Knowledge-Based Control for Robot Arm
Aboubekeur Hamdi‐Cherif · InTech eBooks · 2011
The present research work reports the usability of knowledge-based control (KBC) as an alternative control method with specific concentration robot arm (RA). This novel control approach is based on the combination of inferences and calculations. It is dictated by the advent of microprocessor technology which has been one of the sources of inspiration for techniques spanning the whole spectrum of controllers design. KBC can contribute to build simple proportional integral and derivative (PID) control schemes (Astrom et al., 1992) to large classes of regulators such as self-tuning regulators and model-reference adaptive controllers, among others (Hanlei, 2010). Because knowledge base systems (KBSs) research has focused on implementing heuristic techniques, the corresponding knowledge-based controllers can justly be considered as the next logical step in control design and implementation (Handelman et al., 1990). The main characteristics of knowledge-based controllers is that they incorporate years-long human expertise under the form of machineunderstandable heuristic rules. In KBC, the knowledge elicited from human experts is codified and embodied within the KB in the form of IF-THEN rules. As a result, the KB technology takes into account the increase in system complexity. This sophistication is naturally encountered as efforts are made to stretch the limits of system performance and integrate more capabilities as a response to technological advances (Calangiu et al., 2010). In addition, the inherent ability of KBSs to support incremental expansion of capabilities and provide justification for recommendations or actions is offered by conventional programming techniques. Serious considerations are being given to increasing system reliability by predicting algorithm failure in RAs control and reconfiguring control laws in response to algorithm failure due to instability/chattering, or large RAs parameter variations. The knowledge-based control (KBC) benefits as applied to RA are to: Implement/incorporate heuristics within the RA control schemes. Diagnose or predict algorithm failure. Identify changes in RA parameters or structure. Recalculate control laws based upon knowledge of the current RA parameters. Select appropriate control laws based on the current RA responses. Execute supportive control logic which has been used for practical controllers in the past. Provide an explanation of the situation to the user as and when requested.