spiral motion and gliding without using thrusters or propellers. This paper presents the modelling and identification on net buoyancy, depth and pitching angle of an underwater glider system. A ballast tank subsystem is considered appropriate for the identification process since it is the main parameter for the motion control. By selecting the ballast rate as the input, three aspects of the dynamics of a glider can be observed: buoyancy, depth of the glider and pitching angle. The MATLAB System Identification Toolbox TM is used to obtain a mathematical model of the glider ballast-buoyancy, ballast-depth and ballast-pitching angle conditioning system. The best three parametric estimation models are chosen, and the results of the comparison between simulated and estimated outputs are presented. The information obtained from the modelling and identification approaches are used for USM's Underwater Glider Prototype controller design. The information observed during this procedure are utilised for optimisation, stability, reliability and robustness analysis of the underwater glider.

Convex conditions, expressed as linear matrix inequalities (LMIs), for stability analysis and robust design of uncertain discrete-time systems with time-varying delay are presented in this paper. Delaydependent and delay-independent convex conditions are given. This paper is particularly devoted to the synthesis case where convex conditions are proposed to consider maximum allowed delay interval. It is also presented some relaxed LMIs that yield less conservative conditions at the expense of increasing the computational burden. Extensions to cope with decentralized control and output feedback control are discussed. Numerical examples, including real world motivated models, are presented to illustrate the effectiveness of the proposed approach. (V.J.S. Leite). constant delay. The problem of robust filtering for discrete-time uncertain systems with delayed states is considered in some papers. Delayed state systems with norm-bounded uncertainties are studied in and with polytopic uncertainties in . In the last case the delay is time-invariant. Recently, the problem of output feedback has attracted attention and can be cited as examples of on going research. In special, presents results for precisely known systems with time-varying delay including both static output feedback (SOF) and dynamic output feedback. The conditions, however, are presented as an interactive method that relax some matrix inequalities. In time-varying delay is assumed and a nonlinear algorithm is proposed to obtain a stabilizing controller. In [24] the results of [11] are extended, including polytopic uncertainties and constant Lyapunov-Krasovskii matrices. An interesting application can be found in the context of network control system: although most of the studies in the literature on this subject deal with continuous-time models, nowadays there are some approaches using discrete-time models with delayed states . See also for a robust adaptive sliding mode control scheme applied to discrete-time systems with time-varying delay in the state and subject to norm-bounded uncertainties. In the context of discrete-time-varying systems with time-varying delay in the state, see for convex approaches to the dynamic output feedback controller design problem.

The authors examine some issues in the transfer function modeling of a single flexible link. Using the assumed-modes approach, it is possible to find the transfer function between the torque input and the net tip deflection. It is shown that when the number of modes is increased for more accurate modeling, the relative degree of the transfer function becomes ill-defined.

In this paper we propose a general purpose hardware/software environment for hard real time simulation of dynamical systems in the context of control engineering. With this system, realistic analysis and tests can be carried out for controller algorithms characterized by time determinism with strict restrictions, multiple inputs and multiple outputs and based on robust control theory. A hard real time

Autonomous in-flight aerial refueling is an important capability for the future deployment of unmanned aerial vehicles, because they will likely be ferried in flight to overseas theaters of operation instead of being shipped unassembled in containers. A reliable sensor, capable of providing accurate relative position measurements of sufficient bandwidth, is key to such a capability. A vision-based sensor and navigation system is introduced that enables precise and reliable probe-and-drogue autonomous aerial refueling for non-micro-sized unmanned aerial vehicles. A performance robust controller is developed and integrated with the sensor system, and feasibility of the total system is demonstrated by simulated docking maneuvers with both a stationary drogue and a drogue subjected to light turbulence. An unmanned air vehicle model is used for controller design and simulation. Results indicate that the integrated sensor and controller enables precise aerial refueling, including consideration of realistic measurement errors and disturbances.

A new approach for robust fixed-order H∞ controller design by convex optimization is proposed. Linear timeinvariant single-input single-output systems represented by a finite set of complex values in the frequency domain are considered. It is shown that the H∞ robust performance condition can be approximated by a set of linear or convex constraints with respect to the parameters of a linearly parameterized controller in the Nyquist diagram. Multimodel and frequency-domain uncertainty can be directly considered in the proposed approach by increasing the number of constraints. The proposed method is compared with the standard H∞ control problem. It is shown by an example that for an unstable uncertain model, a PID controller can be designed with the proposed approach which gives better H∞ performance than a 7th order unstable controller obtained by the standard H∞ solution.

This paper presents the design of a sliding mode fuzzy logic-based controller for an active vehicle suspension system. The controller design is based on a Macpherson strut model and combines sliding mode techniques with fuzzy logic methodologies. This control ...

The design of logic controllers for event-driven systems continue to rely largely on intuitive methods rather than on formal techniques. This approach results in a control code that requires extensive verification, is hard to maintain and modify, and may even fail at times. Supervisory control theory (SCT) provides a formal approach to logic control synthesis. In order to demonstrate the usefulness of the supervisory control theory in manufacturing systems, an educational test-bed that simulates an automated car assembly line has been built using LEGO® blocks. Finite state machines (FSMs) are used for modeling operations of the assembly line, and for the specifications that accomplish the task of successfully completing the assembly repeatedly. Using the technique of SCT, we derive a supervisor that enforces the specifications while offering the maximum flexibility of assembly. Subsequently a controller is extracted from the maximally permissive supervisor for the purpose of implementing the control by selecting, when possible, at most one controllable event from among the ones allowed by the supervisor. Testing to check the correctness of the control code is reduced, since the controller is guaranteed to enforce the specifications.

Autonomous mobile robots have been used to carry out different tasks without continuous human guidance. To achieve the tasks, they must be able to navigate and avoid different kinds of obstacles that faced them. Navigation means that the robot can move through the environment to reach a destination. Obstacles avoidance considers a challenge which robot must overcome. In this work, the authors propose an efficient technique for obstacles avoidance through navigation of swarm mobile robot in an unstructured environment. All robots cooperate with each other to avoid obstacles. The robots detect the obstacles position around them and store their positions in shared memory. By accessing the shared memory, the other robots of the swarm can avoid the detected obstacles when they face them. To implement this idea, the Authors used a MATLAB® and V-REP® (Virtual Robot Experimentation Platform).

In this study a fuzzy logic controller for mobile robot navigation has been designed. The designed controller deals with the uncertainty and ambiguity of the information the system receives. The technique has been used on an experimental mobile robot which uses a set of seven ultrasonic sensors to perceive the environment. The designed fuzzy controller maps the input space (information coming from ultrasonic sensors) to a safe collision-avoidance trajectory (output space) in real time. This is accomplished by an inference process based on rules (a list of IF-THEN statements) taken from a knowledge base. The technique generates satisfactory direction and velocity maneuvers of the autonomous vehicle which are used by the robot to reach its goal safely. Simulation and experimental results show the method can be used satisfactorily by wheeled mobile robots moving on unknown static terrains.

Carbohydrate-restricted diets (CRDs) promote weight loss, reductions in plasma triacylglycerol (TAG) levels, and increases in highdensity lipoprotein cholesterol (HDL-C) levels but may cause undesirable low-density lipoprotein cholesterol (LDL-C) responses in some people. The objective of the present study was to determine the effect of adding soluble fiber to a CRD on plasma LDL-C and other traditionally measured markers of cardiovascular disease. Using a parallel-arm, double-blind, placebo-controlled design, 30 overweight and obese men (body mass index, 25-35 kg/m 2 ) were randomly assigned to supplement a CRD with soluble fiber (Konjac-mannan, 3g/d) (n = 15) or placebo (n = 15). Plasma lipids, anthropometrics, body composition, blood pressure, and nutrient intake were evaluated at baseline and at 6 and 12 weeks. Compliance was excellent as assessed by 7-day weighed dietary records and ketonuria. Both groups experienced decreases in ( P b .01) body weight, percent body fat, systolic blood pressure, waist circumference, and plasma glucose levels. After 12 weeks, HDL-C and TAG improved significantly in the fiber (10% and À34%) and placebo (14%, À43%) groups. LDL-C decreased by 17.6% ( P b .01) at week 6 and 14.1% ( P b .01) at week 12 in the fiber group. Conversely, LDL-C reductions were significant in the placebo group only after 12 weeks (À6.0%, P b .05). We conclude that although clearly effective at lowering LDL-C, adding soluble fiber to a CRD during active and significant weight loss provides no additional benefits to the diet alone. Furthermore, a CRD led to clinically important positive alterations in cardiovascular disease risk factors. D

In contrast to major depression, only few studies are available so far on the effects of repetitive transcranial magnetic stimulation (rTMS) in anxiety disorders. In order to summarise available data concerning the putative anxiolytic action of repetitive rTMS, a systematic literature review was carried out. Although interpretation of the results is difficult because of a large variety of used treatment protocols and the lack of a placebo-controlled design in the majority of studies, there is evidence for anxiolytic action of rTMS both from preclinical trials and studies in humans. Based on the idea of interhemispheric imbalance and/or deficits in cortico-limbic control as a model for human anxiety, inhibitory rTMS of the prefrontal cortex has been shown to exert beneficial effects in a number of studies in healthy subjects, patients with PTSD and panic disorder. However, to further elucidate the putative anxiolytic action of rTMS in patients with anxiety disorders future studies have to be conducted addressing in particular the limitations of the studies mentioned above.

In this paper, a structure-specified mixed H2/H∞ controller design using particle swarm optimization (PSO) is proposed for control balancing of Bicyrobo, which is an unstable system associated with many sources of uncertainties due to un-model dynamics, parameter variations, and external disturbances. The structure-specified mixed H2/H∞ control is a robust and optimal control technique. However, the design process normally comes up with a complex and non-convex optimization problem which is difficult to solve by the conventional optimization methods. PSO is a recently useful meta-heuristic search method used to solve multi-objective and non-convex optimization problems. In the method, PSO is used to search for parameters of a structure-specified controller which satisfies mixed H2/H∞ performance index. The simulation and experimental results show the robustness of the proposed controller in compared with the conventional proportional plus derivative (PD) controller, and the efficiency of the proposed algorithm in compared with the genetic algorithm (GA).

Functional verification mainly based on coverage driven constraint. The universal memory controller is aiming
to advance the performance of the prevailing memory controllers through a complete integration of the all memory in
addition of providing novel features. It mainly serves as one amongst the basic important feature that’s lagging in most of
memory controller that is power being consumed. The aim of the paper is to validate the design and to generate regression
test cases in the test-bench in order to obtain 100% functional coverage using System Verilog (SV) and a typical code
coverage

In Part I of this paper, we presented the fundamentals of the theory of IDA-PBC. In this part, we discuss the main new results and the practical applications of this control design approach as well as the current open problems and future directions.

Using a randomized double-blind placebo-controlled design we examined the effects of caffeine on working memory (WM) as a function of extraverted personality. Participants (N = 59) received 200 mg of caffeine and placebo in counterbalanced-order over two sessions prior to completing a 'N-Back' WM paradigm. Findings revealed that caffeine administration relative to the placebo condition resulted in heightened WM performance, but only for extraverted participants. We suggest based on previous theory and research that dopamine function (DA) may be the most plausible mechanism underlying this finding.

Underwater glider is an autonomous underwater vehicle that glides by controlling their buoyancy and attitude using internal actuators. By changing the vehicle's buoyancy intermittently, vertical motion can be achieved. Characteristics of glider motion include upward and downward movement in a saw tooth pattern, turning and gliding in a vertical spiral motion and gliding without using thrusters or propellers. This paper presents the modelling and identification on net buoyancy, depth and pitching angle of an underwater glider system. A ballast tank subsystem is considered appropriate for the identification process since it is the main parameter for the motion control. By selecting the ballast rate as the input, three aspects of the dynamics of a glider can be observed: buoyancy, depth of the glider and pitching angle. The MATLAB System Identification Toolbox TM is used to obtain a mathematical model of the glider ballast-buoyancy, ballast-depth and ballast-pitching angle conditioning system. The best three parametric estimation models are chosen, and the results of the comparison between simulated and estimated outputs are presented. The information obtained from the modelling and identification approaches are used for USM's Underwater Glider Prototype controller design. The information observed during this procedure are utilised for optimisation, stability, reliability and robustness analysis of the underwater glider.

This note improves the results of Zheng, Wang, and Lee (Automatica 38 (2002) 517) on the design of multivariable PID controllers via LMI approach. The idea of the improvement is to transform the problem of PID controller design to that of static output feedback (SOF) controller design for a system in descriptor form. The merits of the present result are: (1) The present condition removes the invertibility constraint on the controllers in and incorporates it to the matrix inequality to be solved. (2) Once the SOF matrix is obtained, the PID gains are ready in hand and no additional computation is needed. (3) The present approach can deal with the case of some system parameters subject to perturbations. ?

This paper addresses robust observer based H∞ control problem for Takagi-Sugeno fuzzy systems with timevarying norm bounded uncertainties. Sufficient relaxed conditions for synthesis of a fuzzy observer and a fuzzy controller for T-S fuzzy systems are derived in terms of a set of linear matrix inequalities (LMIs). In comparison with the existing techniques in literature, the proposed approach considerably simplifies the design procedure and gives in only one step the controller and the observer gains. The observer and controller designed are capable to reject the disturbance assumed known but norm bounded. In order to highlight the performance of the proposed control algorithm, numerical simulations are performed.

Modelling and boundary control for the Burgers equation is studied in this paper. Modelling has been done via processing of numerical observations through proper orthogonal decomposition (POD) with Galerkin projection. This results in a set of spatial basis functions together with a set of ordinary differential equations (ODEs) describing the temporal evolution. Since the dynamics described by the Burgers equation are non-linear, the corresponding reduced-order dynamics turn out to be non-linear. The presented analysis explains how the free boundary condition appears as a control input in the ODEs and how controller design can be accomplished. The issues of control system synthesis are discussed from the point of practicality, performance and robustness. The numerical results obtained are in good compliance with the theoretical claims. A comparison of various different approaches is presented.

In this paper a method is presented for deriving the explicit robust model-based optimal control law for constrained linear dynamic systems. The controller is derived o -line via parametric programming before any actual process implementation takes place. The proposed control scheme guarantees feasible operation in the presence of bounded input uncertainties by (i) explicitly incorporating in the controller design stage a set of feasibility constraints and (ii) minimizing the nominal performance, or the expectation of the performance over the uncertainty space. An extension of the method to problems involving target point tracking in the presence of persistent disturbances is also discussed. The general concept is illustrated with two examples. ?

This paper presents a model based controller design approach for plants that operate in several distinct operating regimes and make transitions between them. Often it is difficult to identify a single global model that describes plant behavior in all the regimes. In the present work we propose an identification method that builds linear models for the individual regimes, and then interpolates nonlinear models in between these local models to match plant dynamics during transitions. The identification technique is shown to work well with transition data which lack excitation. A model predictive controller based on the local and the transition models is then presented and applied to a reactor.

In this study, a Best First Search (BFS) Based Intelligent PID Tuning Method is proposed for a mobile robot path tracking problem. BFS is an informed search strategy in Artificial Intelligence (AI). It has been used in many areas of intelligent systems. Here, it is used to search values of both ultimate period and ultimate gain of Ziegler-Nichols (ZN) PID Tuning Method. So, using these ultimate period and gain, PID parameters can be c from tables of ZN method. The performance measure in this problem is described as minimizing total error during any specified trajectory tracking. Thus the proposed method does not depend on the system model. It only needs error information during the tracking control. So, this method can be used to tune PID parameters of any tracking controller for a system with unknown system dynamics. The proposed method is tested with simulations in MATLAB during trajectory tracking of a mobile robot. In addition, it is compared with classical PID and conventional PID tuning methods including Ziegler-Nichols method, Tyreus-Luyben method, and Shinskey method.

Keywords: Intelligent PID tuning, best first search, autonomous mobile robot, mobile robot path tracking problem, computed torque PID control, dynamic model, Ziegler-Nichols, Tyreus-Luyben, Shinskey.

This paper proposes the design and experimentation of digital control of soft-switched interleaved boost converter using FPGA for Telecommunication System. The switching devices in the proposed converter are turned on and off with Zero Voltage Switching (ZVS) and Zero Current Switching (ZCS) respectively. The circuit is operated in Continuous Conduction Mode (CCM) with various load ranges having duty cycle of more than 50%. The proposed converter is studied by developing the simulation module in MATLAB/SIMULINK. A PI controller is designed and implemented in FPGA to obtain a regulated DC output for line and load variations. Simulation and experimentation results are verified with a prototype development of the proposed converter. The results indicate that the converter performance is enhanced with closed loop control.

The piezoelectric actuator (PEA) is a well-known device for managing extremely small displacements in the range from 10 pm (1 pm = 10 12 m) to 100 m. When developing a control system for a piezo-actuated positioning mechanism, the actuator dynamics have to be taken into account. An electromechanical piezo model, based on physical principles, is presented in this paper. In this model, a first-order differential equation is adopted to describe the hysteresis effect, and a partial differential equation is used to describe the mechanical behavior. Since, in practice, a PEA is most often used as an actuator for positioning mechanisms, we considered the influence of such a mechanism on the overall mechanical behavior of PEA and positioning mechanism together. For a well-designed mechanism, the overall mechanical behavior practically equals that of a single mass-spring-damper system, of which the undamped eigenfrequency and the relative damping can be designed favorably. With respect to traditional voltage steering, charge steering has the advantage that no hysteresis is encountered between electrical input and elongation. Electrical steering configurations for both cases of steering are presented. Finally, for the case of charge steering, we derived the total model of a piezo-actuated positioning mechanism. This model is dominated by the mechanical model, which could be designed favorably. Therefore, this model gives a broad range of possibilities for model-based controller design.

In this paper, a new small signal model is theoretically established and validated by both simulation and experiments for a three-phase three-level boost-type AC/DC Vienna converter. The adopted identification methodology consists of three steps: first, the converter steady state and dynamic models are derived from the nonlinear state space equations, initially expressed in the dqo synchronous reference frame, by means of a local linearization around the nominal operating point, thus yielding twenty transfer functions relating the inputs to the outputs of the system. The second step is the numerical verification using both the averaged state space model built in SIMULINK, and the converter circuit simulation using SPS of Matlab. Finally, an experimental validation of the transfer functions on a 1.5 kW laboratory prototype, supported by the DS 1104 real-time controller board of dSPACE is carried out. The results are quantified and compared as magnitude and phase Bode graphs. It is confirmed that the proposed new small signal model represents rather accurately the real plant and is, therefore, reliable for further tasks such as dynamic analysis, numerical simulation and controller design purposes.

Two linear Artificial Neural Network (ANN)-based controllers for the control of a nonlinear system are presented. The optimization and pole placement control design strategies are used. For both strategies, linear ANNs are used to model a Continuous Stirred Tank Heater (CSTH) process. Third-order discrete-time models or Adaline ANNs are used whose parameters are updated at every sampling time to provide adaptability to the controllers. Simulation tests show that the linear ANN-based controllers can overcome strong nonlinear coupling effects, reject step disturbances, and provide adequate damping to the subsystem interactions. Both ANN-based control design strategies are shown to be interchangeable with each other for the control of the CSTH process.

This paper presents the design and development of a system controller for our USM Autonomous Underwater Vehicle (USM-AUV). The state space design approach was used because this design approach is most suitable for nonlinear system and multi-input multi-output (MIMO) system set-up. The full state feedback control scheme under the state space design topic has been selected for the controller design. In the initial stage, the controller system was designed using the linearised equation of motion. For the USM-AUV vehicle design, the thruster motor output force was selected as the input with the depth position as the output. Analysis of the designed control system performances has been done via simulation in Matlab control simulation software. A satisfactory performance has been achieved from the designed controller system. Optimal parameters values have also been acquired for optimal performance of our USM-AUV platform.

The proposed system for vehicle tracking and anti-theft detection locking system using Gsm & Gps deals with the design & development of a theft control system for safety and security in automobiles, which is being used to prevent the theft of a vehicle. This system makes use of an embedded system contributes major roles which based on Global System for Mobile communication (GSM) technology. The proposed system is placed in the vehicle and Mobile interfaced with engine protocols. When the vehicle is being stolen, the information about vehicle is received by vehicle owner with GSM technology for corrective action. The information is forwarded to central processing system which is in the form of the text message. The controller fetch the sms and sends it to the Global Positioning System (GPS) module and GPS module reads location in the form of latitude and longitude and sends information to the user's mobile. After getting location information about vehicle the mobile, car owner can control the ignition of the vehicle's engine. Controlling action generated by Car owner or system, it will stop or lock the engine immediately. The designed unit is very simple & low cost.

Presently there is vast interest in UAV (Unmanned Aerial Vehicle) development given its civilian and military applications. One of the main UAV components is the autopilot system. Its development invariably demands several lab simulations and field tests. Generally after an UAV crash few parts remain usable. Thus, before embedding an autopilot system, it has to be exhaustively lab tested. With educational and research purposes in autopilot control systems development area, a test platform is herein proposed. It employs Matlab/Simulink to run the autopilot controller under test, the flight simulator X-Plane with the aircraft to be commanded, a microcontroller to command model aircraft flight control surfaces and a servo to drive these control surfaces. These resources are interconnected through data communication buses. So that, the autopilot controller designed on Matlab/Simulink is tested by controlling an aircraft on X-Plane. The inputs given to the aircraft flight control surfaces in the X-Plane are simultaneously sent to the microcontroller which translates these commands into effective servo movement control. Through this platform, designed autopilot systems can be applied into models similar to real aircraft minimizing risks and increasing flexibility for design changes. As study case, tests results from a roll attitude autopilot system are presented.

We investigated the effectiveness of cognitive behavioural therapy (CBT) and a combined intervention of workplace-and individual-focused techniques among self-employed people on sick leave owing to work-related psychological complaints (such as anxiety, depression, and burnout). Both interventions were based on CBT; however, one was conducted by psychotherapists and involved extensive CBT, while the other was delivered by ''labour experts'' and consisted of a brief CBT-derived intervention combined with both individual-focused and workplace interventions. One hundred and twenty-two self-employed people who had applied for sickness benefit from an insurance company enrolled in a randomized controlled design. These individuals were assessed before the intervention and then at 4 months and 10 months after the onset of the intervention. The outcome was assessed based on duration of sick leave until partial and full return to work and on psychological complaints. Significant effects on partial and full return were found in favour of the combined intervention: partial return occurred 17 and 30 days earlier in this group than in the CBT group and the control group, respectively. For full return to work, the difference was approximately 200 days. A decrease in psychological complaints was present in each condition but we found no significant interaction effects. The results suggest that work resumption should be addressed earlier in individuals receiving CBT. This insight is of value for the (scarce) literature concerning interventions for individuals who are on sick leave owing to work-related psychological complaints.

Many children with Autism Spectrum Disorders (ASD) participate in social skills or Theory of Mind (ToM) treatments. However, few studies have shown evidence for their effectiveness. The current study used a randomized controlled design to test the effectiveness of a 16-week ToM treatment in 8-13 year old children with ASD and normal IQs (n = 40). The results showed that, compared to controls, the treated children with ASD improved in their conceptual ToM skills, but their elementary understanding, self reported empathic skills or parent reported social behaviour did not improve. Despite the effects on conceptual understanding, the current study does not indicate strong evidence for the effectiveness of a ToM treatment on the daily life mindreading skills.

We develop a finite-element analysis (FEA) model to describe transient and static operation of gas-exchange valves. Such valves, directly controlled by solenoids, are a promising method for enhancing automotive engine efficiency. The FEA model is validated by experimental testing on an actual automotive prototype valve. We show that a nonlinear lumped-parameter model that uses FEA results also closely matches experimental data. The lumped-parameter model is suitable for optimization of design and can be readily used for closed-loop simulation. We present a simplified lumped-parameter model to facilitate controller design. Finally, we compare a dynamic open-loop simulation with experimental results.

In today's fast paced world of increasing and innovative new technology, fuzzy logic is a practical mathematical addition to classic Boolean logic. We can see its applications in many fields of science and engineering. This paper gives a general overview with a large bibliography, on many such applications to target tracking, pattern recognition, robotics, power systems, controller design, chemical engineering, biomedical engineering, vehicular technology, economy management as well as decision making, communications and networking, electronic engineering, civil engineering, sensor technology, and industry