This paper presents a quantitative, predictive model of human self-images consistent with several personality temperament indicators including Lowry Colors, Enneagram, Merrill's C.A.P.S., as well as with the clinical personality disorders. We predict how the Myers-Briggs conflict-type behaviors stem from this model, and validate the ideas with a computer simulation that closely predicts the observed distribution between the Enneagram and Myers-Briggs conflict types in the human population. The theory directly results in the ability to design feedback systems with personalities. It also clearly points toward how to develop robots with human-like behaviors and chat-bots with human-like personalities.

Traditional robust control system design methodologies typically provide methods for dealing with well-understood, easily-modelled disturbances of similar magnitude. However, many proposed applications for robotics require interaction with an unstructured environment, which may even include people and other robots. Providing a more advanced toolkit than simple feedback laws, fuzzy logic offers advantages in such situations; however designers often use trial-and-error (and eventually experience) in constructing their fuzzy membership sets. Moreover, many interactive tasks easily accomplished by human beings remain beyond the ability of current robotics technology. We propose a more formal methodology at the high-level design phase, based on understandings of personality types. Specifically, similarities between human personalities and robot controls allow one to choose a personality for the control as the first stage of the design process. This might provide a powerful method of tuning when one only has intuitive understanding (and order-of-magnitude estimates) of the robot, its environment, people, and other robots. Our method allows combining this type of knowledge with intuitive understanding of human personalities when tackling a control design problem. To illustrate the methodology, a design that combines four distinct personalities in a fuzzy-PID control allows a simulated system to react more reasonably, and quite differently, to unmodeled disturbances that differ by an order of magnitude.

This work proposes a modification to the Cerebellar Model Articulation Controller (CMAC) for use in a direct adaptive control scheme. With the original CMAC, when an oscillation in the input occurs between two CMAC cells and across the origin, the weights in the adjacent cells drift in opposite directions - resulting in control signal chatter. When the chatter is severe it can cause a sudden increase in error referred to as bursting. Weight update modifications strong enough to prevent weight drift can severely limit performance. In the proposed method each basis function has a tail, remaining non-zero for the duration of the subsequent cells indexing. A small oscillation about the origin now occurs entirely within each basis function, so that each weight is much less likely to drift in one direction. The robust weight modification can then be made much weaker, resulting in significantly better performance. A simulation of a quadrotor helicopter subject to both a sinusoidal disturbance and an unknown payload verifies the stability and performance of the proposed method.

This paper proposes an adaptive control for a rigidlink, flexible-joint robot using the Cerebellar Model Articulation Controller (CMAC) and the backstepping method, which is a suitable method when joints are underdamped and exhibit a large amount of flexibility. A previously proposed robust weight update method, deemed the introspective method, is placed into a Lyapunov-stable framework. In the introspective method, each local CMAC cell measures the output error in its own domain and over the domain of several sequentially activated cells on the same CMAC array. The cell then votes on whether it appears its previous weight update has reduced this error or not. The sum of all votes from the activated cells determines whether weight updates continue. In order to ensure uniformly ultimately bounded signals, a robust CMAC operates in parallel using a conservative e-modification weight update. Simulations with a two link flexible-joint arm show significantly improved performance over e-modification and a model-based LQR control.

In a mixed liquor biological wastewater treatment process, the dissolved oxygen level is a very important factor. This paper proposes an adaptive neural network control strategy to maintain a set point in aerated bioreactors. The proposed method prevents weight drift and associated bursting, without sacrificing performance. The controller is tested on a simplified version of the benchmark simulation model number 1, with disturbances in influent. The proposed controller outperforms PI control.

In biped walking, swing-leg retraction can reduce the energy loss at heel-strike by reducing the foot speed relative to the ground at touch-down. However, it also takes extra effort to brake and then accelerate the swing leg in the rearward direction. This energetic trade-off of retraction is influenced by the mechanical coupling with the stance leg preemptive push-off, and changes with different step lengths and speeds. Previously it has not been clear under which circumstances a retracting hip torque has a net energetic benefit (if ever). Here, using a simple biped model probed with numerical and analytic methods, we show how the effectiveness of leg retraction is influenced by actuator efficiencies for positive and negative work. As the efficiency of negative work decreases, the energetic advantage of retracting hip torque is found (mainly) for longer steps, whereas at shorter steps the hip joint extends (not retracts) prior to heel-strike. For fast walking, however, a braking hip torque is still required at the end of swing phase to ensure heel-strike.

Swing leg retraction, the backward rotation of the swing leg prior to heel-strike, is known to have several advantages in legged locomotion. To achieve this motion, a hip torque is required at the end of the swing phase to brake the forward rotation of the leg and/or accelerate its backward motion. In walking, pre-emptive push-off of the stance leg also occurs at the end of the swing, so its relative timing with late-swing retracting torque influences gait energetics. To find the best relative timing between the stance leg's push-off force and the swing leg retraction torque, we calculate their work-based energetics in a simple bipedal model using impulsive approximations and with the aid of the so-called overlap parameter that quantifies the relative order and the percentage overlap of the push-off and retraction impulses. By minimizing the energetic cost of the gait, we found that it is energetically favorable to start with the push-off force, and postpone braking the leg swing until completely after the push-off (impulsive force/torque). The implication for the more realistic non-impulsive cases is to apply the retraction torque at the very end of the push-off before heel-strike. We show that the results are valid for many other bipedal models, for both periodic and aperiodic gaits, and regardless of the actuator efficiencies for positive and negative work.

This paper presents an adaptive haptic control for a one degree-of-freedom master-slave teleoperated device. The aim is to reduce excessive collision forces that occur when there are significant time delays in master-slave communication. The control design also allows the operator to move the slave in free space and in a soft medium. Previous approaches to haptic teleoperation typically design for either movement in a medium or constrained contact with a solid surface; then, it is up to the operator to avoid collisions or precisely anticipate collisions. The proposed control runs on the slave side inner loop, with no time delay, and tracks commanded forces from the outer loop. A Lyapunov-stable backstepping-with-tuning-functions design provides a way to ensure smooth forces are applied that guarantee stability in the presence of unmodeled environmental stiffness and viscosity. Experiments using a Phantom hand controller interacting with simulated environment show that collision forces are substantially reduced compared to two other control methods. In collision-free operation, the performance is comparable to other methods.

A new robust weight update method for use in direct neural-adaptive (or fuzzy-adaptive) control of oscillating systems can improve performance when compared to (e-modification. The proposed method is particularly applicable when using neural networks with local basis functions like the the Cerebellar Model Arithmetic Computer. The existence of ultimate bounds on the signals is established using a Lyapunov function. These ultimate bounds depend on the hounds of the nonlinear functions, whereas e-modification results in ultimate bounds that depend on a set of unknown ideal weights. Simulations demonstrate the new method performs very well in a situation where e-modification performs very poorly: when the input oscillates between two local basis functions.

A neural network used in a direct-adaptive control scheme can achieve trajectory tracking of a (highly) flexible joint robot holding an unknown payload without need for many learning repetitions. A modification of the Lyapunov stable nonlinear control method known as backstepping with tuning functions is derived to achieve this. Specifically, the introduction of appropriate weightings of the different tuning-function terms results in high performance. Also, a robust redesign of the tuning function method is presented to account for the uniform approximation (modeling) error of the neural network. This computationally burdensome method is made practical by taking advantage of the efficient structure of the CMAC neural network. Simulations with a (highly) flexible-joint robot show immediate compensation for a payload with performance nearly recovered after five seconds.

An artificial neural network is proposed for the precision control of flexible-joint robots. The training method uses backstepping in an online, direct neuroadaptive scheme in order to guarantee stability. The online weight updates include a learning term that improves performance while maintaining stability. Albus's cerebellar model arithmetic computer algorithm is modified to work for flexible robots by utilizing radial basis functions to deal with the elasticity. The resulting hybrid network is referred to as CMAC-RBF associative memory or CRAM network. Many of the properties of the CMAC for rigid robot control are kept by using CRAM for flexible-joint robots.