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Understanding the neurophysiological signals underlying voluntary motor control and decoding them for controlling limb prostheses is one of the major challenges in applied neuroscience and rehabilitation engineering. While pattern recognition of continuous myoelectric (EMG) signals is arguably the most investigated approach for hand prosthesis control, its underlying assumption is poorly supported, i.e., that repeated muscular contractions produce consistent patterns of steady-state EMGs. In fact, it still remains to be shown that pattern recognition-based controllers allow natural control over multiple grasps in hand prosthesis outside well-controlled laboratory settings. Here, we propose an approach that relies on decoding the intended grasp from forearm EMG recordings associated with the onset of muscle contraction as opposed to the steady-state signals. Eight unimpaired individuals and two hand amputees performed four grasping movements with a variety of arm postures while EMG recordings subsequently processed to mimic signals picked up by conventional myoelectric sensors were obtained from their forearms and residual limbs, respectively. Off-line data analyses demonstrated the feasibility of the approach also with respect to the limb position effect. The sampling frequency and length of the classified EMG window that off-line resulted in optimal performance were applied to a controller of a research prosthesis worn by one hand amputee and proved functional in real-time when operated under realistic working conditions.

In recent years, the main goal of the automative industry has been to reduce fuel consumption. Downsizing is a promising way to achieve this, which has shown success. Downsized, turbocharged engines suffer from slow transient torque response. This slow response is due to the slow dynamics of the turbocharger. This paper investigates the torque response of a spark ignited engine with variable geometry turbine (VGT) and variable valve timing. Optimal open-loop trajectories for the overlap and the VGT position for a fast transient response are found. This optimization is based on a 1-D simulation model. Based on this optimization, a generic feedback strategy for controlling the VGT is found. This strategy is implemented and evaluated on an engine and shows good performance., QC 20140131

Time and fuel optimal control for a diesel-electric powertrain in transient operation is studied using a four state, three controls non-linear mean value engine model. In the studied transients the engine starts at idle and stops when the generated energy fulfills the driving mission requirement. During the driving mission both the engine speed and output power are allowed to vary, but with a constraint on power. Two strategiesare then developed and evaluated. One where the driving mission is optimized with the generator power considered a free variable,and a second strategy where the accelerating phase of the transient is first optimized and then the optimal controls fora fixed generator power are used. The time optimal control is shown to be almost as fuel efficient as the fuel optimal controleven though the gain in time is large. The time optimal control also has the advantage of using constant power output, making itsimple and easily implementable, whilst the fuel optimal control is more complex and changes with the length of the driving mission.

Time and fuel optimal control for a diesel-electric powertrain in transient operation is studied using a four state, three controls non-linear mean value engine model. In the studied transients the engine starts at idle and stops when the generated energy fulfills the driving mission requirement. During the driving mission both the engine speed and output power are allowed to vary, but with a constraint on power. Two strategiesare then developed and evaluated. One where the driving mission is optimized with the generator power considered a free variable,and a second strategy where the accelerating phase of the transient is first optimized and then the optimal controls fora fixed generator power are used. The time optimal control is shown to be almost as fuel efficient as the fuel optimal controleven though the gain in time is large. The time optimal control also has the advantage of using constant power output, making itsimple and easily implementable, whilst the fuel optimal control is more complex and changes with the length of the driving mission.

Time and fuel optimal control for a diesel-electric powertrain in transient operation is studied using a four state, three controls non-linear mean value engine model. In the studied transients the engine starts at idle and stops when the generated energy fulfills the driving mission requirement. During the driving mission both the engine speed and output power are allowed to vary, but with a constraint on power. Two strategiesare then developed and evaluated. One where the driving mission is optimized with the generator power considered a free variable,and a second strategy where the accelerating phase of the transient is first optimized and then the optimal controls fora fixed generator power are used. The time optimal control is shown to be almost as fuel efficient as the fuel optimal controleven though the gain in time is large. The time optimal control also has the advantage of using constant power output, making itsimple and easily implementable, whilst the fuel optimal control is more complex and changes with the length of the driving mission.

Time and fuel optimal control for a diesel-electric powertrain in transient operation is studied using a four state, three controls non-linear mean value engine model. In the studied transients the engine starts at idle and stops when the generated energy fulfills the driving mission requirement. During the driving mission both the engine speed and output power are allowed to vary, but with a constraint on power. Two strategiesare then developed and evaluated. One where the driving mission is optimized with the generator power considered a free variable,and a second strategy where the accelerating phase of the transient is first optimized and then the optimal controls fora fixed generator power are used. The time optimal control is shown to be almost as fuel efficient as the fuel optimal controleven though the gain in time is large. The time optimal control also has the advantage of using constant power output, making itsimple and easily implementable, whilst the fuel optimal control is more complex and changes with the length of the driving mission.

Time and fuel optimal control for a diesel-electric powertrain in transient operation is studied using a four state, three controls non-linear mean value engine model. In the studied transients the engine starts at idle and stops when the generated energy fulfills the driving mission requirement. During the driving mission both the engine speed and output power are allowed to vary, but with a constraint on power. Two strategiesare then developed and evaluated. One where the driving mission is optimized with the generator power considered a free variable,and a second strategy where the accelerating phase of the transient is first optimized and then the optimal controls fora fixed generator power are used. The time optimal control is shown to be almost as fuel efficient as the fuel optimal controleven though the gain in time is large. The time optimal control also has the advantage of using constant power output, making itsimple and easily implementable, whilst the fuel optimal control is more complex and changes with the length of the driving mission.

This study applies a state feedback-based Closed-Loop Combustion Control (CLCC) using Fast Thermal Management (FTM) on a multi-cylinder Variable Compression Ratio (VCR) engine. At speeds above 1500 rpm is the FTM's bandwidth broadened by using the VCR feature of this engine, according to a predefined map, which is a function of load and engine speed. Below 1500 rpm is the PID-based CLCC using VCR applied instead of the FTM while slow cylinder balancing is effectuated by the FTM. Performance of the two CLCC controllers are evaluated during a European EC2000 drive cycle, while HC, CO and CO2 emissions are measured online by a Fast Response Infrared (FRI) emission equipment. A load and speed map calculated for a 1.6L Opel Astra is used to get reference values for the dynamometer speed and the load control. The drive cycle test is initiated from a hot engine and hence no cold start is included. Commercial RON/MON 92/82 gasoline, which corresponds to US regular, is utilized. The Linear Quadratic Gaussian (LQG) state feedback controller handles most tasks well, but has some difficulty with retarded combustion phasings, where the controller is outside of its design range. A mean fuel mileage of 6.8 L/100 km is achieved, which is an improvement of 13% compared to an equivalent SI simulation using steady state data from the same engine.

This paper compares several strategies for air-fuel ratio tran-sient control. The strategies are: A factory-standard look-up table based system (a SAAB Trionic 5), a feedback PI controller with and without feed-forward throttle correction, a linear feed-forward control algorithm, and two nonlinear feed- forward algorithms based on artificial neural networks. The control strategies have been implemented and evaluated in a SAAB 9000 car during a transient driving test, consisting of an acceleration in the second gear from an engine speed of 1500 rpm to 3000 rpm. The best strategies are found to be the neural network based ones, followed by the table based factory system. The two feedback PI controllers offer the poorest performance.